R Hypothesis Testing & Confidence Intervals

In R language, various functions and packages can be used for hypothesis testing and confidence interval estimation. Here are some commonly used methods:

Hypothesis testing:

1. T-test: Conduct a t-test using the t.test() function. For example, to perform an independent sample t-test on two groups of data, you can use the following code:
   t.test(x, y, alternative = “two.sided”)

   Here, x and y are the two sets of data, and the alternative parameter indicates a two-sided test.

2. Performing analysis of variance (ANOVA): Conduct ANOVA using the aov() function. For instance, to analyze variance among multiple groups, you can use the following code:
   aov_model <- aov(response ~ group, data = df) summary(aov_model)

2. Confidence interval estimation:

1. For estimating the confidence interval of the mean, the t.test() function can be used. For example, to estimate the 95% confidence interval of the mean for a set of data, the following code can be used:
   t.test(x, conf.level = 0.95)$conf.int
2. To estimate the confidence interval for a proportion, you can use the prop.test() function. For example, to estimate a 95% confidence interval for a proportion in a dataset, you can use the following code:
   prop.test(x, n, conf.level = 0.95)$conf.int

The above are some commonly used methods, and specific functions and parameters can be selected as needed for hypothesis testing and confidence interval estimation.