Binary Independent Variables

# Binary Independent Variables - I. Binary (or Dummy)...

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1 Slide #1 I. Binary (or Dummy) Independent Variables ? Slide #2 III. Introduction A. New type of variable 1. Past: used quantitative variables (numerically measurable); continuous 2. Now: variables that take small number of values; discrete a) Gender b) Market size c) Region of country d) Marital status (married vs. not), etc Slide #3 Introduction (cont.) B. Used as IV in this section C. Used as DV later in course Slide #4 Introduction (cont.) Institute of Management Accountants (IMA) publishes an annual Salary Guide In Strategic Finance magazine sfmag@imanet.org Annual survey of members “…based on a regression equation derived from survey results.” Slide #5 IMA Salary Guide (cont.) SALARY = 35,491 + 18393TOP + 8392SENIOR – 10615ENTRY +914YEARS +10975ADVDEGREE – 8684NODEGREE + 9195PROFCERT + 8417MALE TOP=1 if top level mgmt, 0 if not SENIOR=1 if senior level mgmt , 0 if not ENTRY=1 if entry level , 0 if not ADVDEGREE=1 if advanced degree , 0 if not NODEGREE=1 if no degree , 0 if not PROFCERT=1 if hold professional certification , 0 if not MALE=1 if male , 0 if not YEARS=years of experience Slide #6 IMA Salary Guide (cont.) Average IMA member (1999) Male 14.5 years experience Professional certification Salary = \$66,356 Figure obtained from substituting values into regression equation

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2 Slide #7 Are Wins Worth More in a Large Market? See regression output for dummy variables as IVs. (note) Slide #8 Introduction (cont.) D. Example #1 1. Y = + X 2 + 2. Y: social program expenditures per state 3. X 2 : state’s total revenue 4. Suppose legislatures controlled by Democrats spend more from same revenue than those controlled by Republicans 5. How account for this in model? 6. What’s the categorical variable? Slide #9 Introduction (cont.) E. Example #2 1. Y = + X 2 + 2. Y: coach’s earnings 3. X 2 : coach’s experience 4. Suppose women earn less than men with equal experience (& other characteristics) 5. How account for this in model? 6. What’s the categorical variable? Slide #10 Introduction (cont.) F. Example #3 1. Y = + X 2 + 2. Y: sales of swimsuits in Minnesota 3. X 2 : Minnesota’s population 4. Suppose sales peak in warm months 5. How account for this in model? 6. What’s the categorical variable? Slide #11 Introduction (cont.) G. Example #4 1. Y = + X 2 + 2. Y: profits of NBA teams 3. X 2 : wins 4. Suppose teams in large markets make more profit on their wins than teams in other markets 5. How account for this in model? 6. What’s the categorical variable? Slide #12 Introduction (cont.) G. Will use Binary (or Dummy) Independent Variables 1. Create a special variable that takes a value of a) if the unit of observation falls into one category b) if the unit falls into the other category 1 0
3 Slide #13 Introduction (cont.) Dummy Independent Variables 1. take values of ONLY OR 1 0 Why Use 0 & 1 Values?

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## This note was uploaded on 09/09/2011 for the course ECON 4451 taught by Professor Richardhofler during the Fall '11 term at University of Central Florida.

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Binary Independent Variables - I. Binary (or Dummy)...

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