Bivariate Quantitative Data: Scatter Plots and Linear Regression

Construct and interpret visual representations of quantitative data including histograms, dot plots, box plots, and cumulative frequency curves.

Calculate and interpret central tendency and spread parameters for populations and samples, analyzing the effects of linear data transformations.

Apply the fundamental counting principle, permutations, combinations, and sample space models to compute exact probabilities.

Solve multi-event probability problems using Venn diagrams, tree diagrams, two-way tables, conditional formulas, and risk metrics.

Define discrete probability mass functions, compute expected values and variances, and model Bernoulli/Binomial processes.

Analyze the normal distribution curve, compute z-scores, and calculate probabilities and percentiles using the standard normal model.

Analyze bivariate relationships, fit least-squares regression lines, interpret correlation coefficients, and examine residuals.

Critique statistical claims in media, differentiate correlation from causation, and evaluate sampling designs and inferential validity.