What is the degree of the correlation between sales and display area in square feet?
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?8 The sales of 14 store branches and display area(in square feet) were obtained. The objective is to predict sales in terms of area of dispslay. Data were encoded into MS Excel. Output is shown below. REGRESSION STATISTICS Multiple R 0.954 R Square 0.910 Adjusted R square 0.902 Standard Error 936.85 Observations 14 Coefficients Standard t stat Error Intercept 901.25 513.02 1.76 Size 1.69 0.15 11.00 Question: Predict the sales if the display area utilized for a month is 3,000 square feet. Group of answer choices A. P5,971.25 B. P33,001.76 C. P2,703,751.69
- 8 The sales of 14 store branches and display area(in square feet) were obtained. The objective is to predict sales in terms of area of dispslay. Data were encoded into MS Excel. Output is shown below. REGRESSION STATISTICS Multiple R 0.954 R Square 0.910 Adjusted R square 0.902 Standard Error 936.85 Observations 14 Coefficients Standard t stat Error Intercept 901.25 513.02 1.76 Size 1.69 0.15 11.0 Predict the sales if the display area utilized for a month is 3,000 square feet. Group of answer choices A.P5,971.25 B. P33,001.76 C. P2,703,751.69A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below. ANOVA dfdf SSSS MSMS FF Significance FF Regression 1 19.52147562 19.52147562 19.03486740 0.002403282 Residual 8 8.204512367 1.02556405 Total 9 27.72598799 Step 2 of 3 : What is the adjusted coefficient of determination for this model, R2a? Round your answer to four decimal places.
- A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is _________ cars. (Type an integer or decimal rounded to one decimal place as needed.)The head width (in) and weight (lb) is measured for a random sample of 20 bears.The data shows that the mean head width is 6.9 inches, mean weight is 214.3 lb, and thecorrelation r = 0.879 and its p-value is less than 0.0001. The suggested linear regressionequation is WEIGHT = -212 + 61.9 WIDTH.(a) How is the best predicted weight value of a given head width found with this data found?(b) For the preceding part, why?(c) Find the best predicted weight given a bear with a head with of 6.5 inches.A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 1. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. 2. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234. 3. Do the sample data provide evidence that the model is useful for predicting average monthly sales for employees based on their sales experience using α=0.05? The test statistic is (Type an integer or decimal rounded to two decimal places as…
- A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The predicted sales is 8.6 cars. The coefficient of determination is 0.234 Test Statistic is 7.04 P-value=A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 The predicted sales is 8.6 cars. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is (Type an integer or decimal rounded to three decimal places as needed.)A study was done to compare tree height with trunk thickness. The following output was generated from the regression model.Simple linear regression results:Dependent Variable: Tree HeightIndependent Variable: Trunk SizeHeight = 39.041668 + 8.668677 TrunkSample size: 25R (correlation coefficient) = 0.4418R-sq = 0.1952Estimate of error standard deviation: 10.09977Parameter estimates: Parameter Estimate Std. Err. Intercept 39.041668 15.1736 Trunk 8.668677 3.670058 Assuming the conditions are met test if trunk size is a good predictor of tree height
