Regression Analysis and Model Building Notes - DS 101 Spring 2008, Study notes of Humanities

Download Regression Analysis and Model Building Notes - DS 101 Spring 2008 and more Study notes Humanities in PDF only on Docsity! Plot of Fitted Model FuelCons = 13.5821 - 0.74881*Week_# 0 2 4 6 8 Week_# 7.5 8.5 9.5 10.5 11.5 12.5 Fu el C on s DS 101 – Spring 2008 Regression Analysis and Model Building #2 February 19th I. Regression Analysis Review We start out with the idea that there is a relationship between two variables in the form of a data set of paired x’s and y’s. And we found we measure this relationship with the correlation coefficient and the coefficient of determination. And we can see this relationship in a simple scatter diagram. This is what we did in the first lab. The method used was the least squares formula to develop the quantitative form of the model with a y-intercept and a slope using the notation b0 and b1 respectively. II. Model Building Exercise: We need to remind ourselves before we get too far along what is the purpose of regression analysis. The following are cases where you need to come up with the proposed linear regression model in the form of y = f(X1, X2, …Xn) → Ŷ = β0 + β1 X1 + β2 X2 + … +.βn X1. Write out the initial model in the space after the case in the above format. Think about why and how you would want to use regression analysis for each case – this gets to focus on what would be the response variable. 1. You are a marketing manager for Home Depot. For the market areas for the 56 Home Depot stores in your district you have the assessed valuation of the houses, total store sales, square footage of the houses, age of the houses, population figures, and if area is considered urban or suburban. 2. You are the director of the Career Center at a large university. Your variables for a sample of recent graduates include overall GPA, major area of study GPA, starting salaries, industry code where they accepted their job, whether or not the graduate was a business major, age, years of work experience prior to graduation. 3. You are a product designer with Black and Decker Tools. Your sample variables for your study of a new drill-bit design include average drill speed used in the test, hardness index of the material used qkd3al-975707-3452973-ds101-notes02-regression-b-doc.doc Page 1 of 5 Run the simple regression analysis Organize x’s and y’s into a data set Enter data into Statgraphics Selection of x’s and y’s DS 101 – Spring 2008 Regression Analysis and Model Building #2 February 19th to test the drill for a three-inch drill hole, length of time for the drill bit to break, whether or not water was used to cool the drill bit when being used. 4. You are a loan manager for a bank. Your sample variables from the mortgages loans made in the last year are the selling price of houses, square footage of the houses, age of the houses, number of room of the houses, number of bathroom in the houses, if the garages is detached (not connected directly to the house), and if it is a corner lot or not. 5. You are a managing director of a large consulting firm. You sample includes the following information about you consulting project manages. These are consultants with between five and seven years experience and are responsible for day-to-day management of your consulting projects. You have their most current ranking as to top third, middle third, bottom third; their field of study in college (business, mathematics, engineering, liberal arts, science), whether they have a masters degree or not, upper-division college GPA, if they have any prior work experience, and if their major client base is private sector or public sector. III. Multiple Linear Regression. Example: We will expand our natural gas consumption to now include a chill factor. Menu Options: Relate → Multiple Factors → Multiple Regression IV. : Hypothesis Testing in Multiple Regression Analysis: With More Than One Predictor Variable. Example: Gas Consumption = f (Average Temperature, Chill Factor) It is a three step process: Step I. State the hypothesis: H0: All βi’s = 0 or Ha: Not all βi’s = 0 qkd3al-975707-3452973-ds101-notes02-regression-b-doc.doc Page 2 of 5