MET MA 603: SAS Programming and Applications
MET MA 603:
SAS Programming and Applications
Proc Corr
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In statistics, correlation measures the strength of the linear relationship between two variables.
Strong correlation indicates that the value of one variable is a good predictor of the value of the other.
Below is one way of describing the strength of correlations. Except for Perfect Correlation and Perfectly Uncorrelated, these are all subjective definitions, and do not need to be followed or memorized. Use only as a guide.
Statistical Correlation
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The Correlation Procedure calculates the correlation between pairs of numeric variables in a SAS dataset.
proc corr data = data3.golf_total ;
run;
The Correlation Procedure
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By default, all numeric variables are included in the analysis. The VAR statement is used to list the variables to include in the analysis.
By default, all pairs of correlations are calculated and displayed in a 2-way table. The WITH Statement is used to specify the variables that appear down the side of the table.
The PLOTS Option creates plots in order to help visualize the relationship between the variables.
proc corr data = data3.golf_total plots=matrix(histogram) ;
var hole1 – hole3 ;
with total_score ;
run;
The Correlation Procedure (cont.)
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Practice
There is a theory in politics that when the Washington football team wins their last game before the election, that the incumbent party (i.e., the party in power) wins the election, and when Washington loses, the incumbent party loses. The dataset election.sas7bdat has the results from 1932 through 2016.
First use Proc Freq to check the data. Address any issues with the data. Then use Proc Corr to test the correlation between Washington’s result and the election result.
Do you think this is a good theory?
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Readings
Textbook section 9.8
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RangeStandardRangeStandard
0Perfectly Uncorrelated
0 to 0.1No or Weak Correlation-0.1 to 0No or Weak Negative Correlation
0.1 to 0.3Weak Correlation-0.3 to -0.1Weak Negative Correlation
0.3 to 0.5Moderate Correlation-0.5 to -0.3Moderate Negative Correlation
0.5 to 1Strong Correlation-1 to -0.5Strong Negative Correlation
1Perfect Correlatation-1Perfect Negative Correlation
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