Introduction to the course structure, diagnostic assessment, and goal setting.

Comprehensive coverage of descriptive statistics, including measures of central tendency, variability, and distribution.

Understanding and interpretation of graphical displays, such as histograms, box plots, scatterplots, and time series plots.

Practice with organizing and summarizing data using frequency distributions, relative frequencies, and cumulative frequencies.

Study of probability concepts, including basic probability rules, conditional probability, and probability distributions.

Analysis of discrete and continuous probability distributions, including binomial, geometric, normal, and uniform distributions.

Application of probability concepts to solve problems and make predictions in real-world scenarios.

Exploration of statistical inference techniques, including confidence intervals and hypothesis testing.

Understanding of sampling distributions, sampling methods, and sampling variability.

Practice with conducting hypothesis tests, including tests for means, proportions, and variances.

In-depth study of regression analysis, including simple linear regression and multiple regression.

Analysis of relationships between variables, including correlation coefficients and coefficient of determination.

Application of regression models to analyze trends, make predictions, and assess model fit.