Accounting Analysis

[Name of the Institute]Accounting Analysis

Car

Age (years)

Selling Price (£000)

1

9

8.1

2

7

6

3

11

3.6

4

12

4

5

8

5

6

7

10

7

8

7.6

8

11

8

9

10

8

10

12

6

11

6

8.6

12

6

8

The above data shows that the relationship between the cars that have been existing in the market and the selling price of the different car. As from the data it is shown that the car who has been existing since last nine years and the selling price of the car is 8.1, while the car that has been existing since last seven years has the selling price is 6. The car existing since last 11 years has the selling price is 3.6, while the car who has been existing since last twelve years has the selling price of 4. A car existing since last 8 years has the selling price 5.

The car 6 has the selling price of 10 who has been existing since last 7 years while the car existing since last 8 years has the selling price of 7.6. The ca that has been existing since last 10 years has the selling price of 8 while the car existing since last 12 years has the selling price of 6. While in the end the last two cars that have been existing since last six years has the selling price of 8 and 8.6 respectively. This relationship shows that the car who are new in the market or not had been existing for a long period of time has higher selling price, the factors might be that the car that are new in the market are providing different and new models to their customers while the car who have been existing for a longer period of time are retaining the same model and as a result of that the selling price of the car has been declined or remained the same perhaps because of the less manufacturing cost or having lower consumer demand. As the car that has been existing since last six years has the selling price of 8 and 8.6 while the car who has been existing since last 7 years has the selling price of 10 while the car existing since last 12 years in the market has the selling price of 4 and 6 (O'Brien, 2012).

SUMMARY OUTPUT

Regression Statistics

 

Multiple R

0.543646332

R Square

0.295551334

Adjusted R Square

0.225106468

Standard Error

1.732105125

Observations

12

ANOVA

 

df

SS

MS

F

Significance F

Regression

1

12.58728503

12.58729

4.195499

0.067701617

Residual

10

30.00188164

3.000188

 

 

Total

11

42.58916667

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

11.17723824

2.143271172

5.215037

0.000393

6.401732492

15.95274399

6.401732492

15.95274399

X Variable 1

-0.47875569

0.233734146

-2.04829

0.067702

-0.99954782

0.042036439

-0.99954782

0.042036439

R- Square

R square is the statistical measure of how close the data are that will fit in the regression line. R square is also called as ...