c Testing Hypothesis 4 For testing hypothesis Exercise count have positive

C testing hypothesis 4 for testing hypothesis

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c. Testing Hypothesis 4 For testing hypothesis: Exercise count have positive effect on final test score, use criteria: Ho : There are no positive effect exercise count on final test score. Ha : There are positive effect exercise count on final test score. Criteria: Ho accepted if tstats ≤ t table Ha accepted if tstats > t table Tstats X3 (1.012) ≤ T table (1.67252), so Ho accepted, There are no positive effect on exercise count on final test score .
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4. Testing Hypothesis 5 Beta Coefficient Test: ꞵX1 = 0.864 ꞵX2 = 0.70 ꞵX3 = 0.043 Because of ꞵX1has the greater number, so studying hour (X1) effected final test score the most. 5. F-Test ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 1836.722 3 612.241 226.739 .000 b Residual 151.211 56 2.700 Total 1987.933 59 a. Dependent Variable: y b. Predictors: (Constant), x3, x2, x1 F Stat used to test goodness of fit model, does prediction value can descript fact condition or not: Ho: accepted if Fstats ≤ Ftable Ha: accepted if Fstats > Ftable Because of Fstats (226.739) > Ftable (2.77), so the equation of regression is a good fit. 6. Normality Test One-Sample Kolmogorov-Smirnov Test Standardized Residual N 60 Normal Parameters a,b Mean .00000 Std. Deviation .974245 Most Extreme Differences Absolute .057 Positive .057 Negative -.039 Test Statistic .057 Asymp. Sig. (2-tailed) .200 c,d a. Test distribution is Normal.
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b. Calculated from data. c. Lilliefors Significance Correction. d. This is a lower bound of the true significance. Because the value of Sig. > 0.05, it is not significant. It’s mean relatively the same data with the average so called normal. 7. Multicolinearity Test Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 34.351 1.599 21.479 .000 x1 .665 .067 .864 9.881 .000 .178 5.627 x2 .347 .343 .070 1.012 .316 .281 3.562 x3 .108 .183 .043 .590 .558 .257 3.897 a. Dependent Variable: y Because VIF < 10 it means doesn’t happen multicolinearity .
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