PPT-Xuhua Xia Slide 1 Multivariate statistics

PCA principal component analysis Correspondence analysis Canonical correlation Discriminant function analysis Cluster analysis MANOVA PCA Given a set of variables x 1 x 2 x. Fitting Several Regression Lines. Many applications of statistical analysis involves a continuous variable as dependent variable (DV) but both continuous and categorical variables as independent variables (IV). . An Introduction &. Multidimensional Contingency Tables. What Are Multivariate Stats?. . Univariate = one variable (mean). Bivariate = two variables (Pearson . r. ). Multivariate = three or more variables simultaneously analyzed . for Social. and . Behavioral. . Sciences. Part IV: Causality. Multivariate. . Regression. Chapter. 11. Prof. Amine Ouazad. Movie Buzz. Can we predict the success of a movie?. Avatar (2009) $760,505,847. and decoding. Kay H. Brodersen. Computational Neuroeconomics Group. Institute of Empirical Research in Economics. University of Zurich. Machine Learning and Pattern Recognition Group. Department of Computer Science. for Social. and . Behavioral. . Sciences. Part IV: Causality. Multivariate. . Regression. R . squared. , F test, . Chapter. . 11. Prof. Amine Ouazad. Data: Variables. y. . Box . = First run U.S. box office ($. Kevin Wilcox. ECOL 600 – Community Ecology. Spring 2014. Useful web resources. Vegan . tutorial:. http. ://cc.oulu.fi/~. jarioksa/opetus/metodi/vegantutor.pdf. The little book of r for multivariate analyses: . models for fMRI . data. Klaas Enno Stephan. (with 90% of slides kindly contributed by . Kay H. Brodersen. ). Translational . Neuromodeling. Unit (TNU). Institute for Biomedical Engineering. University . Xuhua Xia. xxia@uottawa.ca. http://dambe.bio.uottawa.ca. Xuhua Xia. Slide . 2. Normal and Thalassemia HBb. Are the two genes homologous?. What evolutionary change can you infer from the alignment? . What is the consequence of the evolutionary change?. Stepwise Regression. Y may depend on many independent variables. How to find a subset of X’s that best predict Y?. There are several criteria (e.g., adjusted R. 2. , AIC, BIC, likelihood ratio test, etc.) for model selection and many algorithms for including or excluding X’s in the model: forward selection, backward elimination, stepwise regression, etc.. Xuhua Xia. xxia@uottawa.ca. http://dambe.bio.uottawa.ca. Xuhua Xia. Slide . 2. Normal and Thalassemia HBb. Are the two genes homologous?. What evolutionary change can you infer from the alignment? . What is the consequence of the evolutionary change?. Xuhua Xia. xxia@uottawa.ca. http://dambe.bio.uottawa.ca. Xuhua Xia. Slide . 2. Lecture Outline. Objectives in this lecture. Grasp the basic concepts distance-based tree-building algorithms. Learn the least-squares criterion and the minimum evolution criterion and how to use them to construct a tree. Many applications of statistical analysis involves a continuous variable as dependent variable (DV) but both continuous and categorical variables as independent variables (IV). . Relationship between DV and continuous IVs is linear and the slope remains the same in different groups: ANCOVA.. Xuhua Xia. xxia@uottawa.ca. http://dambe.bio.uottawa.ca. Xuhua Xia. Slide . 2. Normal and Thalassemia HBb. Are the two genes homologous?. What evolutionary change can you infer from the alignment? . What is the consequence of the evolutionary change?. Xuhua Xia. xxia@uottawa.ca. http://dambe.bio.uottawa.ca. Xuhua Xia. Slide . 2. Normal and Thalassemia HBb. Are the two genes homologous?. What evolutionary change can you infer from the alignment? . What is the consequence of the evolutionary change?.