Need some help with Regression ... They are cut/pasted in order, separated by #'s. Thank you!

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\f(a) Develop a scatter diagram with the line speed as the independent variable. 25 25 20 20 15 15 Number of Defective Parts Number of Defective Parts 10 10 5 0 10 20 30 40 50 60 0 10 20 30 40 50 60 O Line Speed (feet per minute) Line Speed (feet per minute) 25 25 20 20 15 15 Number of Defective Parts Number of Defective Parts 10 10 5 5 0 10 20 30 40 50 60 0 10 20 30 40 50 60 O Line Speed (feet per minute) Line Speed (feet per minute) A(b) What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? 0 There appears to be no noticeable relationship between line speed (feet per minute) and the number of defective parts. 0 There appears to be a negative relationship between line speed (feet per minute) and the number of defective parts. 0 There appears to be a positive relationship between line speed (feet per minute) and the number of defective parts. (c) Use the least squares method to develop the estimated regression equation. j}: (d) Predict the number of defective parts found for a line speed of 25 feet per minute. : (a) Use a = 0.01 to test the hypotheses Hzl : s2 = 0 H3131 andfor 32 is not equal to zero for the model y = Bo + 31x1 + 3212 + s, where X1 2 television advertising {$1,000s) X2 = newspaper advertising {$1,0005). Find the value of the test statistic. {Round your answer to two decimal places.) E Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. O Reject Ho. There is insufficient evidence to conclude that there is a significant relationship among the variables. O Do not reject H . There is sufficient evidence to conclude that there is a significant relationship among the variables. O Reject Ho. There is sufficient evidence to conclude that there is a significant relationship among the variables. O Do not reject H . There is insufficient evidence to conclude that there is a significant relationship among the variables. (b) Use a = 0.05 to test the significance of B 1. State the null and alternative hypotheses. O Ho: B , # 0 H, : B , =0 O Ho: B1 = 0 OHO: B, 0 O Ho: B1 = 0 H,: B, 0Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. O Reject H . There is insufficient evidence to conclude that , is significant. O Reject Ho. There is sufficient evidence to conclude that , is significant. O Do not reject H. There is sufficient evidence to conclude that B, is significant. O Do not reject Ho. There is insufficient evidence to conclude that , is significant. Should x, be dropped from the model? O Yes O No95 5|] 1.5 9D 2.9 2.0 95 4.9 1.5 92 2.5 2.5 95 3.u 3.3 94 3.5 2.3 94 2.5 4.2 94 3.n 2.5 The owner then used multiple regression analysis to predict gross revenue (y), in thousands of dollars, as a function of television advertising (x1), in thousands of dollars, and newspaper advertising (x2), in thousands of dollars. 111e estimated regression equation was i : 83.2 + 2.29;:1 + 1.302(2. (a) What is the gross revenue (in dollars} expected for a week when $5,000 is spent on television advertising (x1 = 5) and $1,500 is spent on newspaper advertising (x2 = 1.5)? (Round your answer to the nearest dollar.) $:l (b) Provide a 95% confidence interval (in dollars) for the mean revenue of all weeks with the expenditures listed in part (a). (Round your answers to the nearest dollar.) $|:|t0$|:| (1:) Provide a 95% prediction interval (in dollars) for next week's revenue, assuming that the advertising expenditures will be allocated as in part (a). (Round your answers to the nearest dollar.) last\": A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is y = 81 + 4x. Salesperson Years of Annual Sales Experience ($1,000s) 1 80 2 3 97 3 4 92 4 4 107 5 6 103 6 8 111 7 10 119 8 10 128 9 11 117 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR : SSE =(b) Compute the coefficient of determination . (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. (c) What is the value of the sample correlation coefficient? (Round your answer to three decimal places.)You may need to use the appropriate technology to answer this question. Consider the data. Xi 2 6 9 13 20 9 18 10 25 25 (a) What is the value of the standard error of the estimate? (Round your answer to three decimal places.) (b) Test for a significant relationship by using the t test. Use a = 0.05. State the null and alternative hypotheses. OH: B, # 0 HA : B 1 = 0 OH: Bo = 0 Ha: Bo # 0 OH : B , = 0 H: B , +0 O Ho: Bo # 0 Ha: Bo = 0 OH: B1 20 Ha: B1 B2 H, : B. = P 2 O Ho: B1 0 O H : B , = 0 H: B , #0 Ho: B 1 20 O Ha: B, so O Ho: B1 0 H: B , >0 H : B , SO O Ho: B2 = 0 H . : B , + 0 O Ho: B2