Relationship Between Hours Spent Watching TV And The Overall GPA (Essay Sample)

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