Skip to content

Statistics & Probability

A complete introductory statistics and probability course — from organizing raw data all the way to hypothesis testing, chi-square, t-tests, and regression. Every topic is worked three ways: Excel first (formulas + Analysis ToolPak), then R, then Python — so the same idea is visible in the tool you actually have open.

Notes

Exercises

Quiz Hub

Projects

Resources

Learning Flow

  1. Read the topic notes for the current chapter — concept, formula, worked example by hand.
  2. Reproduce the same result in Excel, then R, then Python.
  3. Work through the matching exercise set (every question has a full solution).
  4. Test yourself in the quiz hub.
  5. Build the chapter's project to apply the concept end to end.

Chapters

# Chapter Covers Start
01 Foundations of Data Population vs. sample, variable types, levels of measurement, tidy data Statistics Basics
02 Frequency Distributions Class intervals, relative & cumulative frequency, histograms, ogives Frequency Distributions
03 Central Tendency Mean, median, mode, weighted mean, grouped-data mean Mean, Median & Mode
04 Variation & Position Range, variance, SD, CV, z-scores, quartiles, IQR, outliers Measures of Variation
05 Probability Sample spaces, addition & multiplication rules, conditional, Bayes, counting Probability Basics
06 Discrete Distributions Random variables, expected value, binomial, Poisson, geometric Random Variables
07 Continuous & Normal Uniform, exponential, normal curve, standard normal, z-scores Uniform & Exponential
08 Sampling Theory Sampling designs, bias, sampling distributions, Central Limit Theorem Sampling Methods
09 Confidence Intervals Estimating a mean and a proportion, margin of error, sample size CI for a Mean
10 Hypothesis Testing Null/alternative, Type I & II errors, p-values, one-sample z-tests Testing Fundamentals
11 t-Tests One-sample, two independent samples, paired samples One-Sample t-Test
12 Chi-Square & ANOVA Goodness-of-fit, independence, homogeneity, one-way ANOVA Goodness-of-Fit
13 Correlation & Regression Scatterplots, Pearson r, least squares, prediction, inference on slope Correlation

Three Tools, One Idea

Excel R Python
Role in this course Primary — every formula is shown as a worksheet function first Primary — the statistician's language, one-line tests with full output Supporting — scripted, reproducible analysis
Descriptive stats AVERAGE, MEDIAN, STDEV.S, Data Analysis → Descriptive Statistics summary(), psych::describe() df.describe()
t-test T.TEST, Data Analysis → t-Test t.test() scipy.stats.ttest_1samp
Chi-square CHISQ.TEST, CHISQ.INV.RT chisq.test() scipy.stats.chi2_contingency
Regression LINEST, SLOPE, Data Analysis → Regression lm() statsmodels.api.OLS

Companion courses: Data Analytics — Excel · Python — Data Analytics