38.8 38.9 42. 43.4 6.9 6.4 7.8 9.1 8.3 8.8 7.7 48.1 49.2 51.8 62.5 69.9 79.6 80.1 9.8 7.7 7.6 11.5 11.2 11.7 10.8 regression model to the n = 27 observations on x = modulus inear regression model also resulted in a value of s = 0.9008. larger when x = 60 than when x = 40. x is from x, the smaller the value of sp. x is from y, the smaller the value of sp.
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- Jensen, Solberg, and Zorn investigated the relationship of insider ownership, debt, and dividend policies in companies. One of their findings was that firms with high insider ownership choose lower levels of both debt and dividends. Shown here is a sample of data of these 3 variables for 11 different industries. Use the data to develop the equation of the regression model to predict insider ownership by debt ratio and dividend payout. Insider Debt Dividend Industry Ownership Ratio Payout Mining 8.2 14.2 10.4 Food and Beverage 18.4 20.8 14.3 Furniture 11.8 18.6 12.1 Publishing 28.0 18.5 11.8 Petroleum refining 7.4 28.2 10.6 Glass and cement 15.4 24.7 12.6 Motor vehicle 15.7 15.6 12.6 18.4 21.7 7.2 Department store 13.4 23.0 11.3 Restaurant 18.1 46.7 4.1 Amusement 10.0 35.8 9.0 HospitalThe following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significantThe owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 33 18.154037618.1540376 6.051345876.05134587 17.6080562317.60805623 4.3383E-034.3383E-03 Residual 55 1.7183457921.718345792 0.343669160.34366916 Total 88 19.8723833919.87238339 Step 2 of 3 : What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places
- The summary output obtained from fitting the multiple regression are given below. Model Unstandardized Coefficients Standardized Sig. Coefficients B Std. Error Beta -3.512 (Constant) Education (years) -3019.226 859.789 .000 658.518 45.852 .581 14.362 .000 Gender -1615.440 253.239 -.249 -6.379 .000 Age (years) 45.008 10.469 .163 4.299 .000 Dependent Variable: Beginning Salary, Male=0 & Female=1. (a) Write down the estimated multiple regression model of the beginning salary on education, gender and age of employees of a company. (b) Interpret estimated regression coefficient values. (c) Find the predicted beginning salary for an employee who is 24 years old male and has 17 years of education.The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.17. Additional information was obtained from EXCEL. Conduct a test of hypothesis to determine if any of the regression coefficients do not equal 0 Use the 005 significance level. The regression equation is: Salary = 19.2 + 3.10 Years + 0.269 Perform - 0.704 Absent Intercept Years Perform Absent Coefficients Standard Error 19.186 3.096 0.269 -0.704 12.146 0.706 0.120 0.586 -1.202 t Stat 1.580 4.385 P-value 0.143 0.001 0.046 0.255 2.252 а. Но: H: Họ: H1: Họ: H1: b. The decision rules are to reject Ho if What is your decision? Interpret. c. 305 Multiple Regression and Correlation Analysis Chapter 14
- Imagine that you first estimate an OLS regression with a random sample of 100 observations and then re-estimate the same regression with 100 additional observations which are also randomly sampled. What would you expect to happen as the sample size increases? a. the explained sum of squares (SSE) increases b. Standard errors increase c. Sum of squared residuals decreases d. R-squared increasesx 5.7 4.1 6.2 4.4 6.5 5.8 4.9 y 1.9 4.8 0.8 3.9 1.2 1.7 3.0 (a) Computethecoefficientofdetermination. (b) Howmuchofthevariationintheoutcomevariableisexplainedbytheleast-squares regression line?After you estimate a simple linear regression you obtain the following sample regression function: Y₁ = 8.8 +0.7091 Xį With an r² of 0.784. The observed dependent variable used for the regression is: Y 9 675 40 40 40 6 5 5 5 5 1 1 Compute the sample variance of X? 8.45 10.79 9.17 7.19 Cannot be computed with the provided information.
- Life Expectancies A random sample of nonindustrialized countries was selected, and the life expectancy in years is listed for both men and women. Men 71.8 68.0 57.2 68.8 69.1 72.0 Women 65.2 67.3 45.0 66.3 68.1 60.2 The correlation coefficient for the data is r=0.829 and =α0.05. Should regression analysis be done? The correlation coefficient for the data is =r0.829 and =α0.05 . Should regression analysis be done? Find the equation of the regression line. Round the coefficients to at least three decimal places. y=′a+bx =a =b Find women's life expectancy in a country where men's life expectancy = 57 years. Round your answer to at least three decimal places. Women's life expectancy in years.19What is the least-squares regression line with the point (9,13) included in the data set? Data Set x y 3 6 4 5 5 7 7 6 8 9 8 8 10 8 11 9 11 7 12 10 13 12 13 10 14 11 This is a reading assessment question. ..... y hat = ______x + ______ Type integers or decimals rounded to 4 decimal places as needed