Relationship Hours Spent Watching TV And The Overall GPA - 1100 Words

Relationship Between Hours Spent Watching TV And The Overall GPA (Essay Sample) Content: Relationship between Hours Spent Watching TV and the overall GPA Introduction It is widely believed that college students watch too much television. Some experts allege that it has a significant impact on learning since it takes much of time that could be dedicated to schoolwork. This study goes out to demonstrate whether or not hours spent watching television affects students performance and to what degree. Owing to the intricacies of sampling data beyond our geographical expanse, the population comprised of college students at Salisbury University. The study population composed of 183 students at Salisbury University. A sample of 183 students was appropriate due sampling related issues and decrease variability; however, the same conclusion is expected for learners in other universities across the United States. The analysis used various statistical methods such as Descriptive statistics, ANOVA, Cross-tabulation and Linear regression to examine the association between watching TV and GPA. However, the outcomes of this study do not form the benchmark for other institutions that might present different results. Variable Selection Variable selection involved the number of hours a student watch TV and their cumulative GPA. The dependent variable is watching TV while GPA is the independent variable. Hypothesis Null Hypothesis: Hours spent watching TV does not have an effect on GPA Alternative Hypothesis: Hours spent watching TV have an impact on GPA Data Analysis Descriptive Statistics Table 1: Descriptive Statistics Descriptive Statistics N Range Minimum Maximum Mean Std. Deviation Variance 13HrsTV 183 6.00 .00 6.00 1.5557 1.27744 1.632 10UGPA 183 2.1000 1.9000 4.0000 3.172459 .4407910 .194 Valid N (listwise) 183 Figure 1: Histogram showing GPA Figure 2: Histogram showing hours watching TV Regression analysis Table 2: Model Summary Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .023a .001 -.005 1.28063 .001 .093 1 181 .761 a. Predictors: (Constant), 10UGPA b. Dependent Variable: 13HrsTV Table 1:Regression Table 3: Coefficients Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero-order Partial Part Tolerance VIF 1 (Constant) 1.347 .690 1.953 .052 10UGPA .066 .215 .023 .305 .761 .023 .023 .023 1.000 1.000 a. Dependent Variable: 13HrsTV Table 4: Collinearity Diagnostics Collinearity Diagnostics Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) 10UGPA 1 1 1.991 1.000 .00 .00 2 .009 14.503 1.00 1.00 a. Dependent Variable: 13HrsTV Figure 3: Regression Standardized residual ANOVA Statistical Analysis Table 5 ANOVA 13HrsTV