b. All requested variables entered. Model Summary Adjusted R Std. Error of Squar
ID: 3254126 • Letter: B
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b. All requested variables entered. Model Summary Adjusted R Std. Error of Square the Estimate Model R Square 004 .40316 063 002 a. Predictors: (Constant), Hours facebook per day ANOVA Sum of Squares df Mean Square Model Sig. 1 .410 2.523 113 .410 Regression 103.047 634 163 Residual 635 Total 103.457 a. Dependent Variable: GPA last quarter b. Predictors: (Constant, Hours facebook per day Coefficientsa Standardized Unstandardized Coefficients Coefficients Std. Error Beta Model .018 183.914 (Constant) 3.392 Hours facebook per day 003 .002 063 1.588 a. Dependent Variable: GPA last quarter Sig 000 113Explanation / Answer
The regression equation is given as GPA = 3.392 – 0.003*HOURS_FB_PER_DAY
With a unit increase in hour’s face-book per day there is 0.003 units decrease in GPA last quarter.
With R^2 = .4%, I can say that there is only .4% variation in GPA last quarter hou which is explained by independent variables r’s face-book per day. This percentage is very less and model is not considered be a good fit to the data. Fox, J. (1997).
Consider null hypothesis, ho: model is significant. This is tested against alternative hypothesis, h1: model is not significant. With F = 2.523 and p-value greater than 0.05, I fail to reject ho and conclude that model is not significant. Draper, N. R., Smith, H., & Pownell, E
It is suggested to consider other significant independent variables in the regression model for more reliable and better results. These variables can be number of hours study, attendance % etc. it is also suggested to make use of step wise regression model where best model can be selected on basis of value of Adj R^2. The value of Adj R^2 increases with addition of significant variables. Glantz, S. A., & Slinker, B. K. (1990).
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