Unit 7 Statistics: Multivariate Analysis of Variance (MANOVA) & Discriminant Functional Analysis (DFA) Chat until class starts

Unit 7 Assignments Reading: Chapters 16 and 17 of your textbook Seminar: During seminar, we will discuss DFA, MANOVA and your Unit 7 project Discussion: You need to answer the discussion question in a response of at least 350 words or longer. Also, provide a minimum of 2 thoughtful responses to other student posts per thread that are at least 75 words. Project: Submit the Unit 7 project covering MANOVA

What is a General Linear Model? In General Linear Models, variables are related to each other in a linear fashion (straight line). In this model, variables are weighted differently to show which ones contribute most to the criterion variable.

What is Discriminant Functional Analysis (DFA)? The goal of Discriminant Analysis is to predict group membership (categorical variables) from a set of predictor variables (quantitative variables). For example, if I wanted to study differences between “college degree completion” and “those who did not complete degree.” I might have a set of predictor variables, such as: SAT scores, college GPA, and personality scores. The weightings will tell you which variables are most predictive of classification.

What are Eigen Values? Eigen values tell you how strongly a discriminant function scores are associated with group membership. Strong eigen values minimize within-group membership and maximize between-group membership. It tells you about the strength of relationship between group membership and scores on weighted linear combination of variables

MANOVA & Unit 7 Project Multiple Analysis of Variance (MANOVA). In the Unit 3 project, we discussed Analysis of Variance (ANOVA), which had one independent and one dependent variable. MANOVA is an extension of the ANOVA, by including more than one dependent variable. In MANOVA, your independent variable is categorical data, and your dependent variables contain quantitative data. You need to download “talent.sav” and compute analysis. Also, look at example posted in doc sharing.

MANOVA Terms Box M Test: Equality of covariance between groups Levine F: If not significant, variance is equal between groups Wilks Lambda: Overall strength of the model. If model is significant, we can look at individual variables

MANOVA Websites s/colleges/nursing/research/Docume nts/MANOVAHowTo.pdf put/SPSS_MANOVA_AO.htm s/STA7114%20Files/Lab%204/Instruction s/manovapage.htm

DFA Websites: k/general-discriminant- analysis/ edu/psycrs/sta tpage/2gldf.pdf