A regression analysis done with Excel for the relationship between The placement test scores and the final grades for 20 students who took the regular course were recorded and yielded the following output. SUMMARY OUTPUT Regression Statistics Multiple R 0.45351 R Square 0.20567 Adjusted F 0.16155 Standard E 16.1747 Observatic 20 ANOVA df SS MS gnificance F Regression 1 1219.35 1219.35 4.66073 0.04461 Residual 18 4709.2 261.622 Total 19 5928.55 Chart Area Coefficientsandard Ernt Stat P-value Lower 95% Upper 95%ower 95.0%pper 95.0% 0.0192 5.95316 59.0587 5.95316 59.0587 Intercept 32.5059 12.6386 2.57195 X Variable 0.47106 0.2182 2.15887 0.04461 0.01264 0.92948 0.01264 0.92948 What percent of the total sum of squares (SST) can be explained by using the estimated regression equation to predict the course grade? A. 45.35% B. 20.57% C. 79.43% D. 21.82%

Using the Excel output reported above, if we were to test to see whether "Attendance" is statistically significantly associated with "Score received on the exam," we would conclude that we should Regression Statistics Multiple R R Square Standard Error Observations Intercept Attendance 0.142620229 0.02034053 20.25979924 22 Coefficients Standard Error 39.39027309 37.24347659 0.340583573 0.52852452 T Stat 1.057642216 0.644404489 P-value 0.302826622 0.526635689 O Reject the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Reject the null hypothesis and conclude that Attendance is is NOT statistically significantly associated with "Score received on the exam" Accept the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Fail to reject the null hypothesis and conclude that we cannot say that Attendance is statistically significantly associated with…

Shown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA   df SS Regression 1 24.061 Residual 10 67.979   Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis?   (b) Perform a t-test and determine whether or not x and y are related. Let ? = 0.05. State the null and alternative hypotheses. (Enter != for ≠ as needed.) H0:       Ha:       Find the value of the test statistic. (Round your answer to three decimal places.)   Find the p-value. (Round your answer to four decimal places.) p-value =  What is your conclusion? .

Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F 492.357679 11.531217 42.697806 Significance F 0.006092 Copy Data Ĵ Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F Significance F 492.357679 11.531217 0.006092 42.697806 Copy Data

Consider the following ANOVA table for a multiple regression model relating housing prices  (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 9090 total observations.  Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size)Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size)                                     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 22 306,443.7975306,443.7975 153,221.8988153,221.8988 21.727421.7274 2.2211E-082.2211E-08 Residual 8787 613,525.5190613,525.5190 7052.01757052.0175     Total 8989 919,969.3165919,969.3165       What percent of variation in housing prices is explained by the number of bedrooms and lot size? Round your answer to two decimal places

A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the​employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees.       ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12     Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95​% confidence interval around the sample slope and interpret its meaning.   The confidence interval is ​(__________​,_________). ​(Type an integer or decimal rounded to three decimal places as​ needed.)

Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…

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…