logistic-regression with examples000.ppt

An Introduction to
Logistic Regression
JohnWhitehead
Department of Economics
East Carolina University

Outline
 Introduction and
Description
 Some Potential
Problems and Solutions
 Writing Up the Results

Introduction and
Description
 Why use logistic regression?
 Estimation by maximum likelihood
 Interpreting coefficients
 Hypothesis testing
 Evaluating the performance of the
model

Why use logistic
regression?
 There are many important research topics
for which the dependent variable is
"limited."
 For example: voting, morbidity or
mortality, and participation data is not
continuous or distributed normally.
 Binary logistic regression is a type of
regression analysis where the dependent
variable is a dummy variable: coded 0 (did
not vote) or 1(did vote)

The Linear Probability
Model
In the OLS regression:
Y =  + X + e ; where Y = (0, 1)
 The error terms are heteroskedastic
 e is not normally distributed because
Y takes on only two values
 The predicted probabilities can be
greater than 1 or less than 0

Q: EVAC
Did you evacuate your home to go someplace safer
before Hurricane Dennis (Floyd) hit?
1 YES
2 NO
3 DON'T KNOW
4 REFUSED
An Example: Hurricane
Evacuations

The Data
EVAC PETS MOBLHOME TENURE EDUC
0 1 0 16 16
0 1 0 26 12
0 1 1 11 13
1 1 1 1 10
1 0 0 5 12
0 0 0 34 12
0 0 0 3 14
0 1 0 3 16
0 1 0 10 12
0 0 0 2 18
0 0 0 2 12
0 1 0 25 16
1 1 1 20 12

OLS Results
Dependent Variable: EVAC
Variable B t-value
(Constant) 0.190 2.121
PETS -0.137 -5.296
MOBLHOME 0.337 8.963
TENURE -0.003 -2.973
EDUC 0.003 0.424
FLOYD 0.198 8.147
R
2
0.145
F-stat 36.010

Problems:
Descriptive Statistics
1070 -.08498 .76027 .2429907 .16325
1070
Unstandardized
Predicted Value
Valid N (listwise)
N MinimumMaximum Mean
Std.
Deviatio
Predicted Values outside the 0,1
range

Heteroskedasticity
TENURE
100
80
60
40
20
0
U
n
s
t
a
n
d
a
r
d
i
z
e
d
R
e
s
i
d
u
a
l
10
0
-10
-20
Dependent Variable: LNESQ
B t-stat
(Constant) -2.34 -15.99
LNTNSQ -0.20 -6.19
Park Test

The Logistic Regression Model
The "logit" model solves these problems:
ln[p/(1-p)] =  + X + e
 p is the probability that the event Y occurs,
p(Y=1)
 p/(1-p) is the "odds ratio"
 ln[p/(1-p)] is the log odds ratio, or "logit"

More:
 The logistic distribution constrains the
estimated probabilities to lie between 0 and
1.
 The estimated probability is:
p = 1/[1 + exp(- -  X)]
 if you let  +  X =0, then p = .50
 as  +  X gets really big, p approaches 1
 as  +  X gets really small, p approaches 0

logistic-regression with examples000.ppt

Comparing LP and Logit
Models
0
1
LP Model
Logit Model

Maximum Likelihood Estimation
(MLE)
 MLE is a statistical method for estimating
the coefficients of a model.
 The likelihood function (L) measures the
probability of observing the particular set
of dependent variable values (p1, p2, ..., pn)
that occur in the sample:
L = Prob (p1* p2* * * pn)
 The higher the L, the higher the probability
of observing the ps in the sample.

 MLE involves finding the coefficients (, )
that makes the log of the likelihood
function (LL < 0) as large as possible
 Or, finds the coefficients that make -2
times the log of the likelihood function (-
2LL) as small as possible
 The maximum likelihood estimates solve
the following condition:
{Y - p(Y=1)}Xi = 0
summed over all observations, i = 1,…,n

Interpreting Coefficients
 Since:
ln[p/(1-p)] =  + X + e
The slope coefficient () is interpreted as the rate of
change in the "log odds" as X changes … not very useful.
 Since:
p = 1/[1 + exp(- -  X)]
The marginal effect of a change in X on the probability
is: p/X = f( X) 

 An interpretation of the logit
coefficient which is usually more
intuitive is the "odds ratio"
 Since:
[p/(1-p)] = exp( + X)
exp() is the effect of the
independent variable on the
"odds ratio"

From SPSS Output:
Variable B Exp(B) 1/Exp(B)
PETS -0.6593 0.5172 1.933
MOBLHOME 1.5583 4.7508
TENURE -0.0198 0.9804 1.020
EDUC 0.0501 1.0514
Constant -0.916
“Households with pets are 1.933 times more likely
to evacuate than those without pets.”

Hypothesis Testing
 The Wald statistic for the  coefficient
is:
Wald = [ /s.e.B]2
which is distributed chi-square with 1
degree of freedom.
 The "Partial R" (in SPSS output) is
R = {[(Wald-2)/(-2LL()]}1/2

An Example:
Variable B S.E. Wald R Sig t-value
PETS -0.6593 0.2012 10.732 -0.1127 0.0011 -3.28
MOBLHOME 1.5583 0.2874 29.39 0.1996 0 5.42
TENURE -0.0198 0.008 6.1238 -0.0775 0.0133 -2.48
EDUC 0.0501 0.0468 1.1483 0.0000 0.2839 1.07
Constant -0.916 0.69 1.7624 1 0.1843 -1.33

Evaluating the Performance
of the Model
There are several statistics which can
be used for comparing alternative
models or evaluating the
performance of a single model:
 Model Chi-Square
 Percent Correct Predictions
 Pseudo-R2

Model Chi-Square
 The model likelihood ratio (LR), statistic is
LR[i] = -2[LL() - LL(, ) ]
{Or, as you are reading SPSS printout:
LR[i] = [-2LL (of beginning model)] - [-2LL (of ending
model)]}
 The LR statistic is distributed chi-square
with i degrees of freedom, where i is the
number of independent variables
 Use the “Model Chi-Square” statistic to
determine if the overall model is
statistically significant.

An Example:
Beginning Block Number 1. Method: Enter
-2 Log Likelihood 687.35714
Variable(s) Entered on Step Number
1.. PETS PETS
MOBLHOME MOBLHOME
TENURE TENURE
EDUC EDUC
Estimation terminated at iteration number 3 because
Log Likelihood decreased by less than .01 percent.
-2 Log Likelihood 641.842
Chi-Square df Sign.
Model 45.515 4 0.0000

Percent Correct Predictions
 The "Percent Correct Predictions" statistic
assumes that if the estimated p is greater
than or equal to .5 then the event is
expected to occur and not occur otherwise.
 By assigning these probabilities 0s and 1s
and comparing these to the actual 0s and
1s, the % correct Yes, % correct No, and
overall % correct scores are calculated.

An Example:
Observed % Correct
0 1
0 328 24 93.18%
1 139 44 24.04%
Overall 69.53%
Predicted

Pseudo-R2
 One psuedo-R2
statistic is the McFadden's-
R2
statistic:
McFadden's-R2
= 1 - [LL(,)/LL()]
{= 1 - [-2LL(, )/-2LL()] (from SPSS printout)}
 where the R2
is a scalar measure which
varies between 0 and (somewhat close to)
1 much like the R2
in a LP model.

An Example:
Beginning -2 LL 687.36
Ending -2 LL 641.84
Ending/Beginning 0.9338
McF. R
2
= 1 - E./B. 0.0662

Some potential problems
and solutions
 Omitted Variable Bias
 Irrelevant Variable Bias
 Functional Form
 Multicollinearity
 Structural Breaks

Omitted Variable Bias
 Omitted variable(s) can result in bias in the
coefficient estimates. To test for omitted variables
you can conduct a likelihood ratio test:
LR[q] = {[-2LL(constrained model, i=k-q)]
- [-2LL(unconstrained model, i=k)]}
where LR is distributed chi-square with q degrees
of freedom, with q = 1 or more omitted variables
 {This test is conducted automatically by SPSS if you
specify "blocks" of independent variables}

An Example:
Variable B Wald Sig
PETS -0.699 10.968 0.001
MOBLHOME 1.570 29.412 0.000
TENURE -0.020 5.993 0.014
EDUC 0.049 1.079 0.299
CHILD 0.009 0.011 0.917
WHITE 0.186 0.422 0.516
FEMALE 0.018 0.008 0.928
Constant -1.049 2.073 0.150
Beginning -2 LL 687.36
Ending -2 LL 641.41

Constructing the LR Test
“Since the chi-squared value is less than the critical
value the set of coefficients is not statistically
significant. The full model is not an improvement over
the partial model.”
Ending -2 LL Partial Model 641.84
Ending -2 LL Full Model 641.41
Block Chi-Square 0.43
DF 3
Critical Value 11.345

 The inclusion of irrelevant variable(s)
can result in poor model fit.
 You can consult your Wald statistics
or conduct a likelihood ratio test.
Irrelevant Variable Bias

Functional Form
 Errors in functional form can result in
biased coefficient estimates and poor
model fit.
 You should try different functional forms
by logging the independent variables,
adding squared terms, etc.
 Then consult the Wald statistics and model
chi-square statistics to determine which
model performs best.

Multicollinearity
 The presence of multicollinearity will not lead to
biased coefficients.
 But the standard errors of the coefficients will be
inflated.
 If a variable which you think should be statistically
significant is not, consult the correlation
coefficients.
 If two variables are correlated at a rate greater
than .6, .7, .8, etc. then try dropping the least
theoretically important of the two.

Structural Breaks
 You may have structural breaks in your data. Pooling
the data imposes the restriction that an independent
variable has the same effect on the dependent variable
for different groups of data when the opposite may be
true.
 You can conduct a likelihood ratio test:
LR[i+1] = -2LL(pooled model)
[-2LL(sample 1) + -2LL(sample 2)]
where samples 1 and 2 are pooled, and i is the number
of dependent variables.

An Example
 Is the evacuation behavior from Hurricanes
Dennis and Floyd statistically equivalent?
Floyd Dennis Pooled
Variable B B B
PETS -0.66 -1.20 -0.79
MOBLHOME 1.56 2.00 1.62
TENURE -0.02 -0.02 -0.02
EDUC 0.05 -0.04 0.02
Constant -0.92 -0.78 -0.97
Beginning -2 LL 687.36 440.87 1186.64
Ending -2 LL 641.84 382.84 1095.26
Model Chi-Square 45.52 58.02 91.37

Constructing the LR Test
Floyd Dennis Pooled
Ending -2 LL 641.84 382.84 1095.26
Chi-Square 70.58 [Pooled - (Floyd + Dennis)]
DF 4
Critical Value 13.277 p = .01
Since the chi-squared value is greater than the critical
value the set of coefficients are statistically different.
The pooled model is inappropriate.

What should you do?
 Try adding a dummy variable:
FLOYD = 1 if Floyd, 0 if Dennis
Variable B Wald Sig
PETS -0.85 27.20 0.000
MOBLHOME 1.75 65.67 0.000
TENURE -0.02 8.34 0.004
EDUC 0.02 0.27 0.606
FLOYD 1.26 59.08 0.000
Constant -1.68 8.71 0.003

Writing Up Results
 Present descriptive statistics in a table
 Make it clear that the dependent variable is
discrete (0, 1) and not continuous and that you
will use logistic regression.
 Logistic regression is a standard statistical
procedure so you don't (necessarily) need to write
out the formula for it. You also (usually) don't
need to justify that you are using Logit instead of
the LP model or Probit (similar to logit but based
on the normal distribution [the tails are less fat]).

An Example:
"The dependent variable which measures
the willingness to evacuate is EVAC. EVAC
is equal to 1 if the respondent evacuated
their home during Hurricanes Floyd and
Dennis and 0 otherwise. The logistic
regression model is used to estimate the
factors which influence evacuation
behavior."

 In the heading state that your dependent variable
(dependent variable = EVAC) and that these are
"logistic regression results.”
 Present coefficient estimates, t-statistics (or Wald,
whichever you prefer), and (at least the) model
chi-square statistic for overall model fit
 If you are comparing several model specifications
you should also present the % correct predictions
and/or Pseudo-R2
statistics to evaluate model
performance
 If you are comparing models with hypotheses
about different blocks of coefficients or testing for
structural breaks in the data, you could present
the ending log-likelihood values.
Organize your regression results in a table:

An Example:
Table 2. Logistic Regression Results
Dependent Variable = EVAC
Variable B B/S.E.
PETS -0.6593 -3.28
MOBLHOME 1.5583 5.42
TENURE -0.0198 -2.48
EDUC 0.0501 1.07
Constant -0.916 -1.33
Model Chi-Squared 45.515

"The results from Model 1 indicate that
coastal residents behave according to
risk theory. The coefficient on the
MOBLHOME variable is negative and
statistically significant at the p < .01 level
(t-value = 5.42). Mobile home residents
are 4.75 times more likely to evacuate.”
When describing the statistics in the
tables, point out the highlights for
the reader.
What are the statistically significant
variables?

“The overall model is significant at
the .01 level according to the Model
chi-square statistic. The model
predicts 69.5% of the responses
correctly. The McFadden's R2
is .066."
Is the overall model statistically
significant?

Which model is preferred?
"Model 2 includes three additional
independent variables. According to the
likelihood ratio test statistic, the partial
model is superior to the full model of
overall model fit. The block chi-square
statistic is not statistically significant at
the .01 level (critical value = 11.35 [df=3]).
The coefficient on the children, gender,
and race variables are not statistically
significant at standard levels."

Also
 You usually don't need to discuss the
magnitude of the coefficients--just the sign
(+ or -) and statistical significance.
 If your audience is unfamiliar with the
extensions (beyond SPSS or SAS printouts)
to logistic regression, discuss the
calculation of the statistics in an appendix
or footnote or provide a citation.
 Always state the degrees of freedom for
your likelihood-ratio (chi-square) test.

References
 http://personal.ecu.edu/whiteheadj/data/logit/
 http://personal.ecu.edu/whiteheadj/data/logit/logitpap.htm
 E-mail: WhiteheadJ@mail.ecu.edu

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