# How to conduct statistical inference in the R language?

In R language, statistical inference typically involves using statistical models and hypothesis testing to analyze data and draw conclusions. Here are some common statistical inference methods:

- Parameter estimation: using sample data to estimate the values of population parameters, such as the mean, standard deviation, proportion, etc. Common methods include maximum likelihood estimation and least squares estimation.
- Hypothesis testing is a method used to test hypotheses about population parameters. Common hypothesis testing methods include t-tests, ANOVA analysis, and chi-square tests.
- Confidence interval estimation: used to estimate the uncertainty range of population parameters, such as a 95% confidence interval.
- Analysis of variance (ANOVA): a method used to compare whether the means of multiple populations are equal, commonly used for handling multiple samples.
- Logistic regression: used to investigate the relationship between independent variables and dependent variables, suitable for binary or multiclass problems.

To conduct statistical inference analysis, statistical analysis packages in R such as stats, lme4, and car can be used. Data processing, model building, hypothesis testing, and other operations can be performed by writing R code. Additionally, visualization packages in R such as ggplot2 and lattice can be utilized to display the results of statistical inference.