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10 Cards in this Set

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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