A regression analysis done with Excel for the relationship between Theplacement test scores and the final grades for 20 students who took the regularcourse were recorded and yielded the following output.SUMMARY OUTPUTRegression StatisticsMultiple R 0.45351R Square 0.20567Adjusted F 0.16155Standard E 16.1747Observatic20ANOVAdfSSMSFgnificance F0.044611 1219.351219.35 4.66073RegressionResidualTotal18 4709.2 261.62219 5928.55Chart AreaCoefficientsandard Ernt StatIntercept 32.5059 12.6386 2.57195P-value Lower 95% Upper 95%ower 95.0%pper 95.0%0.0192 5.95316 59.0587 5.95316 59.0587X Variable 0.47106 0.2182 2.15887 0.04461 0.01264 0.92948 0.01264 0.92948What percent of the total sum of squares (SST) can be explained by using theestimated regression equation to predict the course grade?A. 45.35%B. 20.57%C. 79.43%D. 21.82%

what percent of the total sum of squares can be explained by using the estimated regression equation…

Answered: A regression analysis done with Excel…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Based on the given multiple regression output tables below, answer the following questions: Model Summary Adjusted R Square Std. Error of the Estimate Durbin- Model R R Square Watson 1 .842 .710 .630 109.430 1.158 ANOVA Sum of Model Squares df Mean Square F Sig. Regression 321946.82 3 107315.6 8.96 0.0027 Residual 131723.20 11 11974.8 Total 453670.00 14 Coefficients Standardized Coefficients Unstandardized Coefficients Model B Std. Error Beta Sig. t (Constant) X Variable 1 X Variable 2 X Variable 3 657.053 167.46 3.92 .0024 5.7103 1.792 -.101 3.19 .0087 -0.4169 0.322 -.077 -1.29 .2222 -3.4715 1.443 -7.996 -2.41 .0349 (a) State the predictors available in this model. (b) Determine the multiple regression model for this analysis.
A study was conducted to examine how fear of public speaking varies across year of tertiary education (1st, 2nd or 3rd). Subjects were interviewed at the end of each of year of their tertiary education for fear of public speaking (measured on a 10 point metric scale). Which of the following would be an appropriate statistical test to conduct, which addresses these hypotheses? Group of answer choices: Factorial ANOVA Mixed ANOVA Multiple Regression Within Subjects ANOVA Single Factor ANOVA
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.
An experiment was carried out to compare the sound distortion produced by 4 different types of coating on magnetic tape. The ANOVA table for this data is given below, where X indicates a missing value. Source Degrees of Freedom Sum of Squares Mean Squares IF Treatment X 14.49 X Error X X X Total 49 165.69 Compute the F statistic. Give your answer to 2 decimal places.
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
The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…
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 theemployee'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

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