Online Statistics Tutoring Topics
Evaluating Data & Making Conjectures
Complementary & Mutually Exclusive Events
Predicting vs. Observing Probability
Accuracy and Scientific Notation
Describing and displaying data
Graphical displays: stemplots, histograms, boxplots,scatterplots.
Numerical Summaries: mean, median, quantiles, variance, standard deviation.
Normal Distributions: assessing normality, normal probability plots.
Categorical Data: two-way tables, bar graphs, segmented bar graphs.
Linear regression and correlation
Linear regression: least-squares, residuals, outliers and influential observations, extrapolation.
Correlation: correlation coefficient, r².
Inference in Linear Regression: confidence intervals for intercept and slope, significance tests, mean response and prediction intervals.
Multiple Linear Regression: confidence intervals, tests of significance, squared multiple correlation.
ANOVA for Regression: analysis of variance calculations for simple and multiple regression, F statistics.
Experiments and sampling
Experimental Design: experimentation, control, randomization, replication.
Sampling: simple, stratified, and multistage random sampling.
Sampling in Statistical Inference: sampling distributions, bias, variability.
Probability Models: components of probability models, basic rules of probability.
Conditional Probability: probabilities of intersections of events, Bayes’s formula.
Random variables: discrete, continuous, density functions.
Mean and Variance of Random Variables: definitions, properties.
Binomial Distributions: counts, proportions, normal approximation.