Chi-Square Test for Population Variances |
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Introduction
The aim of this test is to compare a population variance, , to a value,
, referred here to as the hypothetical variance of the test, based on a random sample,
, of size
, from the population in question.
Method
If the population from which the sample is drawn is normally distributed, then the test statistic
(1)
is chi-square distributed with degrees of freedom.
To test the sample variance, , against the hypothetical variance of the test,
, we calculate the test statistic
,
and find the likelihood that is equal to
.
Refer to Statistical Inference, Decision Theory and Hypothesis Testing for additional information on this subject.
Hypotheses
The null hypothesis takes the form , with the following alternative hypotheses:
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§ | ![]() |
§ | ![]() |
Refer to Alternative Hypothesis for additional information of this subject.
Output Format
One-Sample Chi-Square Test for Variances
Sample: |
<The Sample Number> |
Observations: |
<Observations in the Sample> |
Hypothetical Variance: |
<The Hypothetical Variance> |
Significance Level: |
<The Significance Level> |
Mean: |
<The Sample Mean> |
Sample Variance: |
<The Sample Variance> |
Chi-Square (X2): |
<X2> |
Degrees of Freedom: |
<Number of Degrees of Freedom> |
Left-Tailed P(X2): |
<The Left-Tailed P-Value of X2> |
Left-Tailed X2 Critical: |
<The Left-Tailed Critical Point> |
Left-Tailed Test: |
"[Do not ]Reject Ho" |
Right-Tailed P(X2): |
<The Right-Tailed P-Value of X2> |
Right-Tailed X2 Critical: |
<The Right-Tailed Critical Point> |
Right-Tailed Test: |
"[Do not ]Reject Ho" |
Two-Tailed (Left) 2P(X2): |
<The Two-Tailed Left P-Value of X2, doubled> |
Two-Tailed (Right) 2P(X2): |
<The Two-Tailed Right P-Value of X2, doubled> |
Two-Tailed (Left) X2 Critical: |
<The Two-Tailed Left Critical Point> |
Two-Tailed (Right) X2 Critical: |
<The Two-Tailed Right Critical Point> |
Two-Tailed Test: |
"[Do not ]Reject Ho" |