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Multiple Regression, Example research scenario: Is there a relationship…

- - - - move over the outcome variable (DV) to the 'dependent variable ' section
        
        then move over ALL the predictor variables (IV) to 'covariates'
        
        NOW, focus on the assumptions -->
      - mental note: 'dependent variable section' = coutcome variable
        'covariates' = predictor variable
    - - ASSUMPTIONS (3/4)
        
        1. test for residuals/outliers
        
        go to 'statistics'
        
        look for the header 'residuals' and select:
        
        a) casewise disgnostics for outliers
        
        b) Keep the standard residuals as 3
        
        JASP will create a table called 'casewise diagnostic'
        
        1 more item...
        
        2. are residuals /outliers normally distributed?
        
        go to 'plots' dropbar
        
        select 'residual vs. histogram'
        
        ensure 'standardised residual' is still selected
        
        review it, does it look normal?
        
        2 more items...
        
        3. test for homoscedasticity (& look for linearity)
        
        under 'residual plots', select 'residual vs. predicted'
        
        a spotted graph should appear
        
        the dots should be randomly distributed, not a 'funnelled' triangular pattern
        
        if they are randomly distributed, this suggests there is linearity
        
        1 more item...
      - Go to 'regression'
        (to test for multicolinearity)
        
        Click on 'correlation'
        
        only move the predictor (IV's) to the 'variables'
        
        Under the header 'sample correlation coefficient, select 'pearson's r'
        
        under 'additional ... select 'display pairwise'
        
        deselect 'report significance'
        
        1 more item...
        
        information which we have obtained this far #we are done
        
        Regression results = 'model summary'
        this contains the
        
        r2 value
        
        adjusted r2
        
        ANOVA table
        
        coefficients table with the
        
        predictors
        
        intercept
        -slope for the predictors
        -info on multicolinearity
        
        tests for the assumptions