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MINITAB PRINTOUT Regression Analysis: SALES PER STORE versus DAILY HIGH TEMPE Predictor Constant DAILY HIGH TEMPERATURE (X) The regression equation is SALES PER STORE (Y) = 812 + 436 DAILY HIGH TEMPERATURE (X) 3= 2930.58 R-3q= 79.9% Analysis of Variance Accessibilite Unavailable 76 DE 436.32 Ses SE Seat 812 95 39800 R-9q (adj) = 78.80 Source Regression 648374033 163177395 Residual Error 19 Total 20 811551429 Pearson correlation of SALES PER STORE (Y) and DAILY HIGH TEMPERATURE (X) =0.894 P 4129 0.20 0.846 50.22 8.69 0.000 M3 F P 648374033 75.50 0.000 8588284

MINITAB PRINTOUT Regression Analysis: SALES PER STORE versus DAILY HIGH TEMPE Predictor Constant DAILY HIGH TEMPERATURE (X) The regression equation is SALES PER STORE (Y) = 812 + 436 DAILY HIGH TEMPERATURE (X) 3= 2930.58 R-3q= 79.9% Analysis of Variance Accessibilite Unavailable 76 DE 436.32 Ses SE Seat 812 95 39800 R-9q (adj) = 78.80 Source Regression 648374033 163177395 Residual Error 19 Total 20 811551429 Pearson correlation of SALES PER STORE (Y) and DAILY HIGH TEMPERATURE (X) =0.894 P 4129 0.20 0.846 50.22 8.69 0.000 M3 F P 648374033 75.50 0.000 8588284

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter3: Straight Lines And Linear Functions
Section3.CR: Chapter Review Exercises
Problem 16CR: XYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ...
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1. What is the linear regression equation on the Minitab printout?

 

 

2. What is the dependent variable?

 

 

3. What is the independent variable?

 

 4. What is the coefficient of correlation? Is the relationship strong, moderate, or weak?

 5.What percentage of the variation in sales can be explained by the variation in temperature?

 

6. What is the slope and what does it mean in terms of this problem?

 

7. What is the y intercept and what does it mean in terms of this problem?

 

8. Conduct a hypothesis test to determine if there is positive correlation at the .05 significance level. Show all 6 steps.

REGRESSION ANALYSIS The owner of large chain of ice-cream stores would like to study the effect of atmospheric temperature on sales during the summer season. Temperature is the independent variable. A random sample of 21 days is selected with the results given as follows: DAY 1 2 3 4 5 6 7 8 9 10 11 12 Source Regression Residual Error Total 13 14 15 16 17 18 19 20 21 Accessibility Unavailable DAILY HIGH TEMPERATURE (X) Predictor Constant DAILY HIGH TEMPERATURE (X) 3 2930.58 R-3g = 79.9% Analysis of Variance 48 60 63 75 80 82 85 88 DF 1 648374033 163177395 20 811551429 90 91 92 65 9.5 98 100 92 87 84 88 80 82 76 MINITAB PRINTOUT Regression Analysis: SALES PER STORE versus DAILY HIGH TEMPE The regression equation is SALES PER STORE (Y) = 812 + 436 DAILY HIGH TEMPERATURE (X) 436.32 Sest SE Seat 812 R-9q (adj) = 78.88 SALES PER STORE (Y) 25000 28000 28500 30500 33600 32500 36800 39000 MS 41000 43600 44000 29200 46500 48000 38700 38300 35800 38600 32600 31400 39800 T P 4129 0.20 0.846 50.22 8.69 0.000 F P 648374033 75.50 0.000 8588284 Pearson correlation of SALES PER STORE (Y) and DAILY HIGH TEMPERATURE (X) = 0.894
Transcribed Image Text:REGRESSION ANALYSIS The owner of large chain of ice-cream stores would like to study the effect of atmospheric temperature on sales during the summer season. Temperature is the independent variable. A random sample of 21 days is selected with the results given as follows: DAY 1 2 3 4 5 6 7 8 9 10 11 12 Source Regression Residual Error Total 13 14 15 16 17 18 19 20 21 Accessibility Unavailable DAILY HIGH TEMPERATURE (X) Predictor Constant DAILY HIGH TEMPERATURE (X) 3 2930.58 R-3g = 79.9% Analysis of Variance 48 60 63 75 80 82 85 88 DF 1 648374033 163177395 20 811551429 90 91 92 65 9.5 98 100 92 87 84 88 80 82 76 MINITAB PRINTOUT Regression Analysis: SALES PER STORE versus DAILY HIGH TEMPE The regression equation is SALES PER STORE (Y) = 812 + 436 DAILY HIGH TEMPERATURE (X) 436.32 Sest SE Seat 812 R-9q (adj) = 78.88 SALES PER STORE (Y) 25000 28000 28500 30500 33600 32500 36800 39000 MS 41000 43600 44000 29200 46500 48000 38700 38300 35800 38600 32600 31400 39800 T P 4129 0.20 0.846 50.22 8.69 0.000 F P 648374033 75.50 0.000 8588284 Pearson correlation of SALES PER STORE (Y) and DAILY HIGH TEMPERATURE (X) = 0.894
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