This course provides a comprehensive introduction to statistical concepts and methods essential for data analysis across diverse fields. The first part of the course establishes fundamental statistical principles, focusing on descriptive statistics, measures of association, probability concepts, and research design. Students will learn to summarize and visualize data, understand screening test characteristics, apply regression analysis, differentiate between experimental and observational studies, and develop foundational knowledge of probability and random variables.
The second part advances to inferential statistics, where students will master sampling distributions, confidence intervals, and hypothesis testing for both proportions and means. The course covers statistical analysis methods for paired data, regression inference, and categorical data analysis, with special attention to identifying and mitigating statistical biases. Through practical examples, students will develop the analytical skills necessary to interpret data accurately, evaluate research findings critically, and apply statistical reasoning to real-world problems.
Learning objectives
After the completion of this course, you will be able to:
- Calculate appropriate measures of association and interpret contingency tables to analyze relationships between categorical variables.
- Apply screening test concepts including sensitivity, specificity, and predictive values to evaluate diagnostic procedures.
- Summarize quantitative data using appropriate descriptive statistics and graphical methods to effectively compare distributions.
- Analyze data using linear regression and evaluate the validity of regression assumptions.
- Differentiate between various sampling methods and research designs, including experimental and observational studies.
- Apply probability concepts and rules to solve practical problems involving uncertainty.
- Construct confidence intervals and conduct hypothesis tests for proportions and means to make statistical inferences.
- Compare two populations using appropriate statistical inference methods for proportions, means, and paired data.
- Perform statistical analysis of categorical data and interpret the results accurately.
- Identify common statistical biases and apply strategies to mitigate their effects on research conclusions.
Course outline
- Introduction to Statistics
- Statistics Part I:
- Measures of Association
- Introduction to Statistics and Contingency Tables
- Screening Tests
- Summarizing Quantitative Variables and Comparing Distributions
- Standardizing Data and the Normal Distribution
- Linear Regression and Addressing Issues with Regression Assumptions
- Randomness and Survey Sampling
- Experiments and Observational Studies
- Introduction to Probability and General Rules of Probability
- Random Variables and Probability Models
- Statistics Part II:
- Sampling Distributions for Proportions and Means
- Confidence Intervals for Proportions and Testing Hypotheses about Proportions
- More about Hypothesis Tests and Comparing two Proportions
- Inference for Means and Comparing two Means
- Inference for Paired Data and Regression
- Categorical Data Analysis
- Statistical Biases
