Star, Incorporated, used Excel to run a least-squs regression analysis, which resulted in the following output: Regression Statistics Multiple R R Square Observations Multiple Choice Intercept Production (X) How much of the variation in cost is not explained by production? O 4.83% 0.9755 0.9517 30 7.87% Coefficients Standard Error T Stat P-Value 175,003 11.57 2.84 0.021 12.55 0.000 It is impossible to determine with the data given.. 2,45% 61,603 0.9213
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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 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 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…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…
- In a study of the performance of a new engine design, the weight of 12 cars (in pounds) and the top speed (in mph) were recorded. A regression line was generated and shown to be an appropriate description of the relationship. The results of the regression analysis are below. Depend Variable: Top Speed Variable Constant Coefficient 107.58 s.e. of Coeff t-ratio prob Weight 0.8710 11.12 0.4146 9.67 0.000 2.10 0.062 R squared = 30.6% R squared (adjusted) = 23.7% s = 10.42 with 12-2 = 10 degrees of freedom Part A: Provide the regression equation based off the analysis provided & explain it in context. Part B: List the conditions for inference that need to be verified. Assuming these conditions have been met, does the data provide convincing evidence of a relationship between weight and top speed? Part C: Assuming all conditions for inference have been verified, determine a 95% confidence interval estimate for the slope of the regression line.Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 Using a = 0.05, Louis should ________________.In the picture, there is a summary of regression analysis output in the R program. Please comment on the Estimate, Std. Error, t value, Pr(>|t|), F-statistic , P value and Residual standard error.
- Plot the relationship between overhead costs and labor-hours. Draw the regression line and evaluate it using the criteria of economic plausibility, goodness of fit, and slope of the regression line.make a simple and multiple linear regression analysis based on the given dataA microcomputer manufacturer has developed a regression model relating his sales (y=$10,000s) with three independent variables. The three independent variables are price per unit(Price in $100s), advertising( ADV in $1000s) and the number of product lines (Lines). Part of the regression results is shown below. Coefficient Standard Error Intercept 1.0211 22.8752 Price(X1) -0.1524 0.1411 ADV (X2) 0.8849 0.2886 Lines(X3) -0.1463 1.5340 Source d.f. S.S. Regression 3 2708.61 Error 14 2840.51 Total 17 5549.12 What has been the sample size (n) for this analysis? Use the above results to find the estimated multiple…
- Find the degrees of freedom in a regression model with an intercept term that has 88 observations and 7 explanatory variables.A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.817 b=32.111 r²=0.877969 r=-0.937 Use this to predict the number of situps a person who watches 13.5 hour(s) of TV can do, and please round your answer to a whole number.Use the following table to calculate the simple linear regression to determine if the amount of time spent on homework can be predicted by amount of sleep. Graph the relationship and determine, numerically, if there are any outliers. Interpret all results in a paragraph citing the appropriate statistics. Show all work [edit] ID School Enrolled Months Enrolled Birthday Month Distance to Work (whole miles) Height (whole inches) Foot Size (whole inches) Hand Size (whole inches) Sleep (minutes) Homework (minutes) 1 Arts and Sciences 12 January 0 60 8 5 360 30 2 Applied Science and Technology 6 February 0 62 7 6 400 45 3 Business and Management 8 April 5 66 10 7 420 60 4 Nursing 10 June 10 68 12 8 440 15 5 Public Service 48 July 15 68 14 8 540 75 6 Arts and Sciences 48 June 30 70 12 9 480 120 7 Applied Science and Technology 36…
