Given are five observations for two variables, x and y. 15 14 16 19 Yi 56 55 48 17 13 The estimated regression equation for these data is ŷ = 63.98 –- 2.38a. a. Compute SSE, SST, and SSR. SSE (to 2 decimals) SST (to 2 decimals) SSR (to 2 decimals) b. Compute the coefficient of determination p2. Comment on the goodness of fit. (to 3 decimals) The least squares line provided an Select your answer - fit; % of the variability in y has been explained by the estimated regression equation (to 1 decimal). c. Compute the sample correlation coefficient. Enter negative value as negative number. (to 3 decimals)
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.Given are five observations for two variables, x and y.x 3 12 6 20 14y 65 50 65 15 18a. Develop a scatter diagram for these data.b. Compute correlation between x and y variable.c. Develop the estimated regression equation by computing b0 and b1Given are data for two variables, x and y. xi 6 11 15 18 20 yi 5 9 13 19 30 An estimated regression equation for these data is yhat=-7.24+1.6x a) Compute the residuals. (Round your answers to two decimal places.) b) Compute the standardized residuals. (Round your answers to two decimal places.) Ans both..otherwise don't answer
- Given are five observations for two variables, x and y.x 3 12 6 20 14y 65 50 65 15 18a. Develop a scatter diagram for these data.b. Compute correlation between x and y variable.c. Develop the estimated regression equation by computing b0 and bConsider the following data for two variables, x and y. x 22 24 26 30 35 40 y 11 21 34 36 39 36 Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2. (Round b0 to one decimal place and b1 to two decimal places and b2 to four decimal places.) ŷ = (e) Use the results from part (d) to test for a significant relationship between x, x2, and y. Use ? = 0.05. Is the relationship between x, x2, and y significant? Find 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 = (f) Use the model from part (d) to predict the value of y when x = 25. (Round your answer to three decimal places.)A statistical program is recommended. Consider the following data for two variables, x and y. x 22 24 26 30 35 40 y 12 22 34 36 41 37 (d) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2. (Round b0 to one decimal place and b1 to two decimal places and b2 to four decimal places.) ŷ = −175+12.63x−.1837x2 Use the results from part (d) to test for a significant relationship between x, x2, and y. Use α = 0.05. Is the relationship between x, x2, and y significant? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.)
- Given are five observations for two variables, x and y. xi 1 2 3 4 5 yi 4 6 6 11 13 Develop the estimated regression equation by computing the values of b0 and b1 using b1 = Σ(xi − x)(yi − y) Σ(xi − x)2 and b0 = y − b1x. ŷ = (e) Use the estimated regression equation to predict the value of y when x = 2.1. Consider the following data for two variables, x and y.x 22 24 26 30 35 40y 12 21 33 35 40 36a. Develop an estimated regression equation for the data of the form yˆ = b0 + b1x.b. Use the results from part (a) to test for a significant relationship between x and y.Use a = .05.c. Develop a scatter diagram for the data. Does the scatter diagram suggest an estimatedregression equation of the form yˆ = b0 + b1x + b2x2? Explain.d. Develop an estimated regression equation for the data of the form yˆ = b0 + b1x +b2x2.e. Refer to part (d). Is the relationship between x, x2, and y significant? Use a = .05.f. Predict the value of y when x = 25.Consider the following data for two variables, x and y. x 9 32 18 15 26 y 9 19 20 15 22 Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2. (Round b0 to two decimal places and b1 to three decimal places and b2 to four decimal places.) ŷ = (c) Use the model from part (b) to predict the value of y when x = 20. (Round your answer to two decimal places.)
- Consider the data. xi 3 12 6 20 14 yi 55 35 60 15 25 The estimated regression equation for these data is ŷ = 68.25 − 2.75x. (a) Compute SSE, SST, and SSR using equations SSE = Σ(yi − ŷi)2, SST = Σ(yi − y)2, and SSR = Σ(ŷi − y)2. SSE=SST=SSR= (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) 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. 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. 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 provided a good fit as a large proportion of the variability in y has…Given are five observations for two variables, x and y. xi 3 12 6 20 14 yi 55 45 50 15 20 #1) Develop the estimated regression equation by computing the values of b0 and b1 using b1 = Σ(xi − x)(yi − y) Σ(xi − x)2 and b0 = y − b1x. y= #2) Use the estimated regression equation to predict the value of y when x = 13.Consider the data. xi 3 12 6 20 14 yi 65 40 60 10 20 The estimated regression equation for these data is ŷ = 77.5 − 3.5x. (a) Compute SSE, SST, and SSR using equations SSE = Σ(yi − ŷi)2, SST = Σ(yi − y)2, and SSR = Σ(ŷi − y)2. SSE = SST = SSR = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = 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. 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…