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Multiple regression model |
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Definition
Using more than one explanatory variable to predict the value of a response variable |
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Multiple regression model = |
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yi = B0 + B1x1i + B2x2i + … + Bkxki + ei |
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Shows the linear correlation between each pair of variables under consideration in a multiple regression model |
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A relationship that exists between two explanatory variables if they have a high linear correlation |
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Definition
The effect of x1 on the value of the response variable does not depend on the value of x2 |
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Multiple regression, F0 = |
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Multiple regression, F0 using R^2 = |
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R^2/(1-R^2) x [(n-k+1)/k] where k is the number of explanatory variables |
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Decision rule for hypothesis testing, multiple regression |
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Definition
If P < alpha, then reject the null |
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1. Guidelines in developing a multiple regression model |
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Definition
Correlation matrix to identify explanatory variables that have a high correlation with the response variable |
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2. Guidelines in developing a multiple regression model |
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Use all explanatory variables that have been identified by the researcher |
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3. Guidelines in developing a multiple regression model |
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Definition
Run regressions, one by one removing variables with small t-statistics and high p-values |
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4. Guidelines in developing a multiple regression model |
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Repeat step 3 until all slope coefficients are significantly different from 0 |
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5. Guidelines in developing a multiple regression model |
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Draw residual plots to see if the model is appropriate |
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