Business Statistics: A First Course (8th Edition)
8th Edition
ISBN: 9780135177785
Author: David M. Levine, Kathryn A. Szabat, David F. Stephan
Publisher: PEARSON
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Based on the sample data and the regression line, complete the following.
The managers of an electric utility wish to examine the relationship between temperature and electricity use in the utility's service region during the summer months. In particular, the managers wish to be able to predict total electricity use for a day from the maximum temperature that day. The bivariate data below give the maximum temperature (in degrees Fahrenheit) and the electricity use (in thousands of kilowatt hours) of electricity generated and sold for a random sample of summer days. A best-fitting line for the data, obtained from least-squares regression, is given by =y+83.852.67x, in which x denotes the maximum temperature and y denotes the electricity use. This line is shown in the scatter plot below.
(a)For these data, values for electricity use that are greater than the mean of the values for electricity use tend to be paired with temperature values that are ▼(Choose one) the mean of…
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…
A regional retailer would like to determine if the variation in average monthly store sales can, in part, be explained by the size of the store measured in square feet. A random sample of 21 stores was selected and the store size and average monthly sales were computed. Complete parts a through c. Use a significance level of 0.10 where needed.
1 Click the icon to view the data table between the store size and average monthly sales.
Compute the simple linear regression model using the sample data to determine whether variation in average monthly sales can be explained by store size. What is the linear regression model based on the sample data?
y= +( )x(Type integers or decimals rounded to two decimal places as needed.)
Interpret the slope coefficient. Select the correct choice below and fill in the answer box to complete your choice. (Type an integer or decimal rounded to two decimal places as needed.)
For each additional square foot of store size,…
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- The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 +4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between 4.028✔ dollars and 4.753 x dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.arrow_forwardListed 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.)arrow_forwardListed 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…arrow_forward
- Use the given data to find the best predicted value of the response variable. Use a significance level of 0.05The regression equation relating attitude rating (x) and job performance rating (y) for the employees of a company is y=11.3+1.24x. Ten pairs of data were used to obtain the equation. The same data yield r=0.852 and y¯=80.14. What is the best predicted job performance rating for a person whose attitude rating is 72?arrow_forwardThe local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill = 15.9 + 4.45*Size Coefficients Estimate Std. Error (Intercept) 15.9 0.3 Size 4.45 0.57 We are 98% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size.arrow_forwardUse the given data to find the best predicted value of the response variable. Use a significance level of 0.05The regression equation relating attitude rating (x) and job performance rating (y) for the employees of a company is y= 11.5 + 1.04x. Ten pairs of data were used to obtain the equation. The same data yield r=0.863 and y¯=80.1 What is the best predicted job performance rating for a person whose attitude rating is 85? Round answer to one decimal place.arrow_forward
- 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 1.7 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) 7.2 119 7.4 157 9.9 9.3 8.8 8.4 253 202 204 194 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)arrow_forwardThe agronomist believed that the amount of rainfall as well as the amount of fertilizer used would affect the crop yield. She did the experiment in the following way. Thirty greenhouses were rented. In each, the amount of fertilizer and the amount of water were varied. At the end of the growing season, the amount of corn was recorded. Use ?=0.05 a) Determine the sample regression line and interpret the coefficients b) Do these data allow us to infer that there is a linear relationship between the amount of fertilizer and the crop yield? c) Do these data allow us to infer that there is a linear relationship between the amount of water and the crop yield? d) What can you say about the fit of the multiple regression model? e) Is it reasonable to believe that the error variable is normally distributed with constant variance (Support your answer)? PLEASE ONLY USE EXCEL AND SHOW ALL EXCEL COMMANDS! Yield Fetilizer Water 201 100 1000 325 200 1000 124 300 1000 129 400 1000 344 500…arrow_forwardTAMU admissions board believes the score you get on the SAT in high school can help predict your college GPA. Below is a regression model using the SAT scores and GPA for 83 college graduates. Calculate a 70% Confidence Interval for the slope of the regression line. Use 4 decimal places.(Note: The SAT scores have been divided by 100 just to make the numbers nicer) Simple linear regression results:Dependent Variable: GPAIndependent Variable: SAT/100GPA = 1.318967 + 0.174653 [SAT/100]Sample size: 83R (correlation coefficient) = 0.1487R-sq = 0.0221Estimate of error standard deviation: 0.118302Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 1.318967 0.3631 81 3.633 0.00049 Slope 0.174653 0.129086 81 1.353 0.1798 Use 4 decimal placesarrow_forward
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