**Categorical Data** are data that capture a characteristic of an experimental unit rather than a numerical value. There are different types of categorical data like nominal and ordinal. These data are distributed in various ways such as binomial, multinomial, and independent multinomial. Methods for estimation and making statistical inferences for categorical data includes approximation of the binomial distribution with a normal distribution, estimation and inference for one and two binomial samples, inference for 2 x 2 and R x C contingency tables, and estimation of sample size.

**We at ****rprogrammingassignmentexperts.com have ****Experts and Tutors who are committed and strive to provide the required guidance and assistance to the students in the best possible way. We cover everything which comes under Inference for Categorical Data; a few are listed as an example:**

- Hypothesis testing for two proportions
- Large sample framework for a difference in two proportions
- Simulating a difference under the null distribution
- Null distribution for the difference in two proportions
- Randomization for two-way tables and chi-square

- Inference for a single proportion
- Identifying when the sample proportion is nearly normal
- Confidence intervals for a proportion
- Hypothesis testing for a proportion
- Choosing a sample size when estimating a proportion

- Small sample hypothesis testing for a proportion
- Generating the null distribution and p-value by simulation
- Generating the exact null distribution and p-value
- Using simulation for goodness of fit tests

- Testing for goodness of fit using chi-square
- Creating a test statistic for one-way tables
- The chi-square test statistic
- The chi-square distribution and finding areas
- Finding a p-value for a chi-square distribution
- Evaluating goodness of fit for a distribution

- Testing for independence in two-way tables
- Expected counts in two-way tables
- The chi-square test for two-way tables