Multiple Regression

Front 1

Multiple regression is

Back 1

a data analysis technique that enables researcher to examine patterns of relationships between multiple independent variables and SINGLE dependent variable

Front 2

multiple variables formula for DV

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DV= (coefficent 1(effect 1) = coefficient 2(effect 2) +....+constant+residual

Front 3

multiple regress formula

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DV=(slope 1 (IV1)+ slope 2((IV2)+...Yintercept+ residual

Front 4

what is adjusted R(sq) for Mult Regression

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the squared multiple correlation between predicted and actual Y scores..the adjustment deflated bias based on sample size and #IV

Front 5

beta coefficients?

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the beta slope in standardized form, with scores converted into standard scores (so apples vs apples comparison)

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first step in Multiple Regression is to examine the interaction of the multiple IVs is that does not pass the F value, you ...

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stop there and don't go one, if good then examine each IV

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trustworthiness of results from any analysis can be affected by problems of

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sampling, measurement, the role of chance, and the technical assumptions of chosen analytic technique

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in Multiple regression, multicollinearity occurs when and with what effect?

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occurs when IV are highly correlated, effect of making it harder to reject null hypothesis around regression coefficients

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assumptions for Multple regression?

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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

Front 10

Bonferroni adjustment

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takes the traditional p value( alpha) .05 and divides it by # of tests, and that sets the new alpha threshold