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.
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
Read more: —> One-Proportion Z-Test in R.
3 Two-proportions z-Test
Read more: —> Two Proportions Z-Test.
4 Chi-square goodness of fit test in R
Read more: —> Chi-square goodness of fit test in R.
5 Chi-Square test of independence in R
Read more: —> Chi-Square Test of Independence in R.
6 See also
This analysis has been performed using R statistical software (ver. 3.2.4).
Want to Learn More on R Programming and Data Science?
Follow us by Email On Social Networks:
Click to follow us on Facebook and Google+ :
Comment this article by clicking on "Discussion" button (top-right position of this page)
Sign up as a member and post news and articles on STHDA web site.
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