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Economics Project Report

A statistical analysis  
I believe income has the strongest influence on sales?
Null hypothesis:
There is no significant linear relationship between sales and income
Alternate hypothesis:
There is significant linear relationship between sales and income

Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics

R Square Change
F Change
df1
df2
Sig. F Change

1
.384a
.147
.123
849860.17082
.147
6.213
1
36
.017

a. Predictors: (Constant), income

Table 1: Model summary

Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.

B
Std. Error
Beta

1
(Constant)
299876.806
554446.724

.541
.592

income
39.170
15.714
.384
2.493
.017

a. Dependent Variable: Sales

Table 2: sales and income coefficients
The required is 14.7% implying that the model is very weak in predicting the sales. Also, the p-value is more than 0.01(0.17), leading to the conclusion that there is no significant linear relationship between sales and income. Both coefficients are positive, meaning that both variables move in the same direction (when income increases, the sales is also expected to increase).
 
Figure 1: scatter plot (sales/income)
 
Multiple Linear Regressions
 

Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics

R Square Change
F Change
df1
df2
Sig. F Change

1
.590a
.348
.221
800973.10805
.348
2.754
6
31
.029

a. Predictors: (Constant), College, location, age, growth, HS, income

b. Dependent Variable: Sales

Table 3: Multiple regression model summary
 

Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.

B
Std. Error
Beta

1
(Constant)
-1442316.007
3509744.117

-.411
.684

growth
3249.589
41634.431
.014
.078
.938

income
-12.892
29.644
-.126
-.435
.667

age
-38483.866
87983.593
-.073
-.437
.665

location
-617289.400
278733.572
-.340
-2.215
.034

HS
65109.415
33144.428
.535
1.964
.059

College
3938.894
27124.119
.035
.145
.885

a. Dependent Variable: Sales

Table 4: Multiple regression coefficients
 
On including all the dependent variables in the multiple linear regression models, its power of predicting sales improves from 12.3% (in simple linear regression model) to 22.1%. Although this is a significant improvement, at 22.1% means that the model is still not very good in predicting sales. Nevertheless, the model does not provide evidence that sales have a significant linear relationship with the dependent variables……………………………….
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