Dairy sales Peninsula Creameries sells both cottage cheese and ice cream. The CEO recently noticed that in months when the company sells more cottage cheese, it seems to sell more ice cream as well. Two of his aides were assigned to test whether this is true or not. The first aide’s plot and analysis of sales data for the past 12 months (in millions of pounds for cottage cheese and for ice cream) appear below. Dependent variable is Ice cream R-squared = 36.9% s = 8.320 with 12 - 2 = 10 degrees of freedom The other aide looked at the differences in sales of ice cream and cottage cheese for each month and created the following output: Cottage Cheese Ice Cream Count 12 Mean 11.8000 Median 15.3500 StdDev 7.99386 IntQRange 14.3000 25th %tile 3.20000 75th %tile 17.5000 Test H 0 : μ (CC - IC) = 0 vs H a : μ (CC – IC) ≠ 0 Sample Mean = 11.800000 t-Statistic = 5.113 w>11 df Prob = 0.0003 Lower 95% bound = 6.7209429 Upper 95% bound = 16.879057 a) Which analysis would you use to answer the CEO’s question? Why? b) What would you tell the CEO? c) Which analysis would you use to test whether the company sells more cottage cheese or ice cream in a typical year? Why? d) What would you tell the CEO about this other result? e) What assumptions are you making in the analysis you chose in part a? What assumptions are you making in the analysis in part c? f) Next month’s cottage cheese sales are 82 million pounds. Ice cream sales are not yet available. How much ice cream do you predict Peninsula Creameries will sell? g) Give a 95% confidence interval for the true slope of the regression equation of ice cream sales by cottage cheese sales. h) Explain what your interval means.
Dairy sales Peninsula Creameries sells both cottage cheese and ice cream. The CEO recently noticed that in months when the company sells more cottage cheese, it seems to sell more ice cream as well. Two of his aides were assigned to test whether this is true or not. The first aide’s plot and analysis of sales data for the past 12 months (in millions of pounds for cottage cheese and for ice cream) appear below. Dependent variable is Ice cream R-squared = 36.9% s = 8.320 with 12 - 2 = 10 degrees of freedom The other aide looked at the differences in sales of ice cream and cottage cheese for each month and created the following output: Cottage Cheese Ice Cream Count 12 Mean 11.8000 Median 15.3500 StdDev 7.99386 IntQRange 14.3000 25th %tile 3.20000 75th %tile 17.5000 Test H 0 : μ (CC - IC) = 0 vs H a : μ (CC – IC) ≠ 0 Sample Mean = 11.800000 t-Statistic = 5.113 w>11 df Prob = 0.0003 Lower 95% bound = 6.7209429 Upper 95% bound = 16.879057 a) Which analysis would you use to answer the CEO’s question? Why? b) What would you tell the CEO? c) Which analysis would you use to test whether the company sells more cottage cheese or ice cream in a typical year? Why? d) What would you tell the CEO about this other result? e) What assumptions are you making in the analysis you chose in part a? What assumptions are you making in the analysis in part c? f) Next month’s cottage cheese sales are 82 million pounds. Ice cream sales are not yet available. How much ice cream do you predict Peninsula Creameries will sell? g) Give a 95% confidence interval for the true slope of the regression equation of ice cream sales by cottage cheese sales. h) Explain what your interval means.
Solution Summary: The author explains the appropriate analysis for the CEO's question. The scatter plot, regression analysis output, histogram of difference, and descriptive statistics are given.
Dairy sales Peninsula Creameries sells both cottage cheese and ice cream. The CEO recently noticed that in months when the company sells more cottage cheese, it seems to sell more ice cream as well. Two of his aides were assigned to test whether this is true or not. The first aide’s plot and analysis of sales data for the past 12 months (in millions of pounds for cottage cheese and for ice cream) appear below.
Dependent variable is Ice cream
R-squared = 36.9%
s = 8.320 with 12 - 2 = 10 degrees of freedom
The other aide looked at the differences in sales of ice cream and cottage cheese for each month and created the following output:
Cottage Cheese
Ice Cream
Count
12
Mean
11.8000
Median
15.3500
StdDev
7.99386
IntQRange
14.3000
25th %tile
3.20000
75th %tile
17.5000
Test H0: μ(CC - IC) = 0 vs Ha: μ(CC – IC) ≠ 0
Sample Mean = 11.800000 t-Statistic = 5.113 w>11 df
Prob = 0.0003
Lower 95% bound = 6.7209429
Upper 95% bound = 16.879057
a) Which analysis would you use to answer the CEO’s question? Why?
b) What would you tell the CEO?
c) Which analysis would you use to test whether the company sells more cottage cheese or ice cream in a typical year? Why?
d) What would you tell the CEO about this other result?
e) What assumptions are you making in the analysis you chose in part a? What assumptions are you making in the analysis in part c?
f) Next month’s cottage cheese sales are 82 million pounds. Ice cream sales are not yet available. How much ice cream do you predict Peninsula Creameries will sell?
g) Give a 95% confidence interval for the true slope of the regression equation of ice cream sales by cottage cheese sales.
h) Explain what your interval means.
Statistics that help describe, summarize, and present information extracted from data. Descriptive statistics include concepts related to measures of central tendency, measures of variability, measures of frequency, shape of distribution, and some data visualization techniques/tools such as pivot tables, charts, and graphs.
Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website)
Click on the datafile logo to reference the data.
DATA file
a. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house:
SUMMARY OUTPUT
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression statistics
Regression
Residual
Total
Multiple R
R Square
ANOVA
Adjusted R Square
Standard Error
Observations
0.7429
0.5519
0.4907
61948.6931
Regression statistics
Regression
Residual
Total
df
Intercept
Sq Ft
Beds
Lower 95%
0.9353 -145129.5298
Intercept
Baths
0.9528 -49383.5243
0.0180
Sq Ft
Beds
0.0326
Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R is greater than 45%, the…
Draw a scatter diagram with square feet of living space as the independent variable and selling price as the dependent variable and describe variable and describe the relationship between the size of a house and the selling price.
Chapter R Solutions
Intro Stats, Books a la Carte Edition (5th Edition)
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