{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

# Chapter 6 - ECMT1020 Chapter6 MultipleLinear...

This preview shows pages 1–13. Sign up to view the full content.

ECMT1020: Chapter 6 1 ECMT1020 Chapter 6 Multiple Linear Regression Analysis II Extracted from “Australasian Business Statistics” Modified by Dr Boris Choy for ECMT1020

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ECMT1020: Chapter 6 2 Topics covered 1. Indicator/Dummy variables 2. A variable selection method 3. Hypothesis testing for a subset of variables References Black 15.2, 15.3
ECMT1020: Chapter 6 3 Learning Objectives Perform multiple regression analysis with indicator/dummy variables Interpret the results for indicator variables Build a regression model from a subset of important explanatory/predictor variables using KaddStat Perform a hypothesis test for a subset of variables manually

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ECMT1020: Chapter 6 4 Indicator (or Dummy) Variables
ECMT1020: Chapter 6 5 Indicator Variables In many situations, some independent variables are qualitative (or categorical). Example: X = Gender X takes two possible values: Male (coded as 1) and Female (coded as 0) X = A question in Student Feedback Survey X takes five possible values: Strongly disagree (1), Disagree (2), Neutral (3), Agree (4) and Strongly agree (5) An indicator variable (or a dummy variable) is a variable that takes only two possible values, coded as 0 and 1. For dichotomous variables, such as gender, only ONE dummy variable is needed. For polychotomous variables, such as a student feedback survey question with c possible choices, c ‐1 dummy variables are needed.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ECMT1020: Chapter 6 6 Indicator Variables A categorical variable ( X ) that represents the location of a department store or a head office of an insurance company in a capital city. For example, NSW (1), WA (2), SA (3), TAS (4), NT (5), Vic (6), QLD (7), ACT (8) You can either use ONE independent variable X (not recommended) or SEVEN indicator variables (recommended) d 1 , d 2 , ..., d 7 in the regression model. Here d 1 =1 and other d i = 0 NSW d 1 = 2 and other d i = 0 WA ... All d i = 0 ACT
ECMT1020: Chapter 6 7 Indicator Variables

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ECMT1020: Chapter 6 8 Indicator Variables Let y = Sales (response variable) x 1 = a predictor variable x 2 = state (categorical) Model 1: Without indicator variables (Not recommended) Model 2: With indicator variables (Recommended) 2 2 1 1 0 x x y 7 27 2 22 1 21 1 1 0 ... d d d x y
ECMT1020: Chapter 6 9 Gender Discrimination and Salaries Example Example: (Black p.615‐617) A random sample of 15 workers is drawn from a pool of employed workers in a particular industry and the workers’ average monthly salaries are determined, along with their age (X 1 ) and gender (X 2 ) As gender can only be male or female, this variable is a dummy variable requiring 0/1 coding. We arbitrarily denote male as 1, and female as 0

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ECMT1020: Chapter 6 10 Gender Discrimination and Salaries Example Table 15.5 from page 616 of text
ECMT1020: Chapter 6 11 Gender Discrimination and Salaries Example Regression output (using dummy variable) Figure 15.7 from page 616 of text

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ECMT1020: Chapter 6 12 Gender Discrimination and Salaries Example
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 50

Chapter 6 - ECMT1020 Chapter6 MultipleLinear...

This preview shows document pages 1 - 13. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online