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Proc freq:
Five secrets*
*Okay, well, lesser known facts
They said I wasn’t that interesting
1. Different and similar chi-squares
2. Fisher’s Exact Test. How to get one. Why you
want one
3. Odds ratios
4. When NOT to compare chi-square values
directly
5. Tests of binomial proportions
Proc freq
getting the chi-square values & more
Enterprise Guide Method
Enterprise Guide Method
Enterprise Guide Method
Enterprise Guide Method
The Syntax
PROC FREQ DATA = mydata.oldpeople ;
TABLES dthflag*nursehome /
NOROW NOPERCENT NOCUM
CHISQ MEASURES ;
Nursing home placement by death
Conditional
probabilities
Being able to find SPSS in the start menu
does not qualify you to perform a
multinomial logistic regression
1. Chi-square values
Chi-square results
Chi-square results
Pearson
Pearson
∑
(fo – fe)2
fe
Chi-square results
Chi-square results
2. What is Fisher’s exact test &
when do I get one?
“Well, you see, what you really need
to do to make this a valid statistical test is
to kill off a few more patients”
Fisher’s Exact Test: probability of a
table as unusual as the one that you
have obtained under the null
hypothesis of no relationship.
With 2 x 2 Tables it’s automatic
Recap: Fisher’s Exact Test
• Small sample size
OR
• Need exact probability
3. Odds ratios
Computing odds ratios
Divide frequency row 1, column 1 by frequency in row 1 column 2
2,846/184 = 13.51 -- odds of a person who lived not being in a nursing
home versus being in a home.
Divide frequency in row 2, column 1 by frequency row 2, column 2
2,239/ 1,077 = 2.08
Divide first result by the second
13.51/ 2.08 = 6.49
Measures
4. Mantel-Haeszel chi-square
Tests ordinal relationship
Same as Pearson if only two categories
Ordinal relationship ?
Don’t just compare values
ER visits versus nursing home
Take-away
1. Different types of chi-square values, different
types of correlations and other tests like odds
ratios do exist.
2. These statistics are very easy to obtain using
SAS.
3. While most times, all of these measures will
point you in the direction of the same
general conclusion, there are times when one
is preferable to the others.
Testing hypothesis π = ?
• PROC FREQ DATA = dsname ;
TABLES varname /
BINOMIAL (EXACT EQUIV P = .333)
ALPHA = .05 ;
BINOMIAL (EXACT EQUIV P = .333)
ALPHA = .05 ;
• The binomial (equiv p = .333) will produce a
test that the population proportion is .333 for
the first category. That is “No” for death. A Zvalue will be produced and probabilities for
one-tail and two-tailed tests.
• The exact keyword will produce confidence
intervals and, since I have specified alpha =
.05, these will be the 95% confidence
intervals.
Different data I had lying around
Hmmm…. This is interesting
Null rejected !