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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- If a regression model of the form y = B,+B,x, +... +B,x, is fit to 131 observations on each variable and yields an R´value of 0.48, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Degrees of freedom Source of Sums of Mean F statistic variation squares squares Regression Error 43 TotalTrying to find the estimated Bs from the regression.In an experiment to determine the relationship between force on a wire and resulting extension, the following data was obtained: Force (N) 10 20 30 40 50 60 70 Extension (mm) 0.22 0.40 0.61 0.85 1.20 1.45 1.7 Determine the most appropriate type of regression line for this data, explaining how you arrive at you answer. Determine of the coefficient of correlation for this data. Comment on the considered accuracy values using this method.
- 1. Write down the multiple regression function?2. What is the multiple correlation and the multiple standard error of estimate?3. What is the coefficient of multiple determination and what does it mean?4. Of the three independent variables, which independent variable is not significant as a predictor (α = 0.05)?magine 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. R-squared increases b. t-statistic decreases c. Sum of squared residuals decreases d. the standard error gets smaller4.3 Consider the simple linear regression model fit to the solar energy data. DATA SETS FOR EXERCISES 555 TABLE B.2 Solar Thermal Energy Test Data y x₁ X₂ X3 X4 Xs 271.8 783.35 33.53 40.55 16.66 13.20 264.0 748.45 36.50 36.19 16.46 14.11 238.8 684.45 34.66 37.31 17.66 15.68 230.7 827.80 33.13 32.52 17.50 10.53 251.6 860.45 35.75 33.71 16.40 11.00 257.9 875.15 34.46 34.14 16.28 11.31 263.9 909.45 34.60 34.85 16.06 11.96 266.5 905.55 35.38 35.89 15.93 12.58 229.1 756.00 35.85 33.53 16.60 10.66 239.3 769.35 35.68 33.79 16.41 10.85 258.0 793.50 35.35 34.72 16.17 11.41 257.6 801.65 35.04 35.22 15.92 11.91 267.3 819.65 34.07 36.50 16.04 12.85 267.0 808.55 32.20 37.60 16.19 13.58 259.6 774.95 34.32 37.89 16.62 14.21 240.4 711.85 31.08 37.71 17.37 15.56 227.2 694.85 35.73 37.00 18.12 15.83 196.0 638.10 34.11 36.76 18.53 16.41 278.7 774.55 34.79 34.62 15.54 13.10 272.3 757.90 35.77 35.40 15.70 13.63 267.4 753.35 36.44 35.96 16.45 14.51 254.5 704.70 37.82 36.26 17.62 15.38 224.7 666.80 35.07…
- 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 HospitalSuppose 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. -3.87The 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 significant
- The 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 placesCan you contruct a 95% confidence interval for the Y intercept for the regression equation. Then explain what do you conclude from this regression?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.