In: Statistics and Probability
Populate an excel sheet with the information, below. After you have installed the Analysis ToolPak, open “Data Analysis” and scroll down to “Regression” and click. The regression window will open. Run a regression analysis on the annual car sales, over the last three years, taken from William and Sam Automotive sales. Year Car Sales 2015 200 2016 250 2017 270.
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.970725343 | |||||||
R Square | 0.942307692 | |||||||
Adjusted R Square | 0.884615385 | |||||||
Standard Error | 12.24744871 | |||||||
Observations | 3 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 1 | 2450 | 2450 | 16.33333333 | 0.154420958 | |||
Residual | 1 | 150 | 150 | |||||
Total | 2 | 2600 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
Intercept | -70320 | 17459.07357 | -4.027705119 | 0.154927474 | -292158.5633 | 151518.5633 | -292158.5633 | 151518.5633 |
Year | 35 | 8.660254038 | 4.041451884 | 0.154420958 | -75.03896087 | 145.0389609 | -75.03896087 | 145.0389609 |
Car sales = -70320 + 35 * year
If you consider year 2015 =1 and year 2016=2 and 2017 =3
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.970725343 | |||||||
R Square | 0.942307692 | |||||||
Adjusted R Square | 0.884615385 | |||||||
Standard Error | 12.24744871 | |||||||
Observations | 3 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 1 | 2450 | 2450 | 16.33333333 | 0.154420958 | |||
Residual | 1 | 150 | 150 | |||||
Total | 2 | 2600 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
Intercept | 170 | 18.70828693 | 9.086882225 | 0.069778423 | -67.71132404 | 407.711324 | -67.71132404 | 407.711324 |
Year | 35 | 8.660254038 | 4.041451884 | 0.154420958 | -75.03896087 | 145.0389609 | -75.03896087 | 145.0389609 |
Car sales = 170 + 35 * year