# Economic Assignment

**Topics:**Regression analysis, Linear regression, Statistics

**Pages:**12 (1926 words)

**Published:**November 13, 2014

OUM BUSINESS SCHOOL

MAY 2014

BMME5103

MANAGERIAL ECONOMICS

MATRICULATION NO: CGS00948301

IDENTITY CARD NO.: 810425-10-6009

TELEPHONE NO.: 016-2051042

E-MAIL : mlbmurugan@gmail.com

LEARNING CENTRE: Kuala Lumpur

PART 1

Market segment

Sales (‘000 units)

Advertising expenditure

(RM’000)

Selling Price

(RM per unit)

Disposable Income

(RM’000)

1

160

150

15.00

19.0

2

220

160

13.50

17.5

3

140

50

16.50

14.0

4

190

190

14.50

21.0

5

130

90

17.00

15.5

6

160

60

16.00

14.5

7

200

140

13.00

21.5

8

150

110

18.00

18.0

9

210

200

12.00

18.5

10

190

100

15.50

20.0

a) Develop a regression model that determines the relationship between Sales and Selling

Price.

I. What is the estimated regression equation?

y = α + β(x)

Sales = 390.38 -14.26 (Selling Price)

II. Is selling price of a significant determinant of sales? At what level(s) of significance? Yes, selling price indeed a significant determinant of sales. This is supported by the reading of P value which is 0.001, whereby 1 – 0.001 = 0.999. This can be translated that the finding is 99.9% confidence and therefore, it can be concluded that there is a positive relationship between selling price and sales.

III. What is the price elasticity of demand at a selling price of RM12.00 per unit? Suppose that a demand “curve” has equation Sales = 390.38 -14.26 (Selling Price). We put “curve” in quotes as this equation is actually a straight line. Suppose that the current price is P0 = RM 12. The sales corresponding to this is Sales (So) = 390.38 -14.26 (12) = 219.26

ED = β2

P0

=

-14.26

X

12.00

= - 0.78

S0

219.26

Since the elasticity value is - 0.78, which less than 1, so the demand or sales is inelastic.

We also can interpret the above as, whenever the price increases by 1%, there is a decrease in sales by 0.78%.

b) Add disposable income as an independent variable and regress sales on both selling price

and disposable income.

I. What is the estimated regression equation?

Sales = 311.61 -12.31 (Selling Price) + 2.75 (Disposable Income)

II. Is the co-efficient on selling price in (a) and (b) the same? Why or why not? The co-efficient on selling price in (a) is (– 14.26) whereas co-efficient on selling price in (b) is (– 12.31). As such, both the co-efficient value is not same. This is because each co-efficient is influenced by other variables in the regression model. Each co-effcient in multiple regression model represent the additional effect of adding another independent variable into the said model, if the effects of all the other variables are already accounted for. As such, we can conclude that the co-efficient will change when other variables are added to or deleted from a model.

III. What is the income elasticity according to the model at a disposable income level of RM18,500 and at a selling of RM12.00 per unit? Sales = 311.61 -12.31 (Selling Price) + 2.75 (Disposable Income) Sales = 311.61 -12.31 (12) + 2.75 (18.5K) = 214.77

EI = (2.75) X (Disposable Income) / (Sales)

EI = (2.75) X (18.5) / (214.77)

= 0.24

When the Income increases by 1%, the sales increases by 0.24%.

c) Add advertising expenditure as another independent variable and regress sales on selling price, disposable income and advertising expenditure. I. What is the estimated regression equation?

Sales = 310.245 + 0.008 (Advertising Expenditure) – 12.202 (Selling Price) + 2.677 (Disposable Income)

II. How “good” is the estimated model?

How “good” is a model is depends on the value of R-square. The higher the value of the R-square, the better the model fits the data. The R-square value for the above model is 0.79 which suggests that 79% of the points should fall within the regression line. The closer the value to 100%, the better fit the...

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