Regression Analysis

Regression Analysis

Question No # 1

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.835478305

 

 

 

 

 

 

 

R Square

0.698023997

 

 

 

 

 

 

 

Adjusted R Square

0.677434724

 

 

 

 

 

 

 

Standard Error

38.26108281

 

 

 

 

 

 

 

Observations

48

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

3

148889.8565

49629.95

33.90231

1.64557E-11

 

 

 

Residual

44

64412.06016

1463.91

 

 

 

 

 

Total

47

213301.9167

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

514.2669369

113.3315243

4.537722

4.36E-05

285.8622608

742.671613

285.8622608

742.671613

6-Pack Price ($)

-242.9707509

43.52628127

-5.58216

1.38E-06

-330.6922056

-155.2492962

-330.6922056

-155.2492962

Income/Capita ($1,000)

1.360181992

1.691791973

0.803989

0.425726

-2.049400646

4.76976463

-2.049400646

4.76976463

Mean Temp. (F)

2.931228055

0.711458375

4.120027

0.000165

1.497377934

4.365078176

1.497377934

4.365078176

Multiple Linear Regression Equation

Cans = 514.266 - 242.970*6-pack-price+1.36018*Income/Capita+2.93122*Mean Temp.

Question No # 2

Interpretation of Coefficient of Independent Variables

Demand Estimation Function

Cans = 514.266 - 242.970*6-pack-price+1.36018*Income/Capita+2.93122*Mean Temp.

The demand estimation function plays a vital role in interpretation of each coefficient of independent variable. In the light of above estimated demand function demonstrate that if price increases by one unit then demand of per capita will fall by 242.97 units. The one unit increase in income/capita will increase demand ...