LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034
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M.Sc. DEGREE EXAMINATION – STATISTICS
FIRST SEMESTER – APRIL 2008
ST 1811 – APPLIED REGRESSION ANALYSIS
Date : 05-05-08 Dept. No. Max. : 100 Marks
Time : 1:00 – 4:00
Section A
Answer ALL the questions
Each question carries 2 marks (10 X 2 =20 Marks)
1) Define the linear regression model in the context of any applied scenario.
2) What are the basic assumptions of a linear regression model?
3) Mention any four major areas where categorical data analysis is used.
4) Explain nominal and ordinal variables with examples
5) Explain interval variable with an example.
6) Explain the link function of a generalized linear model
7) What is the role of binary data in the generalized linear model.
8) What is meant by multicollinearity?
9) Write down the sampling variance of the slope coefficients in the multiple regression model.
10) What is the role of the variance inflation factor?
Section B
Answer any 5 questions
Each question carries 8 marks (5 X 8 = 40 Marks)
11) Derive the estimate of the parameters of a linear regression model using the method of least squares.
12) Explain the three components of a generalized linear model
13) Identify the natural parameter for the binomial logit model in the context of a decision taken to purchase a particular product
14) Identify the natural parameter for the Poisson log linear model for a count data in the context of the number of silicon wafers used in the production of a computer chip.
15) Explain Poisson log linear model.
16) Explain the regression model of the status of the employees on the education and income level in the context of the usage of a dummy variable.
17) Explain the concept of multi collinearity in the context of regression delivery time of and item on the distance traveled and the gasoline consumed.
18) Write short note on stepwise regression methods.
Section C
Answer any 2 questions
Each question carries 20 marks (2 X 20 = 40 Marks)
19 a) How do you write the distribution of a transformed mean of a response variable of a Poisson log linear model in the natural exponential family form.
(10 Marks)
19 b) Give an illustration for multinomial responses using baseline logit models.
(10 Marks)
20 a) Illustrate the Poisson general linear model from a study of nesting horseshoe crab. ( 10 Marks)
20 b) Explain the use of dummy variables in the logit models with an example.
(10 Marks)
21 a) Explain the regression model of the status of the employees on the education and income level in the context of the usage of a dummy variable. ( 10 Marks)
21 b) Explain the concept of multi collinearity in the context of regression delivery time of and item on the distance traveled and the gasoline consumed.
(10 Marks)
22 a) Job satisfaction of the employees of a company is categorized in to 1. Not satisfied 2. A little satisfied 3. Satisfied and 4. Very much satisfied. Construct a multinomial model for regressing job satisfaction on income and the gender.
( 10 Marks)
22 b) Explain the four methods for scaling residuals bringing out the relationship between them. (10 Marks)