11.4.3 WP Consider the following computer output. The regression equation is Y = 12.9 + 2.34 x Predictor Constant Coef 12.857 Regression Residual error Total 2.3445 Analysis of Variance Source 1.032 X 0.1150 ? S = 1.48111 R-sq = 98.1% R-sq (adj) = 97.9% SE Coef DF 1 8 9 T ? SS 912.43 17.55 929.98 P ? ? MS F P 912.43 ? ? ? a. Fill in the missing information. You may use bounds for the P-values. b. Can you conclude that the model defines a useful linear relationship? c. What is your estimate of o²?
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- Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 132 7.4 170 9.8 268 9.4 224 8.9 225 8.4 209 Q The regression equation is y=-162+ (43.1)x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm, based on the regression equation, is -71.5 kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? OA. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample…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…Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.3 7.4 9.8 9.5 8.8 8.5 Weight (kg) 152 187 286 247 237 231 The regression equation is y =+ (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)
- Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05 Right arm - 102; 101; 94; 79; 80 Left arm - 177; 172; 143; 144; 143 The regression equation is y(carety)= ___+___x. Given that the systolic blood pressure in the right arm is 90mm Hg, the best predicted systolic blood pressure in the left arm is _____ mm Hg.Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.1 7.3 9.9 9.3 8.8 8.3 Weight (kg) 137 176 282 230 230 214 The regression equation is y=+x. (Round the constant to the nearest integers needed. Round the coefficient to one decimal place as needed.) The best-predicted weight for an overhead width of 1.8 cm, based on the regression equation, is: ____ kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct because a weight of zero does not…The average height of a large group of children is 43 inches, and the SD is 1.2inches. The average weight of these children is 40 pounds, and the SD is 2pounds. The correlation between the two variables is r = 0.65.A scatter diagram is drawn, with height on the horizontal axis and weight on thevertical axis. The scatter diagram is football shaped. The regression line forpredicting weight based on height is drawn through the scatter.(a) Predict the weights and the typical size of the error for those predictions ineach of the following case:A child who is 43 inches tall is predicted to weigh _____________ pounds, give ortake _____________ pounds.A child who is 41.8 inches tall is predicted to weigh ____________ pounds, give ortake _____________ pounds.
- A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: What is the population model of this regression problem? What is the sample estimates of the regression problem? Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer.The average height of a large group of children is 43 inches, and the SD is 1.2inches. The average weight of these children is 40 pounds, and the SD is 2pounds. The correlation between the two variables is r = 0.65.A scatter diagram is drawn, with height on the horizontal axis and weight on thevertical axis. The scatter diagram is football shaped. The regression line forpredicting weight based on height is drawn through the scatter.(a) Predict the weights and the typical size of the error for those predictions ineach of the following case:A child who is 43 inches tall is predicted to weigh _____________ pounds, give ortake _____________ pounds.A child who is 41.8 inches tall is predicted to weigh ____________ pounds, give ortake _____________ pounds. 37 pounds and is 41.8 inches tall. Relative to allchildren with the same height, this child’s weight is (pick one)(i) smaller than average(ii) about average(iii) larger than average(iv) impossible to determineShow your work and justify…The fish in my pond have mean lenth 14 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .8 pounds. The correlation coefficient of length and weight is .4. 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?
- An agribusiness performed a regression of wheat yield (bushels per acre) using observations on 21 test plots with four predictors (rainfall, fertilizer, soil acidity, hours of sun). The standard error was 1.02 bushels.The coefficient of determination of a set of data points is 0.842 and the slope of the regression line is −3.56. Determine the linear correlation coefficient of the data.Use the shoe print lengths and heights shown below to find the regression equation, letting shoe print lengths be the predictor (x) variable. Then find the best predicted height of a male who has a shoe print length of 28.5 cm. Would the result be helpful to police crime scene investigators in trying to describe the male? Use a significance level of α=0.05. Shoe Print (cm) 29.1 29.1 31.8 31.9 27.5 Foot Length (cm) 25.7 25.4 27.9 26.7 25.1 Height (cm) 175.4 177.8 185.2 175.4 173.2 The best predicted height is enter your response here cm. (Round to two decimal places as needed.) Would the result be helpful? A. No, because the description would be the same regardless of shoe print length. B. Yes, because the description would be based on an actual shoe print length. C. Yes, because the correlation is strong, so the predicted…