Introduction to Statistics: Understand the basic concepts of statistics, including data types (qualitative and quantitative), levels of measurement (nominal, ordinal, interval, ratio), and the role of statistics in decision-making.
Descriptive Statistics: Learn techniques for summarizing and presenting data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation).
Probability Theory: Study the fundamental principles of probability, including sample spaces, events, probability axioms, conditional probability, and independence of events.
Probability Distributions: Understand different probability distributions, such as the binomial distribution, normal distribution, and Poisson distribution. Learn about their properties and applications.
Sampling Theory: Familiarize yourself with sampling techniques and methods, including simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Understand the advantages and disadvantages of each method.
Statistical Inference: Learn about estimation and hypothesis testing. Understand how to estimate population parameters from sample data and make inferences about population characteristics.
Statistical Tests: Study common statistical tests such as t-tests, chi-square tests, and ANOVA (analysis of variance). Understand when and how to use these tests to compare means, proportions, and variances.
Correlation and Regression Analysis: Learn about the relationship between two variables using correlation analysis. Understand the principles of linear regression analysis and how to interpret regression coefficients.
Time Series Analysis: Study techniques for analyzing time series data, including trend analysis, seasonal variation, and forecasting methods.