Recent Changes for "Statistics" - Davis Wikihttp://daviswiki.org/StatisticsRecent Changes of the page "Statistics" on Davis Wiki.en-us Statisticshttp://daviswiki.org/Statistics2013-03-15 20:37:30MaxwellKappes <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 98: </td> <td> Line 98: </td> </tr> <tr> <td> <span>-</span> Francisco Samaniego (Bayesian Analysis) </td> <td> <span>+</span> Francisco Samaniego (Bayesian Analysis<span>, 130A/B</span>) </td> </tr> <tr> <td> Line 101: </td> <td> Line 101: </td> </tr> <tr> <td> </td> <td> <span>+ * His STA 130A/B class is VERY theoretical</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2012-03-08 14:59:12brianhj <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 86: </td> <td> Line 86: </td> </tr> <tr> <td> </td> <td> <span>+ <br> + I took Regression Analysis with him. He's a genius, explains concepts clearly and exams are straight forward. Wish he taught more undergrad stats courses.<br> + </span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2010-10-14 10:58:00ChristopherAden <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 24: </td> <td> Line 24: </td> </tr> <tr> <td> <span>- Dr. Chris Drake, Undergraduate Adviser</span> </td> <td> <span>+ Dr. Jie Peng, Undergraduate Adviser (For Fall 2010, while Dr. Chris Drake is on sabbatical)</span> </td> </tr> <tr> <td> Line 26: </td> <td> Line 26: </td> </tr> <tr> <td> <span>-</span> Alejandra Garibay, Peer Advisor (200<span>9</span>-201<span>0</span>) </td> <td> <span>+</span> Alejandra Garibay, Peer Advisor (20<span>1</span>0-201<span>1</span>) </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2010-10-13 17:53:07ChristopherAdenRemoved teachers that no longer teach, added the teachers not previously listed. <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 30: </td> <td> Line 30: </td> </tr> <tr> <td> <span>-</span> Check the UC Davis [http://<span>regi</span>st<span>r</span>a<span>r</span>.ucdavis.edu/<span>UCDW</span>e<span>bCatalog</span>/<span>p</span>r<span>o</span>gra<span>ms/STA/STAf</span>a<span>c.h</span>t<span>ml G</span>e<span>neral</span> C<span>a</span>t<span>al</span>o<span>g</span>] </td> <td> <span>+</span> Check the UC Davis <span>Statistics department's </span>[http://<span>www.</span>sta<span>t</span>.ucdavis.edu/<span>cours</span>e<span>s</span>/<span>unde</span>rgra<span>du</span>ate C<span>ourse Descrip</span>t<span>i</span>o<span>ns</span>] </td> </tr> <tr> <td> Line 52: </td> <td> Line 52: </td> </tr> <tr> <td> <span>-</span> '''130AB - Brief Mathematical Stats and Probability Theory'''. Supposedly easier than STA 131ABC, but depending on the Prof., that is not always the case.<span>&nbsp;&nbsp;''e.g.'' Samaniego for 130B is harder than Roychowdury for 131B.</span> </td> <td> <span>+</span> '''130AB - Brief Mathematical Stats and Probability Theory'''. Supposedly easier than STA 131ABC, but depending on the Prof., that is not always the case. </td> </tr> <tr> <td> Line 73: </td> <td> Line 73: </td> </tr> <tr> <td> <span>- ==Professors==</span> </td> <td> <span>+ ==[http://www.stat.ucdavis.edu/people/faculty Professors]==</span> </td> </tr> <tr> <td> Line 77: </td> <td> Line 77: </td> </tr> <tr> <td> <span>-</span> * Since becoming department chair, he no longer teaches at the undergraduate level<span>&nbsp;that much</span>, but he still teaches STA231C, Graduate-level mathematical statistics. </td> <td> <span>+</span> * Since becoming department chair, he no longer teaches at the undergraduate level, but he still teaches STA231C, Graduate-level mathematical statistics. </td> </tr> <tr> <td> Line 88: </td> <td> Line 88: </td> </tr> <tr> <td> <span>- Robert Shumway (Analysis of Variance, Regression Analysis, Multivariate Statistics, Time Series Analysis)<br> - * Time series specialist</span> </td> <td> </td> </tr> <tr> <td> Line 93: </td> <td> Line 91: </td> </tr> <tr> <td> </td> <td> <span>+ * Responds very quickly to emails and class mailing list questions</span> </td> </tr> <tr> <td> Line 104: </td> <td> Line 103: </td> </tr> <tr> <td> -<span>&nbsp;&nbsp;*</span> [http://www.r-project.org R] - The somewhat hard to understand command-line statistical package. It does not have the neat GUI features as the commercial version (S-PLUS); yet the ever-growing community of R developers provide add-ons to facilitate unique routines which make this a cunning edge program for research. R is for people who have decent knowledge of programming and a constant supply of novel problems in data analysis.<br> -<span>&nbsp;&nbsp;*</span> [http://www.sas.com/ SAS] - Arguably the most used (and coveted by employers) program for people in the field of business and economics. The programming language is even less intuitive then R, but there are many resources and professionals that can help you in learning the language. It is also only used for data analysis, so one can't get as creative in its analysis as R, but it's very fast. Good to learn for people who are looking to work in companies which require data analysts. It also has the ugliest graphics engine ever.. (but again, it's fast).<br> <span>-</span> * [wiki:WikiPedia:SPSS] - [http://www.spss.com Statistics Package for the Social Sciences], another popular commercial statistics software<span>.</span> </td> <td> <span>+ * [wiki:WikiPedia:R_programming_language R Wikipedia] </span>- [http://www.r-project.org R<span>&nbsp;Homepage</span>] - The somewhat hard to understand command-line statistical package. It does not have the neat GUI features as the commercial version (S-PLUS); yet the ever-growing community of R developers provide add-ons to facilitate unique routines which make this a cunning edge program for research. R is for people who have decent knowledge of programming and a constant supply of novel problems in data analysis.<br> <span>+ * [wiki:WikiPedia:SAS_System SAS Wikipedia] </span>- [http://www.sas.com/ SAS<span>&nbsp;Homepage</span>] - Arguably the most used (and coveted by employers) program for people in the field of business and economics. The programming language is even less intuitive then R, but there are many resources and professionals that can help you in learning the language. It is also only used for data analysis, so one can't get as creative in its analysis as R, but it's very fast. Good to learn for people who are looking to work in companies which require data analysts. It also has the ugliest graphics engine ever.. (but again, it's fast).<br> <span>+</span> * [wiki:WikiPedia:SPSS<span>&nbsp;SPSS Wikipedia</span>] - [http://www.spss.com Statistics Package for the Social Sciences<span>&nbsp;Homepage</span>], another popular commercial statistics software<span>, but only in the social sciences. Basically unused outside of the departments of sociology, human development, psychology, etc.</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2010-10-11 19:27:05ChristopherAden <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p>No differences found!</div> Statisticshttp://daviswiki.org/Statistics2010-10-11 19:26:11ChristopherAden <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 12: </td> <td> Line 12: </td> </tr> <tr> <td> <span>-</span> ||[http://a<span>nson</span>.ucdavis.edu/]|| </td> <td> <span>+</span> ||[http://<span>www.st</span>a<span>t</span>.ucdavis.edu/]|| </td> </tr> <tr> <td> Line 16: </td> <td> Line 16: </td> </tr> <tr> <td> <span>-</span> At UC Davis statistics ["Majors" <span>M</span>ajors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a comput<span>er science</span> emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB. </td> <td> <span>+</span> At UC Davis statistics ["Majors" <span>m</span>ajors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a comput<span>ational</span> emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB. </td> </tr> <tr> <td> Line 25: </td> <td> Line 25: </td> </tr> <tr> <td> <span>-</span> <span>M</span>i<span>n</span> <span>Y</span>u, Undergraduate Program Coordinator<br> <span>-</span> Al<span>ici</span>a <span>L</span>i<span>n</span>, Peer Advisor (200<span>6</span>-200<span>7</span>) </td> <td> <span>+</span> <span>El</span>i<span>zabeth</span> <span>D</span>u<span>dley</span>, Undergraduate Program Coordinator<br> <span>+</span> Al<span>ejandr</span>a <span>Gar</span>i<span>bay</span>, Peer Advisor (200<span>9</span>-20<span>1</span>0) </td> </tr> <tr> <td> Line 35: </td> <td> Line 35: </td> </tr> <tr> <td> <span>-</span> '''32 - Statistical Analysis through Computers'''. Nearly equivalent to STA 13 in terms of statistical concepts covered; yet, there is more emphasis in the usage of computer package (R). For stat majors, this is a lot more useful course than STA 13. </td> <td> <span>+</span> '''32 - Statistical Analysis through Computers'''. Nearly equivalent to STA 13 in terms of statistical concepts covered; yet, there is more emphasis in the usage of computer package<span>s</span> (R). For stat majors, this is a lot more useful course than STA 13. </td> </tr> <tr> <td> Line 43: </td> <td> Line 43: </td> </tr> <tr> <td> <span>-</span> '''104 - Nonparametric Statistics'''. Many statistical analyses are based on common properties of known statistical models. Nonparametric statistics focus on parameterization via the data. These parameters are flexible and thus distribution free. This class teaches you how to apply the most common nonparametric statistical tests. Fit for more unusual problems. This potentially can be a difficult course, but usually the students are non-majors and that dilutes its rigorousness. </td> <td> <span>+</span> '''104 - Nonparametric Statistics'''. Many statistical analyses are based on common properties of known statistical models. Nonparametric statistics focus on parameterization via the data. These parameters are flexible and thus distribution free. This class teaches you how to apply the most common nonparametric statistical tests. Fit for more unusual problems. This potentially can be a difficult course, but usually the students are non-majors and that dilutes its rigorousness.<span>&nbsp;There used to be an upper division non parametrics class that was much more rigorous, but this is no longer the case.</span> </td> </tr> <tr> <td> Line 45: </td> <td> Line 45: </td> </tr> <tr> <td> <span>-</span> '''106 - Analysis of Variance'''. Teaches the mathematics of basic ANOVA. Considered one of the easiest classes that one can take in the major. Stats 106 and 108 have a reputation of being more or less plug and chug classes. </td> <td> <span>+</span> '''106 - Analysis of Variance'''. Teaches the mathematics of basic ANOVA. Considered one of the easiest classes that one can take in the major. Stats 106 and 108 have a reputation of being more or less plug and chug classes.<span>&nbsp;Topics include 1-way and 2-way ANOVA, complete randomized block designs, Analysis of Covariance, and nested ANOVA.</span> </td> </tr> <tr> <td> Line 47: </td> <td> Line 47: </td> </tr> <tr> <td> <span>-</span> '''108 - Linear Regression'''. Teaches the mathematics (and data analysis depending on Prof.) of simple linear regression. Unfortunately, it doesn't teach you much more than that. The statistics department desperately needs an undergraduate class for nonlinear regression. </td> <td> <span>+</span> '''108 - Linear Regression'''. Teaches the mathematics (and data analysis depending on Prof.) of simple linear regression. Unfortunately, it doesn't teach you much more than that. The statistics department desperately needs an undergraduate class for nonlinear regression.<span>&nbsp;Topics include simple linear regression, multiple linear regression, ANOVA approach to regression, model selection criteria (AIC, Adjusted R^2, Mallows' Cp), backwards elimination, and forward selection model building.</span> </td> </tr> <tr> <td> Line 55: </td> <td> Line 55: </td> </tr> <tr> <td> <span>-</span> * This class is often considered to be better than its sister class math 135A. The stats class focus<span>s</span>es more on applications/problem solving, where the math class does deep into the theory. Most people tend to agree that the stats version is also easier. -["Users/MattHh"] </td> <td> <span>+</span> * This class is often considered to be better than its sister class math 135A. The stats class focuses more on applications/problem solving, where the math class does deep into the theory. Most people tend to agree that the stats version is also easier. -["Users/MattHh"]<span><br> + * The amount of theory in this class largely depends upon the teacher. Some professors stick more to applications of probability, while others go deep into the mathematics behind probability, such as Roussas.</span> </td> </tr> <tr> <td> Line 59: </td> <td> Line 60: </td> </tr> <tr> <td> <span>-</span> '''135 - Multivariate Data Analysis'''. Most of the material in undergraduate statistics courses are taught in a univariate setting. Multiple variables arise commonly in real world situations. Hence, this class tends to deal with more realistic data sets. </td> <td> <span>+</span> '''135 - Multivariate Data Analysis'''. Most of the material in undergraduate statistics courses are taught in a univariate setting. Multiple variables arise commonly in real world situations. Hence, this class tends to deal with more realistic data sets.<span>&nbsp;This class does not give as much rigor as the similar STA232C course, but for an undergraduate course, you take what you can get.</span> </td> </tr> <tr> <td> Line 61: </td> <td> Line 62: </td> </tr> <tr> <td> <span>-</span> '''137 - Applied Time Series Analysis'''. You get to learn the basics of time series analysis (starting with AR and MA models). Get to use Shumway's time series software for Windows, ASTSA. </td> <td> <span>+</span> '''137 - Applied Time Series Analysis'''. You get to learn the basics of time series analysis (starting with AR and MA models). Get to use Shumway's time series software for Windows, ASTSA.<span>&nbsp;No other undergraduate course deals with time series, widely used in economics and biostatistics (longitudinal analysis).</span> </td> </tr> <tr> <td> Line 63: </td> <td> Line 64: </td> </tr> <tr> <td> <span>-</span> '''138 - Categorical Data Analysis'''. Learn the analysis of categorical data. Most of the times, you use partitioned count data. </td> <td> <span>+</span> '''138 - Categorical Data Analysis'''. Learn the analysis of categorical data. Most of the times, you use partitioned count data.<span>&nbsp;The class has been taught by Rahman Azari for the past several years, and is a required course for those pursuing the B.S. Statistics option, unless you can get signed off on taking a different 130-level class instead (135, 137).</span> </td> </tr> <tr> <td> Line 65: </td> <td> Line 66: </td> </tr> <tr> <td> <span>-</span> '''141 - Statistical Computing'''. Traditionally taught by Temple Lang. Class consists of multiple computing assignments (in R) with a final project at the end. There are no exams, but the assignments are time consuming enough to the point that you rather take an exam than finish an assignment. You get preached that math is really not that useful on the broad scale. </td> <td> <span>+</span> '''141 - Statistical Computing'''. Traditionally taught by Temple Lang. Class consists of multiple computing assignments (in R) with a final project at the end. There are no exams, but the assignments are time consuming enough to the point that you rather take an exam than finish an assignment. You get preached that math is really not that useful on the broad scale.<span>&nbsp;Assignments are incredibly open-ended and allow for a total exploration of the data, with the focus being on how to succinctly express large volumes of data and deal with human error in your data sets.</span> </td> </tr> <tr> <td> Line 76: </td> <td> Line 77: </td> </tr> <tr> <td> <span>- Katherine "Katie" S. Pollard (Categorical Data Analysis, Biostatistics)<br> - * Researcher at Genome Center<br> - * Her work was published in Time in October 2006.</span> </td> <td> <span>+ * Since becoming department chair, he no longer teaches at the undergraduate level that much, but he still teaches STA231C, Graduate-level mathematical statistics.</span> </td> </tr> <tr> <td> Line 80: </td> <td> Line 79: </td> </tr> <tr> <td> <span>-</span> * One of the oldest professors on campus whose courses are quite rigorous </td> <td> <span>+</span> * One of the oldest professors on campus whose courses are quite rigorous<span>. Likes to prove everything.</span> </td> </tr> <tr> <td> Line 83: </td> <td> Line 82: </td> </tr> <tr> <td> </td> <td> <span>+ * Very different teaching style in lecture versus office hours. If you find yourself having trouble in his lectures, give his office hours a chance. He sits down and really makes sure you understand every step.</span> </td> </tr> <tr> <td> Line 85: </td> <td> Line 85: </td> </tr> <tr> <td> </td> <td> <span>+ * Has a bowl of chocolates in his office for students to take whenever he's around</span> </td> </tr> <tr> <td> Line 89: </td> <td> Line 90: </td> </tr> <tr> <td> <span>-</span> Duncan Temple-Lang (Statistics<span>&nbsp;through computers</span>) </td> <td> <span>+</span> Duncan Temple-Lang (<span>Computational </span>Statistics) </td> </tr> <tr> <td> Line 102: </td> <td> Line 103: </td> </tr> <tr> <td> <span>- Jessica Utts ([http://en.wikipedia.org/wiki/Jessica_Utts Wikipedia], [http://anson.ucdavis.edu/~utts/ webpage]) works in parapsychology and "is on the current executive board of the International Remote Viewing Association". The IRVA is the last gasp of the formal academic ESP research from the last several decades. During the cold war a few Universities did serious studies to see if there was anything to the various psychic claims. Remote viewing (aka "astral travel") is one of the last still hovering on the fringes and still getting funding. Since a good deal of ESP research involves statistics, it would makes sense.<br> - <br> - </span> </td> <td> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2010-02-05 22:49:59JabberWokky <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 14: </td> <td> Line 14: </td> </tr> <tr> <td> <span>- </span>Statistics<span>&nbsp;at </span>UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the relatively small number of course requirements, students often choose to double major in statistics and their chosen field of application. </td> <td> <span>+ '''</span>Statistics<span>''' at ["</span>UC Davis<span>"]</span> is a small/intimate <span>["</span>major<span>s" major]</span> and is considered great preparation for careers in several fields ranging from business to science. Because of the relatively small number of course requirements, students often choose to double major in statistics and their chosen field of application. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2009-02-19 18:16:52JoePomidor <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 14: </td> <td> Line 14: </td> </tr> <tr> <td> <span>-</span> Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the rel<span>i</span>tively small number of course requirements, students often choose to double major in statistics and their chosen field of application. </td> <td> <span>+</span> Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the rel<span>a</span>tively small number of course requirements, students often choose to double major in statistics and their chosen field of application. </td> </tr> <tr> <td> Line 70: </td> <td> Line 70: </td> </tr> <tr> <td> <span>-</span> * ["CLIMB"] - is a program that focus<span>s</span>es on mathematical and statistical modeling in biology. If you are interested in going to grad school in biostatistics, applied statistics or mathematics, or biology, this research program is for you. </td> <td> <span>+</span> * ["CLIMB"] - is a program that focuses on mathematical and statistical modeling in biology. If you are interested in going to grad school in biostatistics, applied statistics or mathematics, or biology, this research program is for you. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-09-28 19:07:32JasonAllerlink fix <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 55: </td> <td> Line 55: </td> </tr> <tr> <td> <span>-</span> * This class is often considered to be better than its sister class math 135A. The stats class focusses more on applications/problem solving, where the math class does deep into the theory. Most people tend to agree that the stats version is also easier. -["MattHh"] </td> <td> <span>+</span> * This class is often considered to be better than its sister class math 135A. The stats class focusses more on applications/problem solving, where the math class does deep into the theory. Most people tend to agree that the stats version is also easier. -["<span>Users/</span>MattHh"] </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-09-26 10:09:50vladthedestroyer(quick edit) <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 50: </td> <td> Line 50: </td> </tr> <tr> <td> <span>-</span> An easier version of STA 131A. This is the old requirement for EE/CE majors, with the new requirement being EC<span>E</span> 161 starting Fall 08. CSE majors must take the more challenging STA 131A class. </td> <td> <span>+</span> An easier version of STA 131A. This is the old requirement for EE/CE majors, with the new requirement being E<span>E</span>C 161 starting Fall 08. CSE majors must take the more challenging STA 131A class. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-09-26 10:07:55vladthedestroyer(quick edit) <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 50: </td> <td> Line 50: </td> </tr> <tr> <td> <span>-</span> An easier version of STA 131A for <span>enginee</span>rs<span>.</span> <span>&nbsp;No</span>t<span>e</span> th<span>a</span>t <span>comput</span>e<span>r</span> s<span>c</span>i<span>ence e</span>ng<span>inee</span>rs must take the more challenging 131A class. </td> <td> <span>+</span> An easier version of STA 131A<span>. This is the old requirement</span> for <span>EE/CE majo</span>rs<span>,</span> <span>wi</span>t<span>h</span> th<span>e new requiremen</span>t <span>b</span>e<span>ing ECE 161</span> s<span>tart</span>ing<span>&nbsp;Fall 08. CSE majo</span>rs must take the more challenging <span>STA </span>131A class. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-09-09 21:27:09NickSchmalenberger(quick edit) <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 108: </td> <td> Line 108: </td> </tr> <tr> <td> <span>-</span> * [http://www.spss.com<span>/ SPSS] -</span> Statistics Package for the Social Sciences, another popular commercial statistics software. </td> <td> <span>+</span> *<span>&nbsp;[wiki:WikiPedia:SPSS] -</span> [http://www.spss.com Statistics Package for the Social Sciences<span>]</span>, another popular commercial statistics software. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-09-09 21:26:03NickSchmalenberger(quick edit) <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 108: </td> <td> Line 108: </td> </tr> <tr> <td> </td> <td> <span>+ * [http://www.spss.com/ SPSS] - Statistics Package for the Social Sciences, another popular commercial statistics software.</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-09-09 19:36:13JabberWokkyFinally. Years of listening to Coast to Coast AM pays off. <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 102: </td> <td> Line 102: </td> </tr> <tr> <td> </td> <td> <span>+ Jessica Utts ([http://en.wikipedia.org/wiki/Jessica_Utts Wikipedia], [http://anson.ucdavis.edu/~utts/ webpage]) works in parapsychology and "is on the current executive board of the International Remote Viewing Association". The IRVA is the last gasp of the formal academic ESP research from the last several decades. During the cold war a few Universities did serious studies to see if there was anything to the various psychic claims. Remote viewing (aka "astral travel") is one of the last still hovering on the fringes and still getting funding. Since a good deal of ESP research involves statistics, it would makes sense.<br> + </span> </td> </tr> <tr> <td> Line 109: </td> <td> Line 111: </td> </tr> <tr> <td> <span>-</span> One might also be interested in some Wiki related statistics: ["User Statistics"] [http://baxter.cernio.com/mrtg/ Server Stats] </td> <td> <span>+</span> One might also be interested in some Wiki related statistics: ["User Statistics"] <span>and </span>[http://baxter.cernio.com/mrtg/ Server Stats] </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-04-10 21:28:11BrandonBarretteStatistics is not on the 3rd floor!!! <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 4: </td> <td> Line 4: </td> </tr> <tr> <td> <span>-</span> ||<span>3rd</span> &amp; 4th floors of the ["Mathematical Sciences Building"], ["UC Davis"] || </td> <td> <span>+</span> ||<span>1st</span> &amp; 4th floors of the ["Mathematical Sciences Building"], ["UC Davis"] || </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2008-03-07 13:03:56JabberWokky <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 14: </td> <td> Line 14: </td> </tr> <tr> <td> <span>- </span> Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the relitively small number of course requirements, students often choose to double major in statistics and their chosen field of application.<br> <span>- </span> At UC Davis statistics ["Majors" Majors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a computer science emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB.<br> <span>- </span> As for graduate degrees one can either earn a M.S. or a Ph.D. The masters degree in statistics actually requires only a few core graduate courses. Since many graduate students were mathematics or other science majors as undergraduates, the first year or so of this degree may include several undergraduate statistics classes. For a Ph.D. in statistics or a Ph.D. in Biostatistics one must take a more rigorous set of courses and conduct extensive research.<br> <span>- </span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 130A/131A (Probability Theory), 130B/131B (Mathematical Statistics) and one upper division course with 130B or 131B as a prerequisite. If you are a math major intending on going to grad school in statistics or a bio major intending on going to grad school in biostatistics, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats are either bio majors who minor in statistics or math/stats majors who minor in ["Quantitative biology and bioinformatics"]. </td> <td> <span>+</span> Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the relitively small number of course requirements, students often choose to double major in statistics and their chosen field of application.<br> <span>+ <br> +</span> At UC Davis statistics ["Majors" Majors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a computer science emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB.<br> <span>+ <br> +</span> As for graduate degrees one can either earn a M.S. or a Ph.D. The masters degree in statistics actually requires only a few core graduate courses. Since many graduate students were mathematics or other science majors as undergraduates, the first year or so of this degree may include several undergraduate statistics classes. For a Ph.D. in statistics or a Ph.D. in Biostatistics one must take a more rigorous set of courses and conduct extensive research.<br> <span>+ <br> +</span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 130A/131A (Probability Theory), 130B/131B (Mathematical Statistics) and one upper division course with 130B or 131B as a prerequisite. If you are a math major intending on going to grad school in statistics or a bio major intending on going to grad school in biostatistics, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats are either bio majors who minor in statistics or math/stats majors who minor in ["Quantitative biology and bioinformatics"]. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-12-26 08:08:10MattHhremoved noteworthy alumni section (can re-add when some people are noteworthy) <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 14: </td> <td> Line 14: </td> </tr> <tr> <td> <span>-</span> Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the relitively small number of course requirements, students often choose to double major in statistics and their chosen field of application.<br> <span>-</span> At UC Davis statistics ["Majors" Majors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a computer science emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB.<br> <span>-</span> As for graduate degrees one can either earn a M.S. or a Ph.D. The masters degree in statistics actually requires only a few core graduate courses. Since many graduate students were mathematics or other science majors as undergraduates, the first year or so of this degree may include several undergraduate statistics classes. For a Ph.D. in statistics or a Ph.D. in Biostatistics one must take a more rigorous set of courses and conduct extensive research.<br> <span>-</span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 130A/131A (Probability Theory), 130B/131B (Mathematical Statistics) and one upper division course with 130B or 131B as a prerequisite. If you are a math major intending on going to grad school in statistics or a bio major intending on going to grad school in biostatistics, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats are either bio majors who minor in statistics or math/stats majors who minor in ["Quantitative biology and bioinformatics"]. </td> <td> <span>+ </span> Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the relitively small number of course requirements, students often choose to double major in statistics and their chosen field of application.<br> <span>+ </span> At UC Davis statistics ["Majors" Majors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a computer science emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB.<br> <span>+ </span> As for graduate degrees one can either earn a M.S. or a Ph.D. The masters degree in statistics actually requires only a few core graduate courses. Since many graduate students were mathematics or other science majors as undergraduates, the first year or so of this degree may include several undergraduate statistics classes. For a Ph.D. in statistics or a Ph.D. in Biostatistics one must take a more rigorous set of courses and conduct extensive research.<br> <span>+ </span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 130A/131A (Probability Theory), 130B/131B (Mathematical Statistics) and one upper division course with 130B or 131B as a prerequisite. If you are a math major intending on going to grad school in statistics or a bio major intending on going to grad school in biostatistics, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats are either bio majors who minor in statistics or math/stats majors who minor in ["Quantitative biology and bioinformatics"]. </td> </tr> <tr> <td> Line 99: </td> <td> Line 99: </td> </tr> <tr> <td> <span>- ==Noteworthy Alumni==<br> - Anne Hansen (Spring '06)<br> - * Statistics, B.A. and Sociology, B.A.<br> - * Ph.D. program in statistics at UC Riverside<br> - Christabel Moy (Fall '06)<br> - * Statistics, B.A. and Biological Science, B.S.<br> - * Currently a junior scientist at UC Davis working on cancer research<br> - * Applying for med school<br> - ["JonasMari" Jonas Mari] (Spring '07)<br> - * Statistics, B.S. and Economics, B.A. with a minor in English<br> - * Science Writer for ["The California Aggie"]<br> - * Statistical Programmer at [http://www.jpresearch.com/ JP Research] in Mountain View, CA<br> - ["MotokiWu" Motoki Wu] (Spring '07)<br> - * Statistics, B.S. and Applied Mathematics, B.S.<br> - * Featured on ["The California Aggie" The California Aggie's] [http://media.www.californiaaggie.com/media/storage/paper981/news/2007/02/07/ScienceTech/Behind.The.Small.Talk-2702914.shtml "Beyond the Small Talk"] for his work with the CLIMB program<br> - * Ph.D. Program in [http://depts.washington.edu/qerm/ Quantitative Ecology and Resource Management] at University of Washington in Seattle<br> - * former [http://climb.ucdavis.edu CLIMB] trainee, modeling vernal pools as metapopulations.<br> - <br> - Please add to this list!</span> </td> <td> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-12-18 08:33:05MattHhswitch to prose intro rather than list <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 14: </td> <td> Line 14: </td> </tr> <tr> <td> <span>- Statistics ["Majors" Majors] can earn the following degrees:<br> - *B.A. in Statistics<br> - *B.S. in Statistics<br> - *B.S. in Statistics Computer Science Option<br> - *M.S. in Statistics<br> - *Ph.D. in Statistics<br> - *Ph.D. in Statistics with Emphasis in Biostatistics</span> </td> <td> <span>+ Statistics at UC Davis is a small/intimate major and is considered great preparation for careers in several fields ranging from business to science. Because of the relitively small number of course requirements, students often choose to double major in statistics and their chosen field of application.<br> + At UC Davis statistics ["Majors" Majors] have a few different degree options. The department offers a B.S. in Statistics and a B.S. in Statistics with a computer science emphasis. The new B.S. in Applied Statistics is similar to the former B.A. in Statistics, which offers a more flexible set of course work, ideal for double majors in the social sciences. Here students may even switch stats 131AB for the less rigorous stats 130AB.<br> + As for graduate degrees one can either earn a M.S. or a Ph.D. The masters degree in statistics actually requires only a few core graduate courses. Since many graduate students were mathematics or other science majors as undergraduates, the first year or so of this degree may include several undergraduate statistics classes. For a Ph.D. in statistics or a Ph.D. in Biostatistics one must take a more rigorous set of courses and conduct extensive research.<br> + The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 130A/131A (Probability Theory), 130B/131B (Mathematical Statistics) and one upper division course with 130B or 131B as a prerequisite. If you are a math major intending on going to grad school in statistics or a bio major intending on going to grad school in biostatistics, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats are either bio majors who minor in statistics or math/stats majors who minor in ["Quantitative biology and bioinformatics"].</span> </td> </tr> <tr> <td> Line 22: </td> <td> Line 19: </td> </tr> <tr> <td> <span>- <br> - There are some proposed changes 2007-2008 school year:<br> - *B.S. in Applied Statistics (same requirements as B.A except 130A and 130B can replace 131A and 131B)<br> - *B.S. in Statistics<br> - *B.S. in Statistics, Computer Science Option<br> - *M.S. in Statistics<br> - *Ph.D. in Statistics<br> - *Ph.D. in Statistics with Emphasis in Biostatistics<br> - <br> - The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 130A (Probability Theory), 130B (Mathematical Statistics) and one upper division course with 130B or 131B as a prerequisite. If you are a math major intending on going to grad school in statistics or a bio major intending on going to grad school in biostatistics, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats minor in ["Quantitative biology and bioinformatics"].<br> - <br> - There are few statistics majors, although more have decided to add it since 2005.</span> </td> <td> </td> </tr> <tr> <td> Line 108: </td> <td> Line 93: </td> </tr> <tr> <td> <span>-</span> * [Distinguished Teaching Award] </td> <td> <span>+</span> * [<span>"</span>Distinguished Teaching Award<span>"</span>] </td> </tr> <tr> <td> Line 130: </td> <td> Line 115: </td> </tr> <tr> <td> <span>-</span> * <span>Currently hard at w</span>or<span>k at</span> [http://climb.ucdavis.edu CLIMB], modeling vernal pools as metapopulations. </td> <td> <span>+</span> * <span>f</span>or<span>mer</span> [http://climb.ucdavis.edu CLIMB]<span>&nbsp;trainee</span>, modeling vernal pools as metapopulations. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-10 21:11:32MattHhmade the formating match other majors pages, added comment to 131a <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 67: </td> <td> Line 67: </td> </tr> <tr> <td> </td> <td> <span>+ * This class is often considered to be better than its sister class math 135A. The stats class focusses more on applications/problem solving, where the math class does deep into the theory. Most people tend to agree that the stats version is also easier. -["MattHh"]</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-10 21:05:13MattHh <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 40: </td> <td> Line 40: </td> </tr> <tr> <td> <span>-</span> =<span>=</span>Courses=<span>=</span> </td> <td> <span>+</span> =<span>&nbsp;</span>Courses<span>&nbsp;</span>=<span><br> + </span> </td> </tr> <tr> <td> Line 42: </td> <td> Line 43: </td> </tr> <tr> <td> <span>- ====STA 13 (Elementary Statistics)====<br> - Learn the basics of statistics including but not limited to: probability distributions, hypothesis testing, confidence intervals, combinatorics, simple linear regression, one-way/two-way ANOVA. Possibly the easiest class in mankind. Most people can learn this material on the job, but it might be good to take it anyways since employers look for it for any discipline.</span> </td> <td> <span>+ == Lower Division ==</span> </td> </tr> <tr> <td> Line 45: </td> <td> Line 45: </td> </tr> <tr> <td> <span>- ====STA 32 (Statistical Analysis through Computers)====<br> - Nearly equivalent to STA 13 in terms of statistical concepts covered; yet, there is more emphasis in the usage of computer package (R?). For stat majors, this is a lot more useful course than STA 13.</span> </td> <td> <span>+ '''13 - Elementary Statistics'''. Learn the basics of statistics including but not limited to: probability distributions, hypothesis testing, confidence intervals, combinatorics, simple linear regression, one-way/two-way ANOVA. Possibly the easiest class in mankind. Most people can learn this material on the job, but it might be good to take it anyways since employers look for it for any discipline.</span> </td> </tr> <tr> <td> Line 48: </td> <td> Line 47: </td> </tr> <tr> <td> <span>- ====STA 100 (Applied Statistics for Biological Sciences)====<br> - Nearly equivalent to STA 13 in terms of statistical concepts covered. Emphasis in biological applications. Basically the same course as STA 13 and 32, but it is considered an upper division course.</span> </td> <td> <span>+ '''32 - Statistical Analysis through Computers'''. Nearly equivalent to STA 13 in terms of statistical concepts covered; yet, there is more emphasis in the usage of computer package (R). For stat majors, this is a lot more useful course than STA 13.</span> </td> </tr> <tr> <td> Line 51: </td> <td> Line 49: </td> </tr> <tr> <td> <span>- ====STA 103 (Applied Statistics for Business and Economics)====<br> - Goes a little deeper than STA 13, applied problems in business and economics.</span> </td> <td> <span>+ == Upper Division ==</span> </td> </tr> <tr> <td> Line 54: </td> <td> Line 51: </td> </tr> <tr> <td> <span>- ====STA 104 (Nonparametric Statistics)====<br> - Many statistical analyses are based on common properties of known statistical models. Nonparametric statistics focus on parameterization via the data. These parameters are flexible and thus distribution free. This class teaches you how to apply the most common nonparametric statistical tests. Fit for more unusual problems. This potentially can be a difficult course, but usually the students are non-majors and that dilutes its rigorousness.</span> </td> <td> <span>+ '''100 - Applied Statistics for Biological Sciences'''. Nearly equivalent to STA 13 in terms of statistical concepts covered. The emphasis is on biological applications. Basically the same course as STA 13 and 32, but it is considered an upper division course.</span> </td> </tr> <tr> <td> Line 57: </td> <td> Line 53: </td> </tr> <tr> <td> <span>- ====STA 106 (Analysis of Variance)====<br> - Teaches the mathematics of basic ANOVA. Considered one of the easiest classes that one can take in the major.</span> </td> <td> <span>+ '''103 - Applied Statistics for Business and Economics'''. Goes a little deeper than STA 13, applied problems in business and economics.</span> </td> </tr> <tr> <td> Line 60: </td> <td> Line 55: </td> </tr> <tr> <td> <span>- ====STA 108 (Linear Regression)====<br> - Teaches the mathematics (and data analysis depending on Prof.) of simple linear regression. Unfortunately, it doesn't teach you much more than that. The statistics department desperately needs an undergraduate class for nonlinear regression.</span> </td> <td> <span>+ '''104 - Nonparametric Statistics'''. Many statistical analyses are based on common properties of known statistical models. Nonparametric statistics focus on parameterization via the data. These parameters are flexible and thus distribution free. This class teaches you how to apply the most common nonparametric statistical tests. Fit for more unusual problems. This potentially can be a difficult course, but usually the students are non-majors and that dilutes its rigorousness.</span> </td> </tr> <tr> <td> Line 63: </td> <td> Line 57: </td> </tr> <tr> <td> <span>- ====STA 120 (Probability and Random Variables for Engineers)====<br> - STA 131A for engineers.</span> </td> <td> <span>+ '''106 - Analysis of Variance'''. Teaches the mathematics of basic ANOVA. Considered one of the easiest classes that one can take in the major. Stats 106 and 108 have a reputation of being more or less plug and chug classes.</span> </td> </tr> <tr> <td> Line 66: </td> <td> Line 59: </td> </tr> <tr> <td> <span>- ====STA 130AB (Brief Math. Stats and Prob. Theory)====<br> - Supposedly easier than STA 131ABC, but depending on the Prof., that is not the case. ''e.g.'' Samaniego for 130B is harder than Roychowdury for 131B.</span> </td> <td> <span>+ '''108 - Linear Regression'''. Teaches the mathematics (and data analysis depending on Prof.) of simple linear regression. Unfortunately, it doesn't teach you much more than that. The statistics department desperately needs an undergraduate class for nonlinear regression.</span> </td> </tr> <tr> <td> Line 69: </td> <td> Line 61: </td> </tr> <tr> <td> <span>- ====STA 131A (Probability Theory)====<br> - Intro to probability theory. Learn about continuous and discrete probability distributions, CLM, moments, expected values, etc. Possibly the most important course in the stats major. Everything else (like hypo. testing) follows from the base knowledge of probabilities.</span> </td> <td> <span>+ '''120 - Probability and Random Variables for Engineers'''<br> + An easier version of STA 131A for engineers. Note that computer science engineers must take the more challenging 131A class.</span> </td> </tr> <tr> <td> Line 72: </td> <td> Line 64: </td> </tr> <tr> <td> <span>- ====STA 131BC (Mathematical Statistics)====<br> - You get taught the mathematics behind estimation, hypo. testing, simple linear regression, ANOVA, convergence and nonparametric statistics. The mathematical rigor not withstanding, the subjects covered here is quite boring (IMO).</span> </td> <td> <span>+ '''130AB - Brief Mathematical Stats and Probability Theory'''. Supposedly easier than STA 131ABC, but depending on the Prof., that is not always the case. ''e.g.'' Samaniego for 130B is harder than Roychowdury for 131B.</span> </td> </tr> <tr> <td> Line 75: </td> <td> Line 66: </td> </tr> <tr> <td> <span>- ====STA 135 (Multivariate Data Analysis)====<br> - Most of the material in undergraduate statistics courses are taught in a univariate setting. Multiple variables arise commonly in real situations. Thus, this class deals with real data sets.</span> </td> <td> <span>+ '''131A - Probability Theory'''. Intro to probability theory. Learn about continuous and discrete probability distributions, CLM, moments, expected values, etc. Possibly the most important course in the stats major. Everything else (like hypo. testing) follows from the base knowledge of probabilities.</span> </td> </tr> <tr> <td> Line 78: </td> <td> Line 68: </td> </tr> <tr> <td> <span>- ====STA 137 (Applied Time Series Analysis)====<br> - You get to learn the basics of time series analysis (starting with AR and MA models). Get to use Shumway's time series software for Windows, ASTSA.</span> </td> <td> <span>+ '''131BC - Mathematical Statistics'''. You get taught the mathematics behind estimation, hypo. testing, simple linear regression, ANOVA, convergence and nonparametric statistics. The mathematical rigor not withstanding, the subjects covered here are quite boring (IMO).</span> </td> </tr> <tr> <td> Line 81: </td> <td> Line 70: </td> </tr> <tr> <td> <span>- ====STA 138 (Categorical Data Analysis)====<br> - Learn the analysis of categorical data. Most of the times, you use partitioned count data.</span> </td> <td> <span>+ '''135 - Multivariate Data Analysis'''. Most of the material in undergraduate statistics courses are taught in a univariate setting. Multiple variables arise commonly in real world situations. Hence, this class tends to deal with more realistic data sets.</span> </td> </tr> <tr> <td> Line 84: </td> <td> Line 72: </td> </tr> <tr> <td> <span>- ====STA 141 (Statistical Computing)====<br> - Traditionally taught by Temple Lang. Class consists of multiple computing assignments (in R) with a final project at the end. There are no exams, but the assignments are time consuming enough to the point that you rather take an exam than finish an assignment. You get preached that math is really not that useful on the broad scale.</span> </td> <td> <span>+ '''137 - Applied Time Series Analysis'''. You get to learn the basics of time series analysis (starting with AR and MA models). Get to use Shumway's time series software for Windows, ASTSA.</span> </td> </tr> <tr> <td> Line 87: </td> <td> Line 74: </td> </tr> <tr> <td> - <span>====</span>ST<span>A </span>145 <span>(</span>Bayesian Statistics<span>)====<br> -</span> Bayesian statistics is a completely different way of doing statistics. Applications in the real world has increased in recent days thanks to the increase in computing power. Used to be taught by Wes Johnson who emphasized its application using WinBUGS or JAGS. Johnson is at UCI, so Samaniego takes over and he emphasizes theory, which is cryptic at best. </td> <td> <span>+ '''138 </span>- <span>Categorical Data Analysis'''. Learn the analysis of categorical data. Most of the times, you use partitioned count data.<br> + <br> + '''141 - </span>S<span>tatistical Computing'''. </span>T<span>raditionally taught by Temple Lang. Class consists of multiple computing assignments (in R) with a final project at the end. There are no exams, but the assignments are time consuming enough to the point that you rather take an exam than finish an assignment. You get preached that math is really not that useful on the broad scale.<br> + <br> + '''</span>145 <span>- </span>Bayesian Statistics<span>'''. </span> Bayesian statistics is a completely different way of doing statistics. Applications in the real world has increased in recent days thanks to the increase in computing power. Used to be taught by Wes Johnson who emphasized its application using WinBUGS or JAGS. Johnson is at UCI, so Samaniego takes over and he emphasizes theory, which is cryptic at best. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-10 09:41:05MotokiWuAdded STA 138 <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 81: </td> <td> Line 81: </td> </tr> <tr> <td> </td> <td> <span>+ ====STA 138 (Categorical Data Analysis)====<br> + Learn the analysis of categorical data. Most of the times, you use partitioned count data.<br> + </span> </td> </tr> <tr> <td> Line 141: </td> <td> Line 144: </td> </tr> <tr> <td> <span>-</span> * [http://www.r-project.org R] - The somewhat hard to understand command-line statistical package. It does not have the neat <span>graphical</span> features as the commercial version (S-PLUS); yet the ever-growing community of R developers provide add-ons to facilitate unique routines which make this a cunning edge program for research. R is for people who have decent knowledge of programming and a constant supply of novel problems in data analysis. </td> <td> <span>+</span> * [http://www.r-project.org R] - The somewhat hard to understand command-line statistical package. It does not have the neat <span>GUI</span> features as the commercial version (S-PLUS); yet the ever-growing community of R developers provide add-ons to facilitate unique routines which make this a cunning edge program for research. R is for people who have decent knowledge of programming and a constant supply of novel problems in data analysis. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-04 13:50:16MotokiWuadded elective stats course info <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 75: </td> <td> Line 75: </td> </tr> <tr> <td> </td> <td> <span>+ ====STA 135 (Multivariate Data Analysis)====<br> + Most of the material in undergraduate statistics courses are taught in a univariate setting. Multiple variables arise commonly in real situations. Thus, this class deals with real data sets.<br> + <br> + ====STA 137 (Applied Time Series Analysis)====<br> + You get to learn the basics of time series analysis (starting with AR and MA models). Get to use Shumway's time series software for Windows, ASTSA.<br> + <br> + ====STA 141 (Statistical Computing)====<br> + Traditionally taught by Temple Lang. Class consists of multiple computing assignments (in R) with a final project at the end. There are no exams, but the assignments are time consuming enough to the point that you rather take an exam than finish an assignment. You get preached that math is really not that useful on the broad scale.<br> + <br> + ====STA 145 (Bayesian Statistics)====<br> + Bayesian statistics is a completely different way of doing statistics. Applications in the real world has increased in recent days thanks to the increase in computing power. Used to be taught by Wes Johnson who emphasized its application using WinBUGS or JAGS. Johnson is at UCI, so Samaniego takes over and he emphasizes theory, which is cryptic at best.<br> + </span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-04 11:29:17MotokiWu <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 6: </td> <td> Line 6: </td> </tr> <tr> <td> <span>- ||(Please fill in hours)||</span> </td> <td> <span>+ ||9-5 (closed 12-1 for lunch)||</span> </td> </tr> <tr> <td> Line 42: </td> <td> Line 42: </td> </tr> <tr> <td> </td> <td> <span>+ ====STA 13 (Elementary Statistics)====<br> + Learn the basics of statistics including but not limited to: probability distributions, hypothesis testing, confidence intervals, combinatorics, simple linear regression, one-way/two-way ANOVA. Possibly the easiest class in mankind. Most people can learn this material on the job, but it might be good to take it anyways since employers look for it for any discipline.<br> + <br> + ====STA 32 (Statistical Analysis through Computers)====<br> + Nearly equivalent to STA 13 in terms of statistical concepts covered; yet, there is more emphasis in the usage of computer package (R?). For stat majors, this is a lot more useful course than STA 13.<br> + <br> + ====STA 100 (Applied Statistics for Biological Sciences)====<br> + Nearly equivalent to STA 13 in terms of statistical concepts covered. Emphasis in biological applications. Basically the same course as STA 13 and 32, but it is considered an upper division course.<br> + <br> + ====STA 103 (Applied Statistics for Business and Economics)====<br> + Goes a little deeper than STA 13, applied problems in business and economics.<br> + <br> + ====STA 104 (Nonparametric Statistics)====<br> + Many statistical analyses are based on common properties of known statistical models. Nonparametric statistics focus on parameterization via the data. These parameters are flexible and thus distribution free. This class teaches you how to apply the most common nonparametric statistical tests. Fit for more unusual problems. This potentially can be a difficult course, but usually the students are non-majors and that dilutes its rigorousness.<br> + <br> + ====STA 106 (Analysis of Variance)====<br> + Teaches the mathematics of basic ANOVA. Considered one of the easiest classes that one can take in the major.<br> + <br> + ====STA 108 (Linear Regression)====<br> + Teaches the mathematics (and data analysis depending on Prof.) of simple linear regression. Unfortunately, it doesn't teach you much more than that. The statistics department desperately needs an undergraduate class for nonlinear regression.<br> + <br> + ====STA 120 (Probability and Random Variables for Engineers)====<br> + STA 131A for engineers.<br> + <br> + ====STA 130AB (Brief Math. Stats and Prob. Theory)====<br> + Supposedly easier than STA 131ABC, but depending on the Prof., that is not the case. ''e.g.'' Samaniego for 130B is harder than Roychowdury for 131B.<br> + <br> + ====STA 131A (Probability Theory)====<br> + Intro to probability theory. Learn about continuous and discrete probability distributions, CLM, moments, expected values, etc. Possibly the most important course in the stats major. Everything else (like hypo. testing) follows from the base knowledge of probabilities.<br> + <br> + ====STA 131BC (Mathematical Statistics)====<br> + You get taught the mathematics behind estimation, hypo. testing, simple linear regression, ANOVA, convergence and nonparametric statistics. The mathematical rigor not withstanding, the subjects covered here is quite boring (IMO).</span> </td> </tr> <tr> <td> Line 46: </td> <td> Line 78: </td> </tr> <tr> <td> <span>-</span> ==<span>Usual </span>Professors== </td> <td> <span>+</span> ==Professors== </td> </tr> <tr> <td> Line 65: </td> <td> Line 97: </td> </tr> <tr> <td> </td> <td> <span>+ * Talks fast, but really easy to get along with.</span> </td> </tr> <tr> <td> Line 68: </td> <td> Line 101: </td> </tr> <tr> <td> </td> <td> <span>+ Francisco Samaniego (Bayesian Analysis)<br> + * [Distinguished Teaching Award]<br> + * His STA 145 is hard (which is fine), but not useful in terms of applying the knowledge in the future.<br> + Jane-Ling Wang (Longitudinal Data Analysis, Survival Analysis)<br> + * Somewhat methodical, but good teacher and person nonetheless.<br> + * Good to take STA 131A from her.</span> </td> </tr> <tr> <td> Line 81: </td> <td> Line 120: </td> </tr> <tr> <td> <span>-</span> Motoki Wu (Spring '07) </td> <td> <span>+ ["MotokiWu"</span> Motoki Wu<span>]</span> (Spring '07) </td> </tr> <tr> <td> Line 84: </td> <td> Line 123: </td> </tr> <tr> <td> <span>-</span> * Ph.D. Program in Quantitative Ecology and Resource Management at University of Washington in Seattle </td> <td> <span>+</span> * Ph.D. Program in <span>[http://depts.washington.edu/qerm/ </span>Quantitative Ecology and Resource Management<span>]</span> at University of Washington in Seattle<span><br> + * Currently hard at work at [http://climb.ucdavis.edu CLIMB], modeling vernal pools as metapopulations.</span> </td> </tr> <tr> <td> Line 87: </td> <td> Line 127: </td> </tr> <tr> <td> </td> <td> <span>+ <br> + ==Useful Statistical Packages==<br> + * [http://www.r-project.org R] - The somewhat hard to understand command-line statistical package. It does not have the neat graphical features as the commercial version (S-PLUS); yet the ever-growing community of R developers provide add-ons to facilitate unique routines which make this a cunning edge program for research. R is for people who have decent knowledge of programming and a constant supply of novel problems in data analysis.<br> + * [http://www.sas.com/ SAS] - Arguably the most used (and coveted by employers) program for people in the field of business and economics. The programming language is even less intuitive then R, but there are many resources and professionals that can help you in learning the language. It is also only used for data analysis, so one can't get as creative in its analysis as R, but it's very fast. Good to learn for people who are looking to work in companies which require data analysts. It also has the ugliest graphics engine ever.. (but again, it's fast).</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-02 22:44:21BrentLaabsnote about demographics page <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 1: </td> <td> Line 1: </td> </tr> <tr> <td> </td> <td> <span>+ ''This article is about the ["University Departments" UCD Department]. For statistics about Davis people, see ["Demographics"].''<br> + </span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-07-02 21:49:44MattHheditted motoki's blurb (corrected some factual errors) and elaborated on grad <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 80: </td> <td> Line 80: </td> </tr> <tr> <td> <span>-</span> * Statistics, B.S. <span>with</span> <span>a minor in both a</span>pplied <span>m</span>ath<span>&nbsp;and quantitativ</span>e<span>&nbsp;biology and bioinfor</span>matics </td> <td> <span>+</span> * Statistics, B.S. <span>and</span> <span>A</span>pplied <span>M</span>athematics<span>, B.S.</span> </td> </tr> <tr> <td> Line 82: </td> <td> Line 82: </td> </tr> <tr> <td> <span>-</span> * Ph.D. Program at University of Washington in Seattle </td> <td> <span>+</span> * Ph.D. Program<span>&nbsp;in Quantitative Ecology and Resource Management</span> at University of Washington in Seattle </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-05-18 02:22:30JonasMari <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 12: </td> <td> Line 12: </td> </tr> <tr> <td> <span>- If you are a</span> Statistics ["Majors" Major]<span>, you</span> can earn the following degrees: </td> <td> <span>+</span> Statistics ["Majors" Major<span>s</span>] can earn the following degrees: </td> </tr> <tr> <td> Line 16: </td> <td> Line 16: </td> </tr> <tr> <td> </td> <td> <span>+ *M.S. in Statistics</span> </td> </tr> <tr> <td> Line 18: </td> <td> Line 19: </td> </tr> <tr> <td> </td> <td> <span>+ <br> + <br> + There are some proposed changes 2007-2008 school year:<br> + *B.S. in Applied Statistics (same requirements as B.A except 130A and 130B can replace 131A and 131B)<br> + *B.S. in Statistics<br> + *B.S. in Statistics, Computer Science Option</span> </td> </tr> <tr> <td> Line 19: </td> <td> Line 26: </td> </tr> <tr> <td> </td> <td> <span>+ *Ph.D. in Statistics<br> + *Ph.D. in Statistics with Emphasis in Biostatistics</span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 29: </td> </tr> <tr> <td> <span>-</span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 13<span>1a</span> (Probability Theory), 131<span>b (Intro to Mathematical Statistics) and one upper division course with 131b</span> as a prerequisite. If you are a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats minor in ["Quantitative biology and bioinformatics"]. </td> <td> <span>+</span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 13<span>0A</span> (Probability Theory),<span>&nbsp;130B (Mathematical Statistics) and one upper division course with 130B or</span> 131<span>B</span> as a prerequisite. If you are a math major intending on going to grad school in stat<span>istic</span>s or a bio major intending on going to grad school in biostat<span>istic</span>s, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. Specifically, many people who plan on going to grad school in biostats minor in ["Quantitative biology and bioinformatics"]. </td> </tr> <tr> <td> Line 25: </td> <td> Line 34: </td> </tr> <tr> <td> <span>-</span> Dr. Chris Drake, Undergraduate Advis<span>o</span>r </td> <td> <span>+</span> Dr. Chris Drake, Undergraduate Advis<span>e</span>r </td> </tr> <tr> <td> Line 27: </td> <td> Line 36: </td> </tr> <tr> <td> </td> <td> <span>+ Alicia Lin, Peer Advisor (2006-2007)</span> </td> </tr> <tr> <td> Line 34: </td> <td> Line 44: </td> </tr> <tr> <td> </td> <td> <span>+ ==Usual Professors==<br> + Wolfgang Polonik (Nonparametric Statistics, Probability Theory, Mathematical Statistics)<br> + * One of the most helpful professors, somewhat easy too<br> + * Thick German accent<br> + Katherine "Katie" S. Pollard (Categorical Data Analysis, Biostatistics)<br> + * Researcher at Genome Center<br> + * Her work was published in Time in October 2006.<br> + George Roussas (Probability Theory, Mathematical Statistics)<br> + * One of the oldest professors on campus whose courses are quite rigorous<br> + * Thick Greek accent<br> + * Makes you buy his textbook, which is quite expensive<br> + Prabir Burman (Biostatistics, Analysis of Variance, Regression Analysis, Multivariate Statistics)<br> + * One of the best professors<br> + Fushing Hsieh (Biostatistics, Analysis of Variance, Regression Analysis)<br> + * One the easiest professors<br> + Robert Shumway (Analysis of Variance, Regression Analysis, Multivariate Statistics, Time Series Analysis)<br> + * Time series specialist<br> + Duncan Temple-Lang (Statistics through computers)<br> + * He is one of the developers for R<br> + Chris Drake (Biostatistics, Sampling Theory)<br> + * Undergraduate Adviser<br> + * Biostatistics specialist<br> + <br> + ==Noteworthy Alumni==<br> + Anne Hansen (Spring '06)<br> + * Statistics, B.A. and Sociology, B.A.<br> + * Ph.D. program in statistics at UC Riverside<br> + Christabel Moy (Fall '06)<br> + * Statistics, B.A. and Biological Science, B.S.<br> + * Currently a junior scientist at UC Davis working on cancer research<br> + * Applying for med school<br> + ["JonasMari" Jonas Mari] (Spring '07)<br> + * Statistics, B.S. and Economics, B.A. with a minor in English<br> + * Science Writer for ["The California Aggie"]<br> + * Statistical Programmer at [http://www.jpresearch.com/ JP Research] in Mountain View, CA<br> + Motoki Wu (Spring '07)<br> + * Statistics, B.S. with a minor in both applied math and quantitative biology and bioinformatics<br> + * Featured on ["The California Aggie" The California Aggie's] [http://media.www.californiaaggie.com/media/storage/paper981/news/2007/02/07/ScienceTech/Behind.The.Small.Talk-2702914.shtml "Beyond the Small Talk"] for his work with the CLIMB program<br> + * Ph.D. Program at University of Washington in Seattle<br> + <br> + Please add to this list!<br> + </span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-04-14 15:51:30JasonAller <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 36: </td> <td> Line 36: </td> </tr> <tr> <td> <span>-</span> One might also be interested in some Wiki related statistics: ["User Statistics"] [http://baxter.cernio.com/mrtg/ Server Stats]<span>&nbsp;[[Include(Stub)]]</span> </td> <td> <span>+</span> One might also be interested in some Wiki related statistics: ["User Statistics"] [http://baxter.cernio.com/mrtg/ Server Stats] </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-03-20 10:20:47MattHhadded popular minor for biostats <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>-</span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 131a (Probability Theory), 131b (Intro to Mathematical Statistics) and one upper division course with 131b as a prerequisite. If you are a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes. </td> <td> <span>+</span> The department also offers a ["minors" minor] in statistics. Requirements are the core stats courses such as 106 (Analysis of Variance), 108 (Linear Regression), 131a (Probability Theory), 131b (Intro to Mathematical Statistics) and one upper division course with 131b as a prerequisite. If you are a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preparation you need, so that you don't enter having to take a bunch of undergrad classes.<span>&nbsp;Specifically, many people who plan on going to grad school in biostats minor in ["Quantitative biology and bioinformatics"].</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-03-14 16:16:38MattHh <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 31: </td> <td> Line 31: </td> </tr> <tr> <td> </td> <td> <span>+ ==Undergraduate Research==<br> + * ["CLIMB"] - is a program that focusses on mathematical and statistical modeling in biology. If you are interested in going to grad school in biostatistics, applied statistics or mathematics, or biology, this research program is for you.</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-01-29 09:03:56JabberWokkySpalling and the grammars. <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>- t</span>he department also offers a ["minors" minor] i<span>s</span> statistics. <span>Here one must tak</span>e the core stats courses such as 106 (<span>a</span>nalysis of <span>v</span>ariance), 108 (<span>linear r</span>egression), 131a (<span>probability thoe</span>ry), 131b (<span>intro to mathematical s</span>tatistics) and one upper division course with 131b as a prerequisite. If <span>one is</span> a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key prep<span>e</span>ration you need, so that you dont enter having to take a bunch of undergrad classes. </td> <td> <span>+ T</span>he department also offers a ["minors" minor] i<span>n</span> statistics. <span>Requirements ar</span>e the core stats courses such as 106 (<span>A</span>nalysis of <span>V</span>ariance), 108 (<span>Linear R</span>egression), 131a (<span>Probability Theo</span>ry), 131b (<span>Intro to Mathematical S</span>tatistics) and one upper division course with 131b as a prerequisite. If <span>you are</span> a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key prep<span>a</span>ration you need, so that you don<span>'</span>t enter having to take a bunch of undergrad classes. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-01-29 08:28:15MattHh <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>-</span> the department also offers a ["minor"<span>, minors</span>] is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes. </td> <td> <span>+</span> the department also offers a ["minor<span>s</span>"<span>&nbsp;minor</span>] is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-01-29 08:27:08MattHh <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>-</span> the department also offers a ["minor"] is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes. </td> <td> <span>+</span> the department also offers a ["minor"<span>, minors</span>] is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-01-29 08:26:21MattHh <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>-</span> the department also offers a <span>minor</span> is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes. </td> <td> <span>+</span> the department also offers a <span>["minor"]</span> is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes. </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2007-01-29 08:25:57MattHh <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> </td> <td> <span>+ the department also offers a minor is statistics. Here one must take the core stats courses such as 106 (analysis of variance), 108 (linear regression), 131a (probability thoery), 131b (intro to mathematical statistics) and one upper division course with 131b as a prerequisite. If one is a math major intending on going to grad school in stats or a bio major intending on going to grad school in biostats, these courses are considered to be the key preperation you need, so that you dont enter having to take a bunch of undergrad classes.<br> + </span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2006-10-13 20:51:29JonasMari <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 2: </td> <td> Line 2: </td> </tr> <tr> <td> <span>-</span> ||<span>???</span> ["Mathematical Sciences Building"], ["UC Davis"] || </td> <td> <span>+</span> ||<span>3rd &amp; 4th floors of the</span> ["Mathematical Sciences Building"], ["UC Davis"] || </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>- If you are a statistics major, you should edit this page!</span> </td> <td> <span>+ There are few statistics majors, although more have decided to add it since 2005.<br> + <br> + ==Undergraduate Advising==<br> + Dr. Chris Drake, Undergraduate Advisor<br> + Min Yu, Undergraduate Program Coordinator<br> + <br> + ==Courses==<br> + Check the UC Davis [http://registrar.ucdavis.edu/UCDWebCatalog/programs/STA/STAfac.html General Catalog]<br> + <br> + <br> + ===If you are a statistics major, you should edit this page!===</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2006-09-29 13:30:00ArlenAbrahamadded link to server stats <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 22: </td> <td> Line 22: </td> </tr> <tr> <td> <span>-</span> One might also be interested in some Wiki related statistics: ["User Statistics"] [[Include(Stub)]] </td> <td> <span>+</span> One might also be interested in some Wiki related statistics: ["User Statistics"] [<span>http://baxter.cernio.com/mrtg/ Server Stats] [</span>[Include(Stub)]] </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2006-04-28 23:54:55JosephBleckman <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 2: </td> <td> Line 2: </td> </tr> <tr> <td> <span>-</span> ||??? Mathematical Sciences Building, ["UC Davis"] || </td> <td> <span>+</span> ||??? <span>["</span>Mathematical Sciences Building<span>"]</span>, ["UC Davis"] || </td> </tr> <tr> <td> Line 22: </td> <td> Line 22: </td> </tr> <tr> <td> <span>-</span> One might also be interested in some Wiki related statistics: ["User Statistics"]<span><br> -</span> [[Include(Stub)]] </td> <td> <span>+</span> One might also be interested in some Wiki related statistics: ["User Statistics"] [[Include(Stub)]] </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2006-01-17 23:31:06RoyWrightSomeone in the Stats dept. really needs to edit this page. <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 2: </td> <td> Line 2: </td> </tr> <tr> <td> <span>-</span> ||<span>360</span> <span>K</span>e<span>rr H</span>all, ["UC Davis"] || </td> <td> <span>+</span> ||<span>???</span> <span>Math</span>e<span>matic</span>al<span>&nbsp;Sciences Bui</span>l<span>ding</span>, ["UC Davis"] || </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2005-12-09 15:06:56DavidReidYou know, in case someone came here wanting to get there. <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 20: </td> <td> Line 20: </td> </tr> <tr> <td> <span>- Horray!<br> - </span> </td> <td> </td> </tr> <tr> <td> Line 24: </td> <td> Line 22: </td> </tr> <tr> <td> </td> <td> <span>+ One might also be interested in some Wiki related statistics: ["User Statistics"]</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2005-12-09 13:26:01DomenicSantangelo+stub <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 23: </td> <td> Line 23: </td> </tr> <tr> <td> </td> <td> <span>+ <br> + [[Include(Stub)]]</span> </td> </tr> </table> </div> Statisticshttp://daviswiki.org/Statistics2005-06-28 12:47:51SummerSongwanted page <div id="content" class="wikipage content"> Differences for Statistics<p><strong></strong></p><table> <tr> <td> <span> Deletions are marked with - . </span> </td> <td> <span> Additions are marked with +. </span> </td> </tr> <tr> <td> Line 1: </td> <td> Line 1: </td> </tr> <tr> <td> </td> <td> <span>+ ||&lt;bgcolor='#E0E0FF'&gt;'''Location'''||<br> + ||360 Kerr Hall, ["UC Davis"] ||<br> + ||&lt;bgcolor='#E0E0FF'&gt;'''Hours'''||<br> + ||(Please fill in hours)||<br> + ||&lt;bgcolor='#E0E0FF'&gt;'''Phone'''||<br> + ||(530)752-2361||<br> + ||&lt;bgcolor='#E0E0FF'&gt;'''Fax'''||<br> + ||(530)752-7099||<br> + ||&lt;bgcolor='#E0E0FF'&gt;'''Website'''||<br> + ||[http://anson.ucdavis.edu/]||<br> + <br> + If you are a Statistics ["Majors" Major], you can earn the following degrees:<br> + *B.A. in Statistics<br> + *B.S. in Statistics<br> + *B.S. in Statistics Computer Science Option<br> + *Ph.D. in Statistics<br> + *Ph.D. in Statistics with Emphasis in Biostatistics<br> + *M.S. in Statistics<br> + <br> + Horray!<br> + <br> + If you are a statistics major, you should edit this page!</span> </td> </tr> </table> </div>