An analyst ran a regression for the last 60 months with the monthly returns for Procter and Gamble (PG) as the dependent variable and themonthly returns for the S&P 500 Index as the independent variable. The results are:Regression StatisticsMultiple R0.521185R Square0.271634Adjusted R Squa 0.259076Standard Error 0.04096Observations60ANOVARegressionResidualTotalInterceptX Variable 1dfSSMSFgnificance F1 0.03629 0.03629 21.63032 1.96E-0558 0.097309 0.00167859 0.133599Coefficientsandard Err t StatP-value Lower 95% Upper 95%ower 95.091 pper 95.090.003808 0.005288 0.72008 0.474368 -0.00678 0.014393 -0.00678 0.0143930.451966 0.097179 4.650841 1.96E-05 0.25744 0.646492 0.25744 0.646492Based on this analysis, the systematic variance of PG stock is0.520.270.040.260.45None of the above

It is given that the regression analysis is performed to predict the value of PG stocks as the…

Answered: An analyst ran a regression for the…

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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7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…
Suppose that a regression line is found to be ý = 0.5470x +7.7000 What is the predicted value for a = 7? O a. 11.53 O b. 54-45 C. 53-35 O d. -3.87
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Minitab output of this regression is reproduced below. Coefficients Term Constant GDP Price Model Summary S 0.299 Error Total Coef Analysis of Variance Source Regression -0.0861 0.7654 -0.0006 R-sq 0.98% DF • 2 7 9 SE Coef 0.5644 0.0574 0.0028 R-sq(adj) 0.98% Adj SS 33.4163 0.6277 34.0440 T-Value O a. 7 O b. 9 O c. 10 O d. Cannot be determined from the information given. -0.1520 13.3400 -0.1290 Adj MS 16.7082 0.0897 P-Value 0.8837 0.0001 0.8330 F-Value P-Value 186.325 0.049 Referring to the table above, how many observations were there in this study?
The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
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.
what is the regression equation for all predictors? I
For linear regression with one variable, the unpredicted portion of the Y-score variance (MS residual) has df = n - 2. True False Submit Answer
= 12.3 The Regression Equation X E Rounding Numbers Calculator X xyzHomework Assessment + → C A A https://www.xyzhomework.com 80% Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in 3- millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 21 4 22 18 6. 23 30 8 33 23 10 31 Ho: p = 0 Ha: p # 0 Find the Linear Correlation Coefficient r= Find the p-value p-value = The p-value is O Less than (or equal to) a O Greater than a The p-value leads to a decișion to Reject Ho O Accept Ho O Do Not Reject Ho signific voters p. The n The conclusion is There is a significant positive linear correlation between advertising expense and profit. O There is a significant negative linear correlation between advertising expense and profit. There ie n cianifiannt linaar corralation…
The beta of a stock has been estimated as 1.4 using regression analysis on a sample of historical returns. A commonly-used adjustment technique would provide an adjusted beta of A. 1.32. B. 1.13. C. 1.0. D. 1.27.
In the below residual plot, which assumption(s) of regression is/are violated? -4 Residuals -2 0 2 Residuals vs Fitted 00 8080 00 08 810 300 049 -8 -6 -4 -2 -2 0 2 4 6 Fitted values Equal variance across levels of X Normality Residuals are independent Linear relationship between X and Y
Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…
3. Regression analysis breaks scores on the DV into... (explain and give equations)

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