1. Analysis of Variance and Regression Using Dummy Variables
Multivariate Models
Analysis of Variance and Regression Using Dummy Variables

2. Models
A Model: A statement of the relationship between a phenomenon to be explained and the factors, or variables, which explain it.
Steps in the Process of Quantitative Analysis:
Specification of the model
Estimation of the model
Evaluation of the model

3. Model of Housing Values and Building Size
Historian A hypothesizes that there is a linear relationship among housing value, building size and the number of families in the dwelling.
Building Size = Square Feet/1000
Housing Value = 1905 Property Assessment in 2002 dollars/1000
Families = Number of families in the dwelling
Housing Value = a + b1(Building Size) + b2(Families).

4. The Model of Determinants of Housing Value
Dep Var: NEWVAL N: Multiple R: Squared multiple R: 0.524
Adjusted squared multiple R: Standard error of estimate:
Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail)
CONSTANT
NEWSIZE
FAMILIES
Analysis of Variance
Source Sum-of-Squares df Mean-Square F-ratio P
Regression
Residual

5. New Questions…
Historian B suggests that there will be a neighborhood effect on housing values, and suggests that the values will be different, even taking size and number of families into consideration, on the north side, south side and east side.
Historian B poses the problem to Historian A.

6. New Possibility: Analysis of Variance
Comparison of the levels of an interval level dependent variable and a categorical or nominal independent variable.
Are the property values different in the three neighborhoods, East, NW and South.
Take a look first at the mean differences.

7. Value by Neighborhood

8. But… Are the results statistically significant?
What is the strength of the relationship?
How would we integrate this information into the earlier regression model?

9. Concepts
We partition the total variation or variance into two components:
(1) variance which is a function of the group membership, that is the differences between the groups; and
(2) variance within the groups.
More formally: Total Sum of Squares = Between Groups Sum of Squares + Within Groups Sum of Squares

10. Equation
Total Sum of Squares = Within Groups Sum of Squares + Between Groups Sum of Squares
TSS= SSW + SSB

11. Calculations

12. LET SSBETWEEN = N* (MEAN-28.818)* (MEAN -28.818)
Case VAR00001$ MEAN N SD VARIANCE SSBETWEEN
3 EASTSIDE
4 NW
5 SOUTHSID
6 Total
LET SSBETWEEN = N* (MEAN )* (MEAN )

13. Anova Table
DF between = k -1
DF within = N – k

14. Degrees of Freedom DF between = k -1 DF within = N – k
Website for F Table:
Eta Squared = SSBetween/Total SS = .345 (equivalent to R Square)

15. So, now what…
We know that the neighborhood affects the value of the house.
How do we integrate that knowledge into a regression model?

16. A Dilemma…. Regression requires interval level measurement.
One cannot include categorical variables in the equation.
Historian A proposes testing separate models for the three neighborhoods.

17. Results
Regression Models for the Three Wards: Determinants of Housing Value
Northwest East Side South Side
Constant 5.90* *
Newsize * * *
Families * N
R Squared
*Statistically significant at the .05 level.

18. Is there another way? Can we develop one model instead of three?
Answer: Yes, by remeasuring the neighborhood at the interval level.
How? By conceiving of new variables identifying the presence or the absence of the neighborhood, that is a set of binary variables, called dummy variables.

19. Illustration of Dummy Variables
Neighborhood
East Side
South Side
Northwest Side 1

20. Illustration continued…
Two new binary variables provide all the information needed for the three categories.
Rule: Create k -1 dummy variables for the original categorical variable.
The omitted category represents the value of the equation when the other dummy variables = 0.

21. New variables: Northwest Side as the Omitted Category
Variable: Eastside. Codes: Yes=1; No=0
Variable: South. Codes: Yes=1; No=0
By implication:
For a household on the Eastside, Eastside=1 and South=0
For a household on the Southside, Eastside=0 and Southside=1
For a household in the Northwest Side, Eastside = 0 and South = 0.

22. Results
Newval = a + b1(Newsize) + b2(Families) + b3(Eastside) + b4(South)
Dep Var: NEWVAL N: Multiple R: Squared multiple R: 0.56
Adjusted squared multiple R: Standard error of estimate: 19.61
Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail)
CONSTANT
NEWSIZE
FAMILIES
EASTSIDE
SOUTH

23. Implications
1. Separate regressions for each neighborhood imply that the other coefficients in the equation vary by ward.
2. Regression with dummy variables implies that the neighborhood effect is a movement of the Y intercept.
There may be interactions between the slope coefficients and the dummy variables, i.e., both 1 and 2 may be the case.