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. P2,703,751.69

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. P2,703,751.69

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic 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
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 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 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.)
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 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.)
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 researcher wants to forecast the annual sales of Walmart, based on store size. To examine the relationship between the store size in square feet and its annual sales in million dollars, a sample of 14 stores was selected shown below in the image: Answer the following: i) Null hypothesis of correlation ii) Coefficient of Correlation and its interpretation iii) Interpret the sig value of ANOVA. iv) Coefficient of Determination and its interpretation v) Write down the Regression Model. vi) Interpret the value of ‘a’ vii) Interpret the value of ‘slope’
Now, the predictors flyer and display were added to the dataset and a multiple linear regression model was fitted. Part of the R output is shown below – Estimate Standard Error Intercept 81.23 35.24 Price -0.0318 0.023 Flyer 10.21 3.28 Display 21.67 13.27 Adj R Square = 78.8% Calculate a 95% confidence interval of Flyer and interpret the same in the context of the problem. Can we say that promoting a product through fliers significantly affect its sales? What is the coefficient of multiple determination of this model? Interpret its value.
. A researcher wants to forecast the annual sales of Walmart, based on store size. To examine the relationship between the store size in square feet and its annual sales in million dollars, a sample of 14 stores was selected shown below in the picture : Answer the following: i) Null hypothesis of correlation ii) Coefficient of Correlation and its interpretation iii) Interpret the sig value of ANOVA. iv) Coefficient of Determination and its interpretation v) Write down the Regression Model. vi) Interpret the value of ‘a’ vii) Interpret the value of ‘slope’
The article “Models for Assessing Hoisting Times of Tower Cranes” (A. Leung and C. Tam, Journal of Construction Engineering and Management, 1999: 385–391) presents a model constructed by a stepwise regression procedure to predict the time needed for a tower crane hoisting operation. Twenty variables were considered, and the stepwise procedure chose a nine-variable model. The adjusted R2 for the selected model was 0.73. True or false: a) The value 0.73 is a reliable measure of the goodness of fit of the selected model. b) The value 0.73 may exaggerate the goodness of fit of the model. c) A stepwise regression procedure selects only variables that are of some use in predicting the value of the dependent variable. d) It is possible for a variable that is of no use in predicting the value of a dependent variable to be part of a model selected by a stepwise regression procedure.
For a particular multiple linear regression analysis, there are ve predictor variables. A sample of size 37 is obtained on the five predictor variables and the response variable. It is found that SST = 1256 and SSE = 372.a. Construct the analysis of variance table for the analysis.b. Find R2 and interpret its value.c. Find the standard error of the estimate, se.d. At the 5% signicance level, do the data provide sufficient evidence to conclude that the ve predictor variables taken together are useful for predicting the response variable?e. State how useful you feel the regression equation is for making predictions about the response variable