Tagged: reference

Confidence intervals and hypothesis tests comparing two means, two proportions, paired data, and two variances.

Critical values of the t distribution for common significance levels and degrees of freedom 1–120.

Cumulative probabilities P(Z ≤ z) for the standard normal distribution, z from 0.0 to 3.4.

One-way and two-way analysis of variance: sum of squares, mean squares, and F-statistics.

One-sample confidence intervals for means, proportions, and variance, plus sample size formulas.

Test statistics and decision rules for one-sample z, t, and chi-square hypothesis tests.

Greek alphabet with uppercase and lowercase forms and their conventional uses in statistics.

Standardization, the Central Limit Theorem, and sampling distributions of the mean.

Probability rules, counting methods, and discrete distributions including binomial and Poisson.

Pearson correlation, least-squares regression, inference for regression coefficients, and chi-square tests.

Critical values of the chi-square distribution for upper and lower tail probabilities, degrees of freedom 1–30.

Critical values of the F distribution at α = 0.05 and α = 0.01, for numerator df 1–10 and common denominator df.

Normal approximation (Wald) and Wilson score intervals for a population proportion, with conditions, sample size formulas, and when the approximation breaks down.

Formulas for central tendency, variability, z-scores, and outlier detection.

How to calculate required sample size for a two-sample t-test and a chi-square test, given a target effect size, significance level, and power.

Formulas and decision rules for Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, and Spearman rank correlation: the nonparametric alternatives to common parametric tests.

Tukey's HSD, Bonferroni correction, and Scheffé's method for identifying which group pairs differ after a significant ANOVA.

Formulas, degrees of freedom, and assumptions for the chi-square test for independence and the chi-square goodness-of-fit test, with worked examples.

The multiple linear regression model, coefficient interpretation, R-squared and adjusted R-squared, the F-test, multicollinearity, and the VIF.

A structured decision guide for selecting the right statistical test. Covers one-sample, two-sample, multi-group, and association tests for parametric and nonparametric situations.