Hypothesis Testing (Chi-Squared Test) (G Dataflow)

Randomly sampled data from the population.

Hypothesized variance of the population.

The null hypothesis is that the population variance is equal to variance.

Default: 1

Probability that this node incorrectly rejects a true null hypothesis.

Default: 0.05

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

Hypothesis to accept if this node rejects the null hypothesis that the population variance is equal to variance.

Default: variance(pop) != variance

A Boolean that indicates whether this node rejects the null hypothesis.

Smallest significance level that leads to rejection of the null hypothesis based on the sample sets.

Lower and upper limits for the population variance. confidence interval indicates the uncertainty in the estimate of the true population variance.

low

Lower limit of the estimate of the population variance.

high

Upper limit of the estimate of the population variance.

Sample statistics of the chi-squared test.

sample variance

Variance of sample set.

degree of freedom

Degree of freedom of the chi-squared distribution that the test statistic follows.

sample chi-squared value

Sample test statistic used in the chi-squared test.

sample chi-squared value is equal to $\frac{(n-1)*\mathrm{sample\; variance}}{\mathrm{variance}}$.

chi-squared critical value (lower)

Lower chi-squared value that corresponds to significance level and alternative hypothesis.

Algorithm for Calculating chi-squared critical value (lower)

Let X _n represent a chi-squared distributed variate with n degrees of freedom. chi-squared critical value (lower) satisfies the following equations based on the value of alternative hypothesis.

alternative hypothesis	chi-squared critical value (lower)
variance(pop) != variance	Prob{X n < chi-squared critical value (lower)} = significance level / 2
variance(pop) > variance	chi-squared critical value (lower) = NaN
variance(pop) < variance	Prob{X n < chi-squared critical value (lower)} = significance level

chi-squared critical value (upper)

Upper chi-squared value that corresponds to significance level and alternative hypothesis.

Algorithm for Calculating chi-squared critical value (upper)

Let X _n represent a chi-squared distributed variate with n degrees of freedom. chi-squared critical value (upper) satisfies the following equations based on the value of alternative hypothesis.

alternative hypothesis	chi-squared critical value (upper)
variance(pop) != variance	Prob{X n > chi-squared critical value (upper)} = significance level / 2
variance(pop) > variance	Prob{X n > chi-squared critical value (upper)} = significance level.
variance(pop) < variance	chi-squared critical value (upper) = NaN

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.