In: Math
A regional transit company wants to determine whether there is a relationship between the age of a bus and the annual maintenance cost. A sample of 10 buses resulted in the following data:
Age of Bus (years) |
Annual Maintenance Cost ($) |
1 |
350 |
2 |
370 |
2 |
480 |
2 |
520 |
2 |
590 |
3 |
550 |
4 |
750 |
4 |
800 |
5 |
790 |
5 |
950 |
Instructions:
SPSS OUTPUT:
a) Scatter diagaram:
From the above scatter diagram we can observe that the points are scattered from lower left corner to upper right corner. It indicates that there was a positive correlation between the given two variables
b)
Correlations |
|||
AGE OF A BUS |
ANNUAL MAINTENNANCE COST |
||
AGEOFABUS |
Pearson Correlation |
1 |
.934** |
Sig. (2-tailed) |
.000 |
||
N |
10 |
10 |
|
**. Correlation is significant at the 0.01 level (2-tailed). |
The correlation coefficient between the age of a bus and annual maintenance cost is (r) = 0.934
c)
Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
220.000 |
58.481 |
3.762 |
.006 |
|
AGE OF A BUS |
131.667 |
17.795 |
.934 |
7.399 |
.000 |
|
a. Dependent Variable: ANNUAL MAINTENNANCE COST |
The estimated regression equation is Y = 220 + 131.667*X
d) From the above table we can observe that both β0 and β1 are different from zero (both the p values are <0.005).
Slope interpretation: One unit change in age of a bus the annual maintenance cost was increased by 131.667 dollars.
e)
Model Summary |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
||||||
1 |
.934a |
.873 |
.857 |
75.498 |
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a. Predictors: (Constant), AGE OF ABUS |
From the above table we can observe that R2 = 0.873, i.e. 87.3% of variation in the annual maintenance cost is explained by age of a bus only.
f) If x = 4.5 years then y = $812.5015