Y- estimate = 29.7857- 0.7286 x
The slope indicates that as the price goes up by $1, the number of units sold goes down by 0.7286 units.
First option (.......)
x | y | SUMMARY OUTPUT | ||||||||||
34.0000 | 3.0000 | |||||||||||
36.0000 | 4.0000 | Regression Statistics | ||||||||||
32.0000 | 6.0000 | Multiple R | 0.9250 | |||||||||
35.0000 | 5.0000 | R Square | 0.8556 | |||||||||
30.0000 | 9.0000 | Adjusted R Square | 0.8267 | |||||||||
38.0000 | 2.0000 | Standard Error | 1.1199 | |||||||||
40.0000 | 1.0000 | Observations | 7.0000 | |||||||||
ANOVA | ||||||||||||
df | SS | MS | F | Significance F | ||||||||
Regression | 1.0000 | 37.1571 | 37.1571 | 29.6241 | 0.0028 | |||||||
Residual | 5.0000 | 6.2714 | 1.2543 | |||||||||
Total | 6.0000 | 43.4286 | ||||||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |||||
Intercept | 29.7857 | 4.7042 | 6.3318 | 0.0014 | 17.6933 | 41.8782 | 17.6933 | 41.8782 | ||||
x | -0.7286 | 0.1339 | -5.4428 | 0.0028 | -1.0727 | -0.3845 | -1.0727 | -0.3845 | ||||
F = 29.624> 16.26 , p-value = 0.0028 < alpha reject Ho, x and y related. t = -5.4428, p-value = 0.0028 < alpha reject Ho, x and y related |
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The following data represent the number of flash drives sold per day at a local computer...
The following data represent the number of flash drives sold per day at a local computer shop and their prices. Price (x) Units Sold (y) 34 36 32 35 30 38 40 4 Refer to Case 2 data use Excel, Data Analysis, Regression tools and develop a least-squares regression line and explain what the slope of the line indicates y-esimated 29.7857 0.7286x The slope indicates that as the price goes up by $1, the number of units sold goes up...
Problem 5- Simple Linear Regression The following data represent the number of flash drives sold per day at a local computer shop and their prices Price $34 36 32 35 30 Units Sold 6 40 A computer output is produced to examine this relationship further SUMMA RY OUTPUT Regression Statistics Multiple R RSquare Adjusted R Square Standard Error Observations 0.924982 0.855592 0.826711 1.119949 7 ANOVA MS gnificance F Regression Residual Total 137.15714 37.15714 29.62415 0.002842 5 б,271429 1.254286 6 43.42857...
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