Other Content

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 F Test ( F Test_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.
Sx1, Sx2
= Sample standard deviations having n
1
N1 and n
2
N1 degrees of
freedom df, respectively.
F
=
F-statistic =
Lists&Spreadsheet Application 349