Given the following data set, let x be the explanatory variable and y be the response variable. x6137523 y4983587 (a) If a least squares line was fitted to this data, what percentage of the variation in the Y would be explained by the regression line? (Enter your answer as a percent.) ANSWER: % (b) Compute the correlation coefficient: r =
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- The following data are the monthly salaries and the grade point averages for students who obtained a bachelor's degree in mathematics. GPA: 2.6, 3.4, 3.6, 3.2, 3.5, and 2.9; Monthly Salary: 330o, 3600, 4000, 3500, 3900, and 3600. Plot the scatter diagram showing the deviations about the estimated regression line and the line y = y(bar).The regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significantIn a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.79 -0.4 -0.79 0.4
- The following data represent the number of flash drives sold per day at a localcomputer shop and their prices.Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope.b. Did the estimated regression equation provide a good fit? Explain.c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold.d. How many units can be sold per day if the price of flash drive is set to $28.5. For the following set of data: Y 1 10 5 4 4 13 a. Find the regression equation for predicting Y from X. b. Does the regression equation account for a significant portion of the variance in the Y scores? Use a = .05 to evaluate the F-ratio %3D X27 33Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspace
- Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedomThe following table contains ACT scores and the GPA for eight college students. Estimate the relationship between GPA (yi) and ACT (x;) using OLS regression by hand. Report the intercept and slope estimates: Student 1 2 3 4 5 6 7 8 GPA 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 ŷ₁ =B₁ + B₁x₁ ACT 21 24 26 27 29 25 25 30 y X ŷ₁An econometric model is a multiple linear regression model if Question 10Select one: a. it explains the average value of y as a linear function of several explanatory variables b. it explains the sample mean of y as a function, linear in the parameters , of several x c. it explains y as a linear function of several x , the explanatory variables d. it explains the average of y as a function of several x e. none of the answers is correct
- write the regression code for the following data based on the below instruction, in STATA. hint: occupation=a categorical variable equal to ”1” if a person’s occupation is ”management”; ”2” if it is ”sales”; ”3” if it is ”clerical”, ”4” if it is ”service”, ”5” if it is ”professional” and ”6” if it is ”other”. Please make sure to use ”other” as the base category;Given the following data set, let x be the explanatory variable and y be the response variable. x75 1 8812 y36102398 (a) If a least squares line was fitted to this data, what percentage of the variation in the y would be explained by the regression line? (Enter your answer as a percent.) ANSWER: % (b) Compute the correlation coefficient: r =Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sg ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract: The regression equation is Price = B + ÞBedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 s= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF MS F P Regression Residual 3 76501718347 25500572782 **** Error 96 60109046053 626135896 Total 99 1. Fill in the missing values *, **, and **** 2. Use the p-value approach to determine if o is significant at the 5% significance level
