• Shuffle
Toggle On
Toggle Off
• Alphabetize
Toggle On
Toggle Off
• Front First
Toggle On
Toggle Off
• Both Sides
Toggle On
Toggle Off
• Read
Toggle On
Toggle Off
Reading...
Front

### How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key

Play button

Play button

Progress

1/10

Click to flip

### 10 Cards in this Set

• Front
• Back
 Multiple regression is a data analysis technique that enables researcher to examine patterns of relationships between multiple independent variables and SINGLE dependent variable multiple variables formula for DV DV= (coefficent 1(effect 1) = coefficient 2(effect 2) +....+constant+residual multiple regress formula DV=(slope 1 (IV1)+ slope 2((IV2)+...Yintercept+ residual what is adjusted R(sq) for Mult Regression the squared multiple correlation between predicted and actual Y scores..the adjustment deflated bias based on sample size and #IV beta coefficients? the beta slope in standardized form, with scores converted into standard scores (so apples vs apples comparison) first step in Multiple Regression is to examine the interaction of the multiple IVs is that does not pass the F value, you ... stop there and don't go one, if good then examine each IV trustworthiness of results from any analysis can be affected by problems of sampling, measurement, the role of chance, and the technical assumptions of chosen analytic technique in Multiple regression, multicollinearity occurs when and with what effect? occurs when IV are highly correlated, effect of making it harder to reject null hypothesis around regression coefficients assumptions for Multple regression? 1. normality of distribution-check by doing histogram and look for fairly normal curves 2. homoscedasticity-homogeniety of variance 3.linearity-data cloud can be summarized with straight line besy Bonferroni adjustment takes the traditional p value( alpha) .05 and divides it by # of tests, and that sets the new alpha threshold