Introductory Statistics: A Slacker's Guide

Explore descriptive statistics by distinguishing numerical and categorical data, and by comparing discrete and continuous measurements, then learn to transform data into information through clear representations.

Explore the center of data by computing the mean, median, and mode, and learn how quartiles, quintiles, and percentiles reveal data location and variation.

Explore data dispersion by measuring range, interquartile range, standard deviation, and variance. Compare data patterns using the coefficient of variation, skewness, and z-scores to standardize data.

Represent data with the five-number summary and box plots, and explore skewness and distribution types. Visualize relationships using contingency tables, frequency distributions, and scatterplots for two variables.

Explore the relationship between populations and samples and how we know without knowing. Formalize the normal distribution, its applications, and the sampling distribution as the tool for statistical inference.

Learn how populations and samples drive statistics, define population versus sample, and compare non-probability and probability sampling with a focus on simple random sampling.

Discover the normal distribution, a continuous probability distribution that concentrates around the mean, explains standard deviation and z-scores, uses area under the curve to assess probabilities, and identifies outliers.

Master reading the z-table and converting z-scores to probabilities in the standard normal distribution. Understand shading, symmetry, and how left and right areas relate to each other.

Learn how the sampling distribution explains why sample means estimate the population mean, and how the central limit theorem and standard error quantify uncertainty in inference.

Learn to infer population parameters from samples using uncertainty, interval estimates, and t-distribution hypothesis testing. Compare one-sample and two-sample tests, proportions, and the role of sample size.

Learn how to quantify uncertainty with sampling distributions, construct confidence intervals around a point estimate, and understand how sample size and margins of error shape inference about population parameters.

Explore hypothesis testing with null and alternative hypotheses, using z-tests to compare sample means to population parameters. Learn about significance levels, p-values, confidence intervals, and error types.

Explore the one sample t-test, comparing sample means to a hypothesized value using the t distribution, degrees of freedom, and sample standard deviation for small samples.

Learn how to compare two populations using two-sample tests by formulating null and alternative hypotheses, computing the difference of means, and choosing Z or T tests with appropriate standard errors.

Learn to infer population proportions using the one-sample z test, comparing the sample proportion to P, understanding standard error and confidence intervals, and applying two-sample proportion tests with pooled P-bar.

Determine the needed sample size by balancing precision, confidence, and variability. Use margin of error, sigma, and desired confidence to compute n, with means and proportions as examples.

Explore correlation and regression to understand relationships between multiple variables. Learn how to quantify the extent of a two-variable relationship and get an introduction to simple linear regression.

Explore linear correlation, interpret r, and visualize relationships with scatterplots, including positive, negative, and no linear correlation, and learn about r-squared and regression.

Explore simple linear regression, predicting an outcome from a single predictor, interpret slope and intercept, assess fit with R-squared, and understand residuals and significance.