Assignment 6 Statistical Inference: (Chapter 11) Part 1&2
Title: Analytical Techniques in Public Administration
Book: Business Analytics: Data Analysis and Decision Making 5th edition
Albright & Winston, 2015 Cengage Learning (ISBN: 978-1-133-58826-9)
Statistics Tool: Download DecisionTools Suite Industrial, Textbook Edition
Part1
1) Estimate a multiple regression equation to predict the price of houses in a given community. Employ all available explanatory variables.
2) Discuss how well the model will predict the price of houses and the “goodness of fit.” How can decision-makers use the model?
3) Indicate if there is evidence of multi-collinearity in this model. Which variables are causing it? What are the associated implications? What steps can be taken to reduce the multicollinearity?
Here is the data file: Assign 6-1 Price of Houses
Part 2
The owner of a restaurant in Bloomington, Indiana has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables.
- a) Build a regression model (hint – your model will have a d.v. and three i.v.s).
- b) Use the coefficients to construct a regression equation that represents: sales as a function of population, advertising in the current year, and advertising in the previous year.
- c) Answer this question: Can you expect predictions of sales in futureyears to be very accurate if they are based on this regression equation? Explain your answer in detail.
- d) Now use your regression equation to predict sales for year 20. Since we don’t know what the population will be in year 20, assume no change from year 19. The planned advertising level for year 20 is $30,000. Find a prediction and a 95% prediction interval for sales in year 20.
Don’t forget that you are an analyst working for a decision-maker. Explain your work or the customer is not going to be pleased, regardless of your “technical expertise.”
Here is the data file: Assign 6-2 Restaurant Sales Data
