Reference no: EM133290968

Question: A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the America market. We are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables.

They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market. This study examines the association between car prices, and fuel type, aspirations, wheelbase, and car length, etc. The factors are affecting the car price considered in this study are fuel type, aspirations, wheelbase, car length, etc. Totally, the study has 200 observations and more than 15 independent variables. Some variables are qualitative, and some variables are quantitative. As you known that the fuel type, aspiration, and engine location are qualitative variables. To use a qualitative variable in a data set, we use code 0 or 1 or 2 to describe them. We then include the qualitative variable as an independent variable in our multiple regression analysis. In the data set, for fuel type variable code 0 means gas type, and code 1 means diesel type. For aspiration variable code 0 means standard type, and code 1 means turbo. For engine location variable, code 0 means front location, and code 1 means rear location. For the rest of the variables such as wheelbase, car width, and car length are quantitative variables. As part of your term project, you need to perform the following tasks:

1. Summarize the data related to car price (consider only car price and not the other variables) in a way that will help the audience see a basic picture of car price in the US. Use standard statistical devices, including graphs. Make this a concise readable, summary of the data that will help your audience understand it. You can use either Excel or Minitab.

2. To find the association between car price and the independent variables, perform the following steps.

a) Consider all independent variables provided, use regression (in Minitab or Excel) methods specify and estimate an equation that adequately predicts car price in the US. Present the relevant statistical results in a neat, understandable way that will enable your audience to learn about car price in the US from your model. Interpret the regression coefficients and the R 2 . Specify the significant variables in predicting the car price. Note: Your estimate equation should be ^y=b0+b1 · x1+b2 · x2+b3 · x3+...

b) Can we include in some or all the qualitative variables from part a? Using regression methods (in Minitab or Excel) specify and estimate a new equation that adequately describes car price in the US.

c) Compare the model you built in part (a) with the model you built in part (b). What is the difference between the two models?

d) Introduce a proper cross-product term that can identify the interaction between the independent (quantitative) variables only. Check the VIF value. Present the relevant statistical results in a neat, understandable way that will enable your audience to learn about life expectancy from your model. Interpret the regression coefficients and the R 2 . Specify the significant variables in predicting the car price.

e) What are the main results of this study?

f) What can you do to improve this model (adding/removing any independent variable or interaction term, different analysis methods,...)