Course overview
Introductory statistics builds a full workflow: collect data carefully, describe patterns, model chance, and draw justified conclusions from samples. You will move from descriptive tools to inference for means and proportions, then finish with relationship modeling and group comparison.
Syllabus
- 01→
Sampling and Data
Define populations, samples, variables, and study designs while identifying selection and measurement bias.
- 02→
Descriptive Statistics
Summarize data with tables, graphs, center, and spread to describe distribution shape and unusual values.
- 03→
Probability Foundations
Use sample spaces, probability rules, and counting logic to quantify chance in repeatable random processes.
- 04→
Discrete Random Variables
Define discrete random variables, probability distributions, expected value, and variance, including binomial and geometric models.
- 05→
Continuous and Normal Models
Work with density curves and normal models to compute and interpret probabilities for continuous variables.
- 06→
Central Limit Theorem
Understand sampling distributions and use the central limit theorem to model sample means and sample proportions.
- 07→
Confidence Intervals
Construct and interpret confidence intervals for means and proportions, including margin of error and confidence level tradeoffs.
- 08→
One-Sample Hypothesis Tests
Set up null and alternative hypotheses, compute test statistics and p-values, and make one-sample decisions for means and proportions.
- 09→
Two-Sample and Chi-Square Inference
Compare two groups with two-sample methods and analyze categorical associations and fit with chi-square procedures.
- 10→
Regression, Correlation, and Intro ANOVA
Model quantitative relationships with correlation and linear regression, then learn the purpose and structure of one-way ANOVA.