(a) Calculate A, B, C, D and E. (b) At a = 0.05 test whether the regression model are fit.
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- can someone produce SPSS results by including the the intercept (a.k.a. the constant), standard error of estimate, unstandardized regression coefficient, the p-value for the unstandardized coefficient, R-square, Beta (i.e. standardized regression coefficient) using Excel with this given data? Overall: 3.4, 2.9, 2.6, 3.8, 3.0, 2.5, 3.9, 4.3, 3.8, 3.4, 2.8, 2.9, 4.1, 2.7, 3.9, 4.1, 4.2, 3.1, 4.1, 3.6, 4.3, 4.0, 2.1, 3.8, 2.7, 4.4, 3.1, 3.6, 3.9, 2.9, 3.7, 2.8, 3.3, 3.7, 4.2, 2.9, 3.9, 3.5, 3.8, 4.0, 3.1, 4.2, 2.9, 3.9, 3.5, 3.8, 4.0, 3.1, 4.2, 3.0, 4.8, 3.0, 4.4, 4.4, 3.4, 4.0, 3.5 Teach: 3.8, 2.8, 2.2, 3.5, 3.2, 2.7, 4.1, 4.2, 3.7, 3.7, 3.3, 3.3, 4.1, 3.1, 2.9, 4.5, 4.3, 3.7, 4.2, 4.0, 3.7, 4.0 2.9, 4.0, 3.3, 4.4, 3.4, 3.8, 3.7, 3.1, 3.8, 3.2, 3.5, 3.8, 4.4, 3.7, 4.00, 3.4, 3.2, 3.8, 3.7, 4.3, 3.4, 4.0 3.1, 4.5, 4.8, 3.4, 4.2, 3.4 Exam: 3.8 3.2, 1.9, 3.5, 2.8, 3.8, 3.8, 4.1, 3.6, 3.6, 3.5, 3.3, 3.6, 3.8, 3.8, 4.2, 4.1, 4.0, 4.3, 4.2, 4.0, 4.1, 2.7, 4.4, 4.4, 4.3, 3.6, 4.1, 4.2, 3.6,…The accompanying table lists overhead widths (cm) of seals measured from photographs and the weights (kg) of the seals. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for an overhead width of 9.2 cm using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Click the icon to view the seal data. a. The explained variation is (Round to the nearest integer as needed.) b. The unexplained variation is (Round to the nearest integer as needed.) c. The 99% prediction interval for an overhead width of 9.2 cm is ☐ kgThe accompanying table lists overhead widths (cm) of seals measured from photographs and the weights (kg) of the seals. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for an overhead width of 8.9 cm using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Click the icon to view the seal data. a. The explained variation is (Round to the nearest integer as needed.) b. The unexplained variation is. (Round to the nearest integer as needed.) c. The 99% prediction interval for an overhead width of 8.9 cm is kgWhat is the differed annual expenditures of two families if their annual net incomes are differed by 2000? The computed regression line has a value of a=4.32 and b=2.12.The fish in my pond have mean length of 10.78 inches and mean weight 4.27 pounds. The correlation coefficient of length and weight is .63. If the length of a particular randomly selected fish is reported to be 10.78 inches, then what should we predict for the weight of that fish using simple linear regression?Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…The fish in my pond have mean length 10 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .6 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 15 inches, then what should we predict for the weight of that fish using simple linear regression?A professor obtains SAT scores and freshman grade point averages (GPAs) for a group of n = 15 college students. The SAT scores have a mean of M = 580 with SS = 22,400, and the GPAs have a mean of 3.10 with SS = 1.26, and SP = 84. Find the regression equation for predicting GPA from SAT scores. What percentage of the variance in GPAs is accounted for by the regression equation? (Compute the correlation r, then find r^2.) Does the regression equation account for a significant portion of the variance in GPA? Use α = .05 to evaluate the F-ratio.The number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature (in °F) for that month. The past year's usage and temperatures are in the following table: (a) Find a regression relating steam usage to temperature. (b) Test for significance of regression using a=0.05. (c) Find a 99% confidence interval for B1 Usage/ 1000 Usage/ 1000 Month Temp. Month Temp. Jan. 21 185.79 July. 68 621.55 Feb. 24 214.47 Aug. 74 675.06 Mar. 32 288.03 Sept. 62 562.03 Apr.o 50 452.93 47 424.84 Oct. May 50 454.58 Nov. 41 369.95 June 59 539.03 Dec. 30 a 273.98The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 90% confidence level with a redshift of 0.0126. Find the explained variation.A regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X1 = median age, X2 = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 45 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p- values to 4 decimal places.) Predictor Intercept AgeMed Coefficient SE tcalc p-value 4,641.0430 798.0634 -28.8630 12.4684 Bankrupt 20.1604 12.1079 FedSpend HSGrad% -0.0181 0.0181 -30.3196 7.1136 (b-1) What is the critical value of Student's tin Appendix D for a two-tailed test at a = .01? (Round your answer to 3 decimal places.) -value =A regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X₁ = median age, X₂ = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 50 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 4 decimal places.) Predictor Intercept AgeMed Bankrupt FedSpend HSGrad% Coefficient t-value = 4,198.5808 -27.3540 17.4893 -0.0124 -29.0314 SE 799.3395 12.5687 12.4033 0.0176 7.1268 tcalc p-value (b-1) What is the critical value of Student's t in Appendix D for a two-tailed test at a = .01? 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