Sales Forecast of BMW: Multiple Regression Analysis

Data Summary

This report has been prepared to evaluate the sales forecast of BMW. BMW is one of the biggest car manufacturing firms. The organization has been established in 1917. Following is the current financial data of the company:

Study of multiple regression models has been done over the sales trend of the company to evaluate the future changes.

Data summary:

Data of the company has been collected on annual basis of 5 years.

Here,

X1 = Average price of cars

X2 = Average advertising expenses

X3 = Annual GDP of company

X4 = average household income

X5 = Major competitor’s GM’s vehicles (Parvizi et al, 2015)

Following is the multiple regression analysis summary of the company:

Y = 857503 – 0752X1 + 0.0000064X2 + 0.00000000083X3 – 18.44X4 + 1.861X5 (Fox, 2015)

Where,

Y = the quantity of cars which has been demanded annually

The constant value of Cars intercept = 857503

X1 = Average price of cars

X2 = Average advertising expenses

X3 = Annual GDP of company

X4 = average household income

X5 = Major competitor’s GM’s vehicles (Darlington and Hayes, 2016)

Coefficient of X variables directly makes an impact over the demand of the cars which are as follows:

(López, Fabrizio and Plencovich, 2014)

Sensitivity analysis:

The regression analysis of the company explains that the sensitivity analysis of each independent intercept is different. It expresses that few changes into the price, advertising expenses; GM, GDP, income etc would impact over the sales of the company. The coefficient of the company explains that the changes into the entire variables would enhance the sales of the company positively except the variable advertising and GDP. Following is the residual value of the company:

It explains that few changes into the X variables impact over the X variable of the company (Chatterjee and Hadi, 2015).

Interpretation:

Further, the interpretation has been done over the all X variables and Y variables of the company and firstly, the following changes into the all 5 variables of the company have been evaluated:

In addition, through the calcualtion os regression analysis of the company, it has been found that the few changes into 0.52 changes into the price of the product would directly make an impcat over the 857503 units of the company. Further, the same analysis has been over other intercept of the company and various macro economical aspcet and it has been found that the -0.000000064855 changes into the advertsing expenses of the product would directly make an impcat over the 857503 units of the company. On the other hand, -0.00000000083 changes into the GDP of the product would directly make an impcat over the 857503 units of the company (Draper and Smith, 2014). At the same time, -18.44 and 1.86 changes into the Income and Gm respectively of the product would directly make an impcat over the 857503 units of the company.

Further, it expresses that the Standrd error of the product is 0. R square is 1. It explains that the company would enjoy a great number of saes of car in near future and at the same time, the performance of the company would also be better.

Conclusion:

To conclude, multiple regression method makes it easy for the company and the analyst to analyze that what are the factors which have impact over the sales of the company and how much would they impact over the performance of the company and the sales revenues of the company.

References:

Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.

Darlington, R.B. and Hayes, A.F., 2016. Regression analysis and linear models: Concepts, applications, and implementation. Guilford Publications.

Draper, N.R. and Smith, H., 2014. Applied regression analysis. John Wiley & Sons.

Fox, J., 2015. Applied regression analysis and generalized linear models. Sage Publications.

López, M.V., Fabrizio, M.C. and Plencovich, M.C., 2014. Multiple Regression Analysis. Probability and Statistics: A Didactic Introduction, 416.

Parvizi, D., Friedl, H., Wurzer, P., Kamolz, L.P., Lebo, P., Tuca, A., Rappl, T., Wiedner, M., Kuess, K., Grohmann, M. and Koch, H., 2015. A multiple regression analysis of postoperative complications after body-contouring surgery: a retrospective analysis of 205 patients. Obesity surgery, 25(8), pp.1482-1490.