## What is the difference between cross tabulation and chi-square?

Cross tabulation table (also known as contingency or crosstab table) is generated for each distinct value of a layer variable (optional) and contains counts and percentages. Chi-square test is used to check if the results of a cross tabulation are statistically significant.

**What are the assumptions for a chi-square test for homogeneity?**

In the test of homogeneity, we select random samples from each subgroup or population separately and collect data on a single categorical variable. The null hypothesis says that the distribution of the categorical variable is the same for each subgroup or population. Both tests use the same chi-square test statistic.

**What makes a chi-square test invalid?**

For example, you may have heard this one: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”. The correct statement is not about the observed in each cell. Those can be less than 5. It’s the EXPECTED count that needs to be >5 per cell*.

### How are crosstab tables and chi-square test used together when doing analysis of two variables?

Crosstabulation is a statistical technique used to display a breakdown of the data by these two variables (that is, it is a table that has displays the frequency of different majors broken down by gender). The Pearson chi-square test essentially tells us whether the results of a crosstab are statistically significant.

**How does a crosstabs with chi-square analysis function?**

A cross tabulation displays the joint frequency of data values based on two or more categorical variables. The joint frequency data can be analyzed with the chi-square statistic to evaluate whether the variables are associated or independent. This table shows frequency counts for each production line and shift.

**How are crosstab tables and chi square test used together when doing analysis of two variables?**

## What are the conditions for applying chi-square test?

Conditions for Applying Chi- square Test: 1. Each of the observation making up the sample of this test should be independent of each other. 2. The expected frequency of any item should not be less than 5.

**What are the conditions that needed to be met to apply chi-square test for association?**

Your data must meet the following requirements:

- Two categorical variables.
- Two or more categories (groups) for each variable.
- Independence of observations. There is no relationship between the subjects in each group.
- Relatively large sample size. Expected frequencies for each cell are at least 1.

**How do you cross tab in Minitab?**

To analyze this data, you choose Stat > Tables > Cross Tabulation and Chi-Square in Minitab. Minitab asks you select the variable that will correspond to the table’s rows and the table’s columns. We’ll choose “Gender” for rows and “Affiliation” for columns.