# Comparing Proportions in R

Previously, we described the essentials of R programming and provided quick start guides for importing data into **R**. Additionally, we described how to compute descriptive or summary statistics, correlation analysis, as well as, how to compare sample means and variances using R software.

This chapter contains articles describing

**statistical tests**to use for**comparing proportions**.# 1 How this chapter is organized?

- One-Proportion Z-Test in R: Compare an Observed Proportion to an Expected One
- Two Proportions Z-Test in R: Compare Two Observed Proportions
- Chi-Square Goodness of Fit Test in R: Compare Multiple Observed Proportions to Expected Probabilities
- Chi-Square Test of Independence in R: Evaluate The Association Between Two Categorical Variables

# 2 One-proportion z-Test

Compare an observed proportion to an expected one.

Read more: —> One-Proportion Z-Test in R.

# 4 Chi-square goodness of fit test in R

Compare multiple observed proportions to expected probabilities.

Read more: —> Chi-square goodness of fit test in R.

# 5 Chi-Square test of independence in R

Evaluate the association between two categorical variables.

Read more: —> Chi-Square Test of Independence in R.

# 6 See also

# 7 Infos

This analysis has been performed using **R statistical software** (ver. 3.2.4).

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**Articles contained by this category :**

Chi-square Goodness of Fit Test in R

Chi-Square Test of Independence in R

One-Proportion Z-Test in R

Two-Proportions Z-Test in R