1 Introduction to data

Data basics, Overview of data collection principles, Observational studies and sampling strategies, Experiments, Examining numerical data, Considering categorical data, Graphical display of data

2 Foundation for inference

Hypothesis testing, Central Limit Theorem, Normal distribution, Applying the normal model, Confidence intervals

3 Inference for categorical data

Inference for a single proportion, Difference of two proportions, Testing for goodness of fit using chi-square, Testing for independence in two-way tables

4 Inference for numerical data

One-sample means with the t distribution, Paired data, Difference of two means, Comparing many means with ANOVA

5 Introduction to linear regression

Line fitting, residuals, and correlation, Fitting a line by least squares regression, Types of outliers in linear regression, Inference for linear regression

6 Multiple and logistic regression

Introduction to multiple regression, Model selection, Checking model assumptions using graphs, Logistic regression

7 Matrices and Vectors

Miscellaneous Definitions and Matrix Operations

8 Multivariate data

Descriptive Statistics, Rows (Subjects) vs. Columns (Variables), Covariances, Correlations and Distances, The Multivariate Normal Distribution, Scatterplots, More than 2 Variable Plots

9 Measures of Central Tendency, Dispersion and Association

Measures of Central Tendency, Measures of Dispersion, Measures of Association, Additional Measures of Dispersion

10 Unconstrained ordination

Principal Components Analysis, Correspondence analysis, Multi-dimensional scaling

11 Groups

Finding groups (Cluster Analysis), Testing groups (ANOSIM), Discriminating among groups (Discriminant analysis)

12 Constrained ordination

Canonical correspondence analysis

Diez D.M., C.D. Barr, M. Cetinkaya-Rundel. 2015. Open Intro Statistics. 3^rd edition

Crawley M.J. 2014. Statistics: An Introduction Using R. Wiley