Statistics II Midterm

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Statistics II Midterm
2012-03-07 22:26:19

Statistics II Midterm
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  1. Parametric
    • depend on population characteristics
    • more sensative and versatile
  2. Z-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
  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
  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