One of the main applications of R programming language is statistical analysis. R provides a variety of tools for statistical analysis, ranging from simple descriptive statistics to advanced modeling techniques. In this lesson, we will explore some of the most commonly used statistical analysis techniques in R.

One of the simplest forms of statistical analysis is computing descriptive statistics, such as mean, median, mode, variance, and standard deviation. R provides built-in functions for these calculations. For example, to compute the mean of a vector x, we use the function mean(x). Similarly, the functions median(x), sd(x), and var(x) compute the median, standard deviation, and variance of x, respectively.

Another common statistical analysis technique is hypothesis testing. In R, we can perform hypothesis testing using the t.test() function. This function performs a t-test, which is used to test the null hypothesis that the mean of a population is equal to a specified value. For example, to test whether the mean of a vector x is equal to 0, we use the code t.test(x, mu = 0).

Regression analysis is another important statistical analysis technique in R. R provides a variety of regression models, including linear regression, logistic regression, and Poisson regression. To fit a linear regression model in R, we use the lm() function. For example, to fit a linear regression model with response variable y and predictor variable x, we use the code model <- lm(y ~ x).

Finally, R provides a variety of functions for performing exploratory data analysis (EDA), such as scatter plots, box plots, and histograms. EDA is a critical step in statistical analysis, as it helps us identify patterns and relationships in the data. To create a scatter plot in R, we use the plot() function. For example, to create a scatter plot of x and y, we use the code plot(x, y).

To learn more about statistical analysis in R, we recommend the following resources:

• An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
• The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• R Graphics Cookbook, by Winston Chang
• R for Data Science, by Hadley Wickham and Garrett Grolemund

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