What is Chi Square?
Chi square statistic is a method of
showing relationship between two categorical variables. In statistic, there are
two types of variables which is the numerical and non-numerical variables. The chi
squared statistic is a single number that tells you how much difference exists
between your observed counts you would expect if there were no relationship at
all in the population.
chi square formula
There are a few variations on the chi
square statistic. However, all of the variations use the same idea, which is
that you are comparing your expected values with the values you actually
collect. One of the most common forms can be used for contingency tables.
Chi square test consists of two types
which is the chi square goodness of fit test and chi square test for
independence. In this section, we will focus on the chi square goodness of fit
test. The purpose of this test was to determines if a sample data matches a population.
This test is applied when there is one categorical variable from a single
population. It is used to determine whether sample data are consistent with a
hypothesized distribution.
example
This test is appropriate when those
conditions are met:
-
The sampling
method is simple random sampling.
-
The variable
under study is categorical.
-
The expected
value of the number of sample observation in each level of the variable is at
least 5.
Step in Chi Square
1)
State
the hypothesis
2)
Formulate
an analysis plan
3)
Analyze
sample data
4)
Interpret
results
Homogeneity of Proportions
The test were used to a single
categorical variable from two or more different populations. It is used to
determine whether frequency counts are distributed identically across different
populations.
This test also will be used when:
-
In the
population, the sampling method is simple random sampling.
-
The variable
is categorical.
-
Data
displayed in contingency table showed that the expected frequency count for
each cell is less than 5.
The steps and procedures are also the same
as the usual chi square test.
By : Hanis Jefry
References :
http://www.statisticshowto.com/probability-and-statistics/chi-square/
http://math.hws.edu/javamath/ryan/ChiSquare.html
http://www.statisticssolutions.com/non-parametric-analysis-chi-square/
http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm
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