Lab 11: Multiple Regression (worth 40 points) Introduction to Lab This lab follows up on lab 10 on time spent watching TV. You will use regression analysis to analyze the combined effects of your independent variables on your dependent variable tvhours. STEP ONE: Choosing Independent Variables Choose at least 4 independent variables that you think might be associated with tvhours. Remember that independent variables cannot be nominal in the level of measurement unless you recode the variable into a series of dummy variables. Independent variable 1: CHILDS Independent variable 2: HEALTH Independent variable 3: MARITALI Independent variable 4: OVERWORK Other independent variables: STEP TWO: Running the Regression Procedure Click Analyze, Regression, Linear and place tvhours in the "Dependent" box and your selected independent variables in the "independent(s) " box. Click Statistics and Descriptives, then OK. Descriptive Statistics Mean Std. Deviation N Hours per day watching TV 2.21 1.770 464 Number of children 1.77 1.544 464 Condition of health 1.97 .740 464 Marital status 2.67 1.713 464 R has too much work to do 2.63 .744 464 well Correlations R has too Hours per day Number of Condition of much work to watching TV children health Marital status do well Pearson Correlation Hours per day watching TV 1.000 042 019 047 .028 Number of children .042 1.000 .007 .359 .112 Condition of health .019 ..007 .000 051 ..053 Marital status .047 -.359 051 1.000 .020 R has too much work to do 028 -.112 -.053 020 1.000 well Sig. (1-tailed) Hours per day watching TV 182 342 .158 275 Number of children .182 439 000 008 Condition of health .342 439 135 128 Marital status .158 000 135 331 R has too much work to do 275 008 128 .331 well N Hours per day watching TV 464 464 464 464 464 Number of children 464 464 464 464 464 Condition of health 464 464 464 464 464 Marital status 464 464 464 464 464 R has too much work to do 464 464 464 464 464 well Model Summary Coefficients Standardized Adjusted R Std. Error of the Unstandardized Coefficients Coefficients Beta Model R R Square Square Estimate Model Std. Error sig. (Constant) 1.563 .438 3.565 <.001 number of children .057 .072 condition health .018 .380 .704 a. predictors: r has too much work to do well marital status .156 .754 dependent variable: hours per day watching tv copy paste your output correlations model summary and coefficients tables here then complete the table below:a="(see" in interpret a sentence. b slopes state regression line numbers for y="a" bxi bx3 beta r2 square as if all independent variables are zero mean variable spent will be m l.v. childs: .082 health: .042 maritali: .073 overwork: .084 unit change there is corresponding increase by about overall one feels overworked .082x1 .073x3 .071 .035 adjusted sentence implies that ofthe variation can accounted variables. step three: interpreting results write addressing: which statistically signicantly related at p what direction those relationships compare strength relationships. total percentage variance explained this interpretation should paragraph form with sentences report you using make various claims existence conclude discussing these analyses taken together say time>