b. Write the estimated regression equation (to 2 decimals if necessary).ŷ =+c. Use the t statistic to test the significance of the relationship at a 0.05 level of significance.What is the p-value? Use Table 2 of Appendix B.p-value is - Select your answerWhat is your conclusion?Select your answerd. Use the F statistic to test the significance of the relationship at a 0.05 level of significance.Compute the F test statistic (to 2 decimals).69What is the p-value? Use Table 4 of Appendix B.p-value isSelect your answer -What is your conclusion?- Select your answer -e. Predict the selling price of an apartment building with gross annual rents of $60,000 (to 1 decimal).$thousands.Icon KeySTExercise 14.40 Algo (Excel's Regression Tool)Q SearchLEGIONLiveW 5.6.The commercial division of a real estate firm is conducting a regression analysis of the relationship between , annual gross rents (in thousands of dollars), and y, sellingprice (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained.InterceptAnnual GrossRentsa. How many apartment buildings were in the sample?ANOVAWhat is the p-value? Use Table 4 of Appendix B.RegressionResidualTotaln-value is - Select your answerQ Searchdf178Coefficients20.0007.280b. Write the estimated regression equation (to 2 decimals if necessary).ŷ =+c. Use the t statistic to test the significance of the relationship at a 0.05 level of significance.What is the p-value? Use Table 2 of Appendix B.p-value isSelect your answer -SS41587.1What is your conclusion?Select your answer -d. Use the F statistic to test the significance of the relationship at a 0.05 level of significance.Compute the F test statistic (to 2 decimals).LEGION51984.1LiveStandard Error3.22131.3621MS FWSignificance Ft Stat6.215.29P-value

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The commercial division of a real estate firm is conducting a regression analysis of the relationship between 2, annual gross rents (in thousands of dollars), and y, selli price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Intercept Annual Gross Rents a. How many apartment buildings were in the sample? x What is the p-value? Use Table 4 of Appendix B. n-value is ANOVA Regression Residual Total Select your answer Q Search df 1 7 8 b. Write the estimated regression equation (to 2 decimals if necessary). ŷ = c. Use the t statistic to test the significance of the relationship at a 0.05 level of significance. What is the p-value? Use Table 2 of Appendix B. p-value is Select your answer - What is your conclusion? Coefficients 20.000 7.280 Select your answer - d. Use the F statistic to test the significance of the relationship at a 0.05 level of significance. Compute the F test statistic (to 2 decimals). SS 41587.1 51984.1 LEGION Standard Error 3.2213 1.3621 Live MS F Significance F t Stat 6.21 5.29 P-value

The commercial division of a real estate firm is conducting a regression analysis of the relationship between 2, annual gross rents (in thousands of dollars), and y, selli price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Intercept Annual Gross Rents a. How many apartment buildings were in the sample? x What is the p-value? Use Table 4 of Appendix B. n-value is ANOVA Regression Residual Total Select your answer Q Search df 1 7 8 b. Write the estimated regression equation (to 2 decimals if necessary). ŷ = c. Use the t statistic to test the significance of the relationship at a 0.05 level of significance. What is the p-value? Use Table 2 of Appendix B. p-value is Select your answer - What is your conclusion? Coefficients 20.000 7.280 Select your answer - d. Use the F statistic to test the significance of the relationship at a 0.05 level of significance. Compute the F test statistic (to 2 decimals). SS 41587.1 51984.1 LEGION Standard Error 3.2213 1.3621 Live MS F Significance F t Stat 6.21 5.29 P-value

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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Let price denote a price index for the goods sold by a restaurant, advert the amount spent on advertising, sales the sales for the restaurant, and consider the following two regressions First regression: sales = B1 + B2price + B3price? + B4advert + ßsadvert? + e, Second regression: sales = B1 + B2price + B3price? + e We estimate both regressions using a sample of 105 observations. The sum of square residuals (E ê) from the first regression equals 50, while the sum of square Li=1 residuals from the second regression equals 70. Suppose we are interested in testing the null hypothesis that expected sales do not depend on advertising. What is the F- statistic for this null hypothesis? Recall the F-statistic is given by ((SSER - SSEU)/J)/(SSEy/(n – K)). O a. -15 O b. 42 Oc. 21 O d. 20 O e. All other options are incorrect.
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 95 mm Hg. Use a significance level of 0.05 Right Arm 103 102 94 79 80 D Left Arm 176 170 144 146 145 : Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y= +x (Round to one decimal place as needed.) ater/ Enter your ahswer in the edit fields and then click Check Answer
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.8 277.9 253.3 259.2 279.0 258.0 274.0 262.4 Height 1784.7 1771.2 1676.0 1645.9 1858.9 1710.1 1788.9 1737.0 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.8 mm is enter your response heremm. (Round to the nearest integer as needed.)
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32
For linear regression with one variable, the unpredicted portion of the Y-score variance (MS residual) has df = n - 2. True False Submit Answer
The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance. Copy-paste the data and run the analysis using MS Excel. starting salary GPA Years of experience Civil Service Ratings 15000 80.1 1 79.5 15000 81.2 1 78.0 15500 81.3 2 79.0 16000 82.4 3 80.0 16200 83.4 3 85.0 17500 87.9 4 89.9 18000 90.3 5 89.1 16300 84.2 3 84.1 17000 87.0 4 89.0 17900 88.1 5 89.2 What is the computed R square of the resulting multiple linear regression and its interpretation?
The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…
Personal wealth tends to increase with age as older individuals have had more opportunitiesto earn and invest than younger individuals. The following data were obtained from a randomsample of eight individuals and records their total wealth (Y) and their current age (X). See attachment for table with figures State the estimated regression line and interpret the slope coefficient.b. What is the estimated total personal wealth when a person is 50 years old?c. What is the value of the coefficient of determination? Interpret it.d. Test whether there is a significant relationship between wealth and age at the 10%significance level. Perform the test using the following six steps.Step 1. Statement of the hypotheses Step 2. Standardised test statistic Step 3. Level of significance Step 4. Decision Rule
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Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 103 102 96 76 76 Left Arm 174 167 149 148 148
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspace