any observed effect of “Number of people in the house” can then be said to be “independent of the effects of these variables that already have been controlled for. This ensures that they will get credit for any shared variability that they may have with the predictor that we are really interested in, “Number of people in the house”. To make sure that these variables (age, education, gender, union member, and retired) do not explain away the entire association between the “number of people in the house” and “Household income in thousands”, let put them into the model first. We also concerned that other variables like age, education, gender, union member, or retired might be associated with both “number of people in the house” and “household income in thousands”. For example, in this analysis, we want to find out whether “Number of people in the house” predicts the “Household income in thousands”. These variables that you want SPSS to put into the regression model first (that you want to control for when testing the variables). To obtain the 95 confidence interval for the slope, click on the Statistics button at the bottom and then put a check in the box for Confidence Intervals.Hit Continue and then hit OK. Next, enter a set of predictors variables into independent(s) pan. Regression Analysis To perform the regression, click on AnalyzeRegressionLinear.Place nhandgun in the Dependent box and place mankill in the Independent box. For example “income” variable from the sample file of customer_dbase.sav available in the SPSS installation directory. In the main dialog box of linear regression (as given below), input the dependent variable.
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