# Statistics II Midterm

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1. Parametric
• depend on population characteristics
• more sensative and versatile
2. Z-Test
T-test
Independent t-Test
• Z-test: need mean and SD, and Pop scores must be normally distributed
• T-Test: need mean and Pop scores normally distributed
• Independent t-Test: need equal Pop variances
3. Nonparametric
distribution-free tests (Chi-Square)
4. Chi-Square uses
frequencies
5. 2-Way Chi-Square
2 categorical variables to determine if variables are independent/related
6. Assumptions of Chi-Square
• Groups are mutually exclusive
• Tallies obtained independently
7. Repeated Measures ANOVA
same individuals measured across time or in more than 2 conditions
8. Advantages of Repeated Measures ANOVA
• Reduces unsystematic variability and gives greater power to detect differences
• Fewer participants required
• Sphericity-scores likely to be related
9. Sphericity
equality of variance between treatment levels
10. Mauchly's Test
• tests sphericity
• If significant (below .05), then sphericity isn't met
11. Multiple Regression
• predict outcome based on >1 IV (multivariable)
• Outcome = model + error
12. The Model
best fitting straight line used to estimate outcome variable
13. Total Sum of Squares
• E(o-e)2
• how good mean is as a model
14. Residual Sum of Squares
difference between observed and regression line
15. Model Sum of Squares
• difference between outcome and regression line
• shows reduction in inaccuracy
16. Outlier
extreme score
17. Residual
• Predicted Outcome - Sample Data Outcome
• have to standardize
• >5% = model is poor representation of data
18. Cook's Distance
• Influences of a case on the model
• >1 = cause for concern
19. Leverage (Hat Values)
• 0 (no influence) to 1 (complete influence)
• Influence of observed over predicted
20. Mahalanobis Distance
Measures distance of cases from the mean of predictor
21. Multiple Regression Assumptions
• Non-zero variance
• Absence of Multicollinearity
• Homoscedascicity
• Independent and Normally Distributed Errors
• Independence
• Linearity
22. MANOVA
• Multivariate: many DVs
• Omnibus test statistic
23. Alphas: Nominal, Actual, Familywise, Experimentwise
• Nominal- alpha researcher desires
• Actual- alpha obtained (type I error)
• Familywise- type I error within a test
• Experimentwise- all tests used within a study
24. MANOVA Assumptions
• Independence
• Random Sampling
• Multivariate Normality
• Homogeneity of Covariance Matrices
25. Following a Significant MANOVA
• Multiple ANOVAs (for each DV)
• Reverse variables to predict which group people belong to
26. Factorial ANOVA
Second IV that's been systematically manipulated by assigning people to different conditions
27. Factorial ANOVA: 3 Things
• Main Effect for X
• Main Effect for Y
• Interaction Between X and Y
28. Factorial ANOVA: "way" means:
number of IV
29. Multivariable vs. Multivariate
• Multivariable- 2+ IV
• Multivariate- 2+ DV
30. Path Analysis
X causes Y and Y causes Z
31. One Sample t-Test
sample compared to population
32. Independent Measures t-Test
means compared between 2 groups
33. Repeated Measures t-Test
means compared between 2 conditions with 1 group
34. ANOVA has ____ groups
3+
35. Orthogonality
zero correlation between variables
36. Experimental
• Researcher controls IV
• Random assignment
37. Multiple Regression: Non-zero Variance
• Predictors should have some variation in value
• They cannot and should not have variances of 0 (otherwise, there is nothing to measure)
38. Multiple Regression: Absence of Collinearity
• There should be NO perfect linear relationship between two or more predictors
• AND no two predictors should be too highly correlated
39. Multiple Regression: Homoscedasticity
• At each level of the predictor variable, the variance of the residual terms should be constant
• If variances are different- Heteroscedastic
40. 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
41. 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)
42. Multiple Regression: Linearity
• The mean values of the outcome variable for each increment of the predictor lie along a straight line
• AKA the relationship is linear!
43. MANOVA: Multivariate Normality
DVs and any combination of DVs must be normally distributed
44. MANOVA: Homogeneity of Covariance Matrices
• Variances for all DVs must be equal across the experimental groups
• AND
• The covariance for all unique pairs of DVs should be equal

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 Author: shelbymailho ID: 140415 Filename: Statistics II Midterm Updated: 2012-03-08 03:26:19 Tags: Stats Folders: Description: Statistics II Midterm Show Answers:

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