In: Statistics and Probability
Descriptive Statistics |
|||
Mean |
Std. Deviation |
N |
|
Sales |
16.2000 |
4.51762 |
10 |
Advertising |
10.2000 |
1.15085 |
10 |
Employees |
17.9000 |
6.90330 |
10 |
On avarge Sales 16.200 (thousands of dollars) , it's mean's that most of sales near to 16.200(thousands of dollars)
On avarge Advertising 10.2000 (in hundreds of dollars) .
On avarge Employees 17.900 number.
Correlations |
||||
Sales |
Advertising |
Employees |
||
Pearson Correlation |
Sales |
1.000 |
.921 |
.849 |
Advertising |
.921 |
1.000 |
.643 |
|
Employees |
.849 |
.643 |
1.000 |
|
Sig. (1-tailed) |
Sales |
. |
.000 |
.001 |
Advertising |
.000 |
. |
.022 |
|
Employees |
.001 |
.022 |
. |
|
N |
Sales |
10 |
10 |
10 |
Advertising |
10 |
10 |
10 |
|
Employees |
10 |
10 |
10 |
Above tabel of Sales to Advrtising and Employees are perfect postive linear realtionship.
Variables Entered/Removeda |
|||
Model |
Variables Entered |
Variables Removed |
Method |
1 |
Employees, Advertisingb |
. |
Enter |
a. Dependent Variable: Sales |
|||
b. All requested variables entered. |
Model Summaryb |
||||||||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Change Statistics |
Durbin-Watson |
||||
R Square Change |
F Change |
df1 |
df2 |
Sig. F Change |
||||||
1 |
.980a |
.960 |
.949 |
1.02002 |
.960 |
84.770 |
2 |
7 |
.000 |
1.488 |
a. Predictors: (Constant), Employees, Advertising |
||||||||||
96% indicates that the model explains all the variability of the Sales data around its mean, it's mean's that given regression model 96% good fit for given data. |
ANOVAa |
||||||
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
176.397 |
2 |
88.198 |
84.770 |
.000b |
Residual |
7.283 |
7 |
1.040 |
|||
Total |
183.680 |
9 |
||||
Regression model is siginificant for given data ata 0.05 level of alpha |
||||||
Coefficientsa |
|||||||||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
90.0% Confidence Interval for B |
Correlations |
Collinearity Statistics |
||||||
B |
Std. Error |
Beta |
Lower Bound |
Upper Bound |
Zero-order |
Partial |
Part |
Tolerance |
VIF |
||||
1 |
(Constant) |
-14.524 |
3.330 |
-4.361 |
.003 |
-20.834 |
-8.214 |
||||||
Advertising |
2.509 |
.386 |
.639 |
6.503 |
.000 |
1.778 |
3.241 |
.921 |
.926 |
.489 |
.586 |
1.706 |
|
Employees |
.286 |
.064 |
.438 |
4.453 |
.003 |
.165 |
.408 |
.849 |
.860 |
.335 |
.586 |
1.706 |
|
a. Dependent Variable: Sales |
Regression model:
Sales = -14.524 +2.509 * Adertising + 0.286 * Employees
Now the predict the Sales to dercrease by the -14.524 as per one unit. If Adertising and Employees values are zero then sales of meas is -14.524.
The predict the sales to increase by 2.509 hundreds of dollars Adertising as per one unit of sales.
The predict the sales to increase by 0.286 number of Employees as per one unit of sales.
Coefficient Correlationsa |
||||
Model |
Employees |
Advertising |
||
1 |
Correlations |
Employees |
1.000 |
-.643 |
Advertising |
-.643 |
1.000 |
||
Covariances |
Employees |
.004 |
-.016 |
|
Advertising |
-.016 |
.149 |
||
a. Dependent Variable: Sales |
Collinearity Diagnosticsa |
||||||
Model |
Dimension |
Eigenvalue |
Condition Index |
Variance Proportions |
||
(Constant) |
Advertising |
Employees |
||||
1 |
1 |
2.927 |
1.000 |
.00 |
.00 |
.01 |
2 |
.069 |
6.525 |
.04 |
.01 |
.66 |
|
3 |
.004 |
27.418 |
.96 |
.99 |
.33 |
|
a. Dependent Variable: Sales |
Residuals Statisticsa |
|||||
Minimum |
Maximum |
Mean |
Std. Deviation |
N |
|
Predicted Value |
9.8508 |
20.5990 |
16.2000 |
4.42715 |
10 |
Std. Predicted Value |
-1.434 |
.994 |
.000 |
1.000 |
10 |
Standard Error of Predicted Value |
.360 |
.757 |
.545 |
.130 |
10 |
Adjusted Predicted Value |
9.6669 |
21.1298 |
16.2835 |
4.47181 |
10 |
Residual |
-.89898 |
2.10759 |
.00000 |
.89958 |
10 |
Std. Residual |
-.881 |
2.066 |
.000 |
.882 |
10 |
Stud. Residual |
-1.111 |
2.340 |
-.031 |
1.034 |
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