Objective 1: Identify the relationship between the sales turnover and advertisement expenditure.

2. Identify the dependent (sales) and independent (advertisement) variable at 99% confidence level.

3. Identify the variation in sales due to advertisement expenditure.

4. Identify the impact of advertisement on sales

5. Predict the sales when advertisement is 500 million.

6. Test the proposed model at 99% confidence interval.

Justification: Since both the variables are on numerical scale so we will use coefficient of correlation that is r. Coefficient of determination that is R2 .Liner regression model and ANOVA

Interpretation:

Ans 1.

Sales Advertisement

Sales 1Advertisement

0.927394

1

From the above table it is observed that r=92.73%. There is a strong positive relationship between sales and advertisement expenditure. So we can conclude that as advertisement will increase the sales will increase. There is a direct relation between the two variables.

Ans 2

Regression StatisticsMultiple R 0.92739

4R Square 0.86006Adjusted R Square

0.847338

Standard Error

4687.836

Observations 13

ANOVADf SS MS F Significan

ce FRegression 1 14856804

161485680416

67.60527

5.0292E-06

Residual 11 241733899.9

21975809.08

Total 12 1727414316

Coefficients

Standard Error

t Stat P-value

Lower 95%

Upper 95%

Lower 99.0%

Upper 99.0%

Intercept 14504.7 2576.87912

5.628786834

0.000154

8833.030563

20176.38

6501.415305

22507.99123

X Variable 1 89.94325

10.93901762

8.222242144

5.03E-06

65.86663626

114.0199

55.96877951

123.9177239

There is 86% variation in sales is due to advertisement expenses and the 14% is due to other unknown factors.

The proposed regression model:

Y=a+bx

Y- Dependent Variable (sales)

a- Population y-intercept

b- X variable

Sales= 14904.5+(89.94)*(Adv. Exp)

From the above model there is a positive impact of advertisement expenditure on sales and if we increase one unit of advertisement expenditure the sales will increase by 89.54 units.

The model is statistically significant as 5.03E-06< 0.01 (alpha)

The probability of the proposed model is 0.0 which is less than alpha (0.01) so we can say that at 99% confidence level the proposed model is statistically significant so the linear regression equation i.e. Sales= 14904.5+(89.94)*(Adv. Exp) is used to forecast sales.

If we substitute x=500 million the value of predicted sales is Rs 59476 millions

Ans 3

If the advertisement expenditure is 500 million the ACC cement turnover will be 59476 million