Application Guide

1-Prop z Test tests the null hypothesis H
0
:prop=p
0
against one of the alternatives
below.
• H
a
: propƒp
0
• H
a
: prop<p
0
• H
a
: prop>p
0
This test is useful in determining if the probability of the success seen in a sample is
significantly different from the probability of the population or if it is due to sampling
error, deviation, or other factors.
2-Prop z Test (zTest_2Prop)
Computes a test to compare the proportion of successes (p
1
and p
2
) from two
populations. It takes as input the count of successes in each sample (x
1
and x
2
) and
the count of observations in each sample (n
1
and n
2
). 2-Prop z Test tests the null
hypothesis H
0
:p
1
=p
2
(using the pooled sample proportion Ç) against one of the
alternatives below.
• H
a
: p
1
ƒp
2
• H
a
: p
1
<p
2
• H
a
: p
1
>p
2
This test is useful in determining if the probability of success seen in two samples is
equal.
c
2
GOF (c
2
GOF)
Performs a test to confirm that sample data is from a population that conforms to a
specified distribution. For example, c
2
GOF can confirm that the sample data came
from a normal distribution.
c
2
2-way Test (c
2
2way)
Computes a chi-square test for association on the two-way table of counts in the
specified Observed matrix. The null hypothesis H
0
for a two-way table is: no
association exists between row variables and column variables. The alternative
hypothesis is: the variables are related.
2-Sample FTest (FTest_2Samp)
Computes an F-test to compare two normal population standard deviations (s
1
and
s
2
). The population means and standard deviations are all unknown. 2-Sample FTest,
which uses the ratio of sample variances Sx1
2
/Sx2
2
, tests the null hypothesis
H
0
:s
1
=s
2
against one of the alternatives below.
• H
a
: s
1
ƒs
2
• H
a
: s
1
<s
2
• H
a
: s
1
>s
2
Below is the definition for the 2-SampleFTest.
Lists&Spreadsheet Application 339