Statistics II Midterm

• depend on population characteristics
• more sensative and versatile

• 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

distribution-free tests (Chi-Square)

frequencies

2 categorical variables to determine if variables are independent/related

• Groups are mutually exclusive
• Tallies obtained independently

same individuals measured across time or in more than 2 conditions

• Reduces unsystematic variability and gives greater power to detect differences
• Fewer participants required
• Sphericity-scores likely to be related

equality of variance between treatment levels

• tests sphericity
• If significant (below .05), then sphericity isn't met

• predict outcome based on >1 IV (multivariable)
• Outcome = model + error

best fitting straight line used to estimate outcome variable

• E(o-e)²
• how good mean is as a model

difference between observed and regression line

• difference between outcome and regression line
• shows reduction in inaccuracy

extreme score

• Predicted Outcome - Sample Data Outcome
• have to standardize
• >5% = model is poor representation of data

• Influences of a case on the model
• >1 = cause for concern

• 0 (no influence) to 1 (complete influence)
• Influence of observed over predicted

Measures distance of cases from the mean of predictor

• Non-zero variance
• Absence of Multicollinearity
• Homoscedascicity
• Independent and Normally Distributed Errors
• Independence
• Linearity

• Multivariate: many DVs
• Omnibus test statistic

• Nominal- alpha researcher desires
• Actual- alpha obtained (type I error)
• Familywise- type I error within a test
• Experimentwise- all tests used within a study

• Independence
• Random Sampling
• Multivariate Normality
• Homogeneity of Covariance Matrices

• Multiple ANOVAs (for each DV)
• Reverse variables to predict which group people belong to

Second IV that's been systematically manipulated by assigning people to different conditions

• Main Effect for X
• Main Effect for Y
• Interaction Between X and Y

number of IV

• Multivariable- 2+ IV
• Multivariate- 2+ DV

X causes Y and Y causes Z

sample compared to population

means compared between 2 groups

means compared between 2 conditions with 1 group

3+

zero correlation between variables

• Researcher controls IV
• Random assignment

• Predictors should have some variation in value
• They cannot and should not have variances of 0 (otherwise, there is nothing to measure)

• There should be NO perfect linear relationship between two or more predictors
• AND no two predictors should be too highly correlated

• At each level of the predictor variable, the variance of the residual terms should be constant
• If variances are different- Heteroscedastic

• For 2 observations, residual terms should be uncorrelated (independent)
• Values range between 0 and 4. Values of 2 means residuals are uncorrelated

• Residuals in the model are random, normally distributed variables with a mean of 0
• (DOES NOT mean predictors should be normally distributed)

• The mean values of the outcome variable for each increment of the predictor lie along a straight line
• AKA the relationship is linear!

DVs and any combination of DVs must be normally distributed

• Variances for all DVs must be equal across the experimental groups
• AND
• The covariance for all unique pairs of DVs should be equal