# Statistics II Midterm

 Parametric depend on population characteristicsmore sensative and versatile Z-Test T-test Independent t-Test Z-test: need mean and SD, and Pop scores must be normally distributedT-Test: need mean and Pop scores normally distributedIndependent t-Test: need equal Pop variances Nonparametric distribution-free tests (Chi-Square) Chi-Square uses frequencies 2-Way Chi-Square 2 categorical variables to determine if variables are independent/related Assumptions of Chi-Square Groups are mutually exclusiveTallies obtained independently Repeated Measures ANOVA same individuals measured across time or in more than 2 conditions Advantages of Repeated Measures ANOVA Reduces unsystematic variability and gives greater power to detect differencesFewer participants requiredSphericity-scores likely to be related Sphericity equality of variance between treatment levels Mauchly's Test tests sphericityIf significant (below .05), then sphericity isn't met Multiple Regression predict outcome based on >1 IV (multivariable)Outcome = model + error The Model best fitting straight line used to estimate outcome variable Total Sum of Squares E(o-e)2 how good mean is as a model Residual Sum of Squares difference between observed and regression line Model Sum of Squares difference between outcome and regression lineshows reduction in inaccuracy Outlier extreme score Residual Predicted Outcome - Sample Data Outcomehave to standardize>5% = model is poor representation of data Cook's Distance Influences of a case on the model>1 = cause for concern Leverage (Hat Values) 0 (no influence) to 1 (complete influence)Influence of observed over predicted Mahalanobis Distance Measures distance of cases from the mean of predictor Multiple Regression Assumptions Non-zero varianceAbsence of MulticollinearityHomoscedascicityIndependent and Normally Distributed ErrorsIndependenceLinearity MANOVA Multivariate: many DVs Omnibus test statistic Alphas: Nominal, Actual, Familywise, Experimentwise Nominal- alpha researcher desiresActual- alpha obtained (type I error)Familywise- type I error within a testExperimentwise- all tests used within a study MANOVA Assumptions IndependenceRandom SamplingMultivariate NormalityHomogeneity of Covariance Matrices Following a Significant MANOVA Multiple ANOVAs (for each DV)Reverse variables to predict which group people belong to Factorial ANOVA Second IV that's been systematically manipulated by assigning people to different conditions Factorial ANOVA: 3 Things Main Effect for XMain Effect for YInteraction Between X and Y Factorial ANOVA: "way" means: number of IV Multivariable vs. Multivariate Multivariable- 2+ IVMultivariate- 2+ DV Path Analysis X causes Y and Y causes Z One Sample t-Test sample compared to population Independent Measures t-Test means compared between 2 groups Repeated Measures t-Test means compared between 2 conditions with 1 group ANOVA has ____ groups 3+ Orthogonality zero correlation between variables Experimental Researcher controls IVRandom assignment Multiple Regression: Non-zero Variance Predictors should have some variation in valueThey cannot and should not have variances of 0 (otherwise, there is nothing to measure) Multiple Regression: Absence of Collinearity There should be NO perfect linear relationship between two or more predictorsAND no two predictors should be too highly correlated Multiple Regression: Homoscedasticity At each level of the predictor variable, the variance of the residual terms should be constantIf variances are different- Heteroscedastic Multiple Regression: Independent Errors For 2 observations, residual terms should be uncorrelated (independent)Values range between 0 and 4. Values of 2 means residuals are uncorrelated Multiple Regression: Normally Distributed Errors Residuals in the model are random, normally distributed variables with a mean of 0(DOES NOT mean predictors should be normally distributed) Multiple Regression: Linearity The mean values of the outcome variable for each increment of the predictor lie along a straight lineAKA the relationship is linear! MANOVA: Multivariate Normality DVs and any combination of DVs must be normally distributed MANOVA: Homogeneity of Covariance Matrices Variances for all DVs must be equal across the experimental groupsANDThe covariance for all unique pairs of DVs should be equal Authorshelbymailho ID140415 Card SetStatistics II Midterm DescriptionStatistics II Midterm Updated2012-03-08T03:26:19Z Show Answers