In the Statistics diploma study program with a focus on data analysis, students learn the basic concepts and applications of parametric data analysis. The main focus is to analyze data from samples of research or experiment results using various statistical methods including correlation tests, regression analysis, T-tests, ANOVA tests, and multivariate analysis. First, students understand the basic concepts of parametric data analysis. They learn the basic principles, objectives, and relevance of parametric analysis in research and experiments. Next, students learn various methods of correlation tests, including Pearson correlation, partial correlation, and multiple correlation. They understand how to interpret correlation results and use these methods to measure relationships between variables. Then, in regression analysis, students understand the basic concepts and methods of regression analysis, including simple and multiple regression analysis. They learn how to model and interpret the relationship between variables using regression.

In the Statistics diploma program with a focus on data management and statistical analysis, students learn statistical concepts and applications using two popular statistical packages, SAS and R. The main focus is on data management, descriptive analysis, parameter estimation, hypothesis testing, correlation analysis, linear regression, and experiment design. First, students understand data management, including collecting, organizing, and managing data in the context of statistical analysis. They learn how to organize data, clean data, and prepare data for analysis. Next, in descriptive analysis, students learn techniques and methods to describe and visualize data using graphs and descriptive statistics. Then, students understand parameter estimation, which includes estimating population parameters from sample data. They learn the concept of confidence intervals and other estimation techniques. In addition, students understand hypothesis testing, which is an important tool in statistics. They learn different types of hypothesis tests and how to make decisions based on the test results.

In the Statistics diploma study program with a focus on sampling methods, students learn how to take samples that match the state of the population to ensure the data obtained is valid. The main focus is to explore several sampling methods, namely simple random sample, stratified random sample, systematic sample, simple cluster sample, and two-step cluster sample. First, students understand the basics of sampling and the importance of choosing the right sampling method according to the characteristics of the population. They understand the basic principles behind each sampling method. Next, students study the simple random sample, which is the basic method of random sampling from the population. They understand how to apply and analyze data generated from simple random samples. Then, students explore stratified random sampling, which divides the population into strata and draws a random sample from each stratum. They understand the advantages and disadvantages of this method and how to apply it in various contexts. In addition, students study systematic sampling, which is a sampling method with defined intervals. They understand how to select intervals and collect data accordingly.