## 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

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.