In the following regression, X = weekly pay, Y = income tax withheld, and n = 35 McDonald's employees.       R2 0.202 Std. Error 6.816 n 35     ANOVA table Source SS df MS F p-value Regression   387.6959     1     387.6959     8.35     .0068   Residual   1,533.0614     33     46.4564               Total   1,920.7573     34                         Regression output confidence interval variables coefficients std. error t Stat p-value Lower 95% Upper 95% Intercept   30.7963     6.4078     4.806     .0000     17.7595     43.8331   Slope   0.0343     0.0119     2.889     .0068     0.0101     0.0584       (a) Write the fitted regression equation.    YˆY^ =  +  X   (b-1) State the degrees of freedom for a two-tailed test for zero slope, and use Appendix D to find the critical value at α = .05. (Round t.025 to 3 decimal places.)    Degrees of freedom   t.025     (b-2) Choose the correct option for H0: β1 = 0 vs H1: β1 ≠ 0.    Reject the null hypothesis if tcalc > 2.035 or if tcalc < -2.035 Reject the null hypothesis if tcalc < -2.035 or if tcalc > -2.035   (c-1) Calculate t. (Round your answer to 3 decimal places.)    tcalc               (c-2) We reject the null hypothesis.    Yes No   (d-1) Find the 95% confidence interval for slope. (Round your answers to 4 decimal places.)    Confidence interval is from    to     (d-2) The confidence interval does not contain zero, which implies    there is no relationship between weekly pay and income tax withheld. there is a relationship between weekly pay and income tax withheld.   (e) Calculate t2 and F. (Round your answers to 2 decimal places.)     t2   Fcalc     (f-1) Calculate R2. (Round your answer to 3 decimal places.)    R2               (f-2) What is the percentage of variation in income tax withheld explained by weekly pay? (Round your answer to the nearest whole number.)    Percentage of variation  %

Refer to the ANOVA table for this regression.  Source d.f. SS MS Regression 2 578,850 289,425 Error 21 378,420 18,020 Total 23 957,270     Using Appendix F on the pictures, calculate F.05, a=0.05, F statistics, R2, and R2 adj.

Use the following ANOVA table for regression to answer the questions. Response: Y Source DF Sum Sq Mean Sq F-value Pr(>F) Regression 1 351.47 351.47 13.32 0.000 Residual Error 359 9474.01 26.39     Total 360 9825.48                 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is .The p-value is .

A researcher is interested in whether an approved supplement enhances the performance of baseball players. The research compares two different baseball teams on the number of home runs they hit for the season. Which test is most appropriate to conduct? Chi-square Independent t-test Correlation/Regression Dependent t-test One-Way ANOVA

A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     Using α = 0.01 to test the…

WALKING RUNNING Mean 7.181818182 7.3636364 Variance 4.563636364 6.4545455 Observations 11 11 Pooled Variance 5.509090909   Hypothesized Mean Difference 0   df 20   t Stat -0.181668105   P(T<=t) one-tail 0.428835886   t Critical one-tail 1.724718243   P(T<=t) two-tail 0.857671772   t Critical two-tail 2.085963447   If the t-Stat value is less than the t Critical two-tail value I can reject the null, right? I am still a little confused on what a few numbers represent.