Graduate Quantitative Methods Center

Resources on Statistical Analysis

GradQuant compiled the following statistical analysis resources below to help you learn statistical concepts and applications.

• Bayesian Statistics
  • “Plain English” Bayesian Statistics
  • Introduction to Bayesian Statistics by Brendon Brewer (open source textbook)
  • Bayesian Statistics –course notes
• Big Data Processing
  • For workshops, training sessions, and online tutorials offered by the High-Performance Computing Center at UCR on big data processing, please visit this page. 
  • Check out the Data Analysis in Genome Biology (GEN242) course offered by Dr. Thomas Girke at the High-Performance Computing Center at UCR. 
• Data Mining and Machine Learning
  • What is Machine Learning?
  • Introduction on Statistical Learning with Applications in R
  • Videos on Machine Learning Course in Caltech
  • Classification and Regression Trees
  • Cluster Analysis
  • Support Vector Machines
  • Multivariate Adaptive Regression Splines
  • Bagging and Ensemble Methods
  • First Steps with TensorFlow
  • Introduction to Deep Neural Networks
  • Convolutional Neural Networks
  • Performance Measures for Machine Learning
  • Improving Machine Learning

  Online Courses

  • Applied Data Mining and Statistical Learning, Penn State Online Course 
• Descriptive Statistics

  Numerical summaries, statistical graphics

  • How to visualize data
  • An introduction to descriptive statistics
  • Slides on Descriptive Statistics and Exploratory Data Analysis
  • A series of Videos on Descriptive Statistics

  SAS

  • Computing Descriptive Statistics in SAS
  • Descriptive Statistics – Categorical Variables

  R

  • Basic Descriptive Statistics in R
  • Exploring Data and Descriptive Statistics using R
  • Descriptive statistics with R

  SPSS

  • Using SPSS for Descriptive Statistics
  • Computing Descriptive Statistics in SPSS 
• Discrete Data Analysis
  • One-way Table: Chi-square Test of Goodness-of-Fit
  • One-way Table: G Test (LRT) of Goodness-of-Fit
  • Two-way Table:Chi-square Test of Independence
  • Two-way Table: G Test of Independence
  • Two-way Table: Fisher’s Exact Test
  • Two-way Table Dependent Samples: McNemar’s Test

  Online Courses:

  • Analysis of Discrete Data, Penn State Online Course 
• Econometrics Models
  • Econometrics Models page from the Econometrics Academy website: introductory videos that show how to implement procedures in Stata, R and SAS.
• Experimental Design

  ANOVA & ANCOVA

  • One-way ANOVA and Two-way ANOVA
  • Online Statbook: Analysis of Variance
  • Analysis of Covariance from online text book: Experimental Design for Behavioral and Social Sciences
  • Understanding Analysis of Covariance

  Repeated Measures

  • An tutorial on fitting Repeated Measures ANOVA using SPSS 

  Sample size determination

  • Determining the Sample Size
  • An online sample size calculator

  Teaching materials

  • Lecture notes for Introduction to Regression Models and Analysis of Variance
  • Lecture notes and R codes for Experimental Design and Data Analysis for Biologists 

  Resources on Causal Inference

  • Course on Causal Inference
  • Regression Discontinuity
  • Introduction to Mediation Analysis
  • Statistics and Causal Inference 

  Books available online:

  • Experimental Design for Behavioral and Social Sciences, by Howard J. Seltman
  • A First Course in Design and Analysis of Experiments, by Gary W. Oehlert 
• Generalized Linear Models (GLMs)

  Generalized Linear Model

  • Generalized Linear Model Theory

  Linear Mixed model

  • Linear Mixed model, appendix to An R and S-PLUS Companion to Applied Regression
  • Linear Mixed Effects Modeling using R

  Logistic Regression

  • Logit Models for Binary
  • General Linear Models for Binary Data R codes for exercise
  • Multinomial Response Models
  • Fitting and Interpreting a Proportional Odds Model

  Count Regression

  • Poisson Models for Count
  • General Linear Models for Count Data R codes for exercise
  • Negative Binomial Regression
  • Zero-Inflated Poisson Models
  • Getting Started with Hurdle Models
  • Do We Really Need Zero-Inflated Models? 
• Introductory Statistics
  • Intro to Statistics –Udacity free online course
  • Intro to Statistics –Saylor free online course
  • Introductory Statistics –textbook
  • Elementary Statistics and Probability Tutorials
• Linear Regression Models

  Linear regression and correlation

  • Introduction to Linear Regression and Correlation Analysis
  • Interpret Regression Analysis Results: P-values and Coefficients
  • Regression Analysis: How to Interpret the Constant
  • Interpreting Regression Coefficients

  Diagnostics and Transformations

  • Linear Regression Diagnostics
  • Box-Cox transformation 

  Multiple linear regression

  • Slides on Multiple Linear Regression
  • Multiple linear regression with R examples
  • Lecture slides on multiple linear regression

  Online Courses:

  • Linear regression, a Penn State University online course 

  • Statistical forecasting: Notes on regression and time series analysis

  Software: 

  • RegressIt, a free Excel add-in for linear and logistic regression. Visit here to learn more about RegressIt. 
• Longitudinal Data Analysis
  • Repeated Measures ANOVA
  • Longitudinal Data Analysis (SPSS)
  • Latent Growth Curve Models Lecture
  • Latent Growth Curve Modeling
  • Multilevel Growth Models
  • Generalized Estimating Equations
  • Nonlinear Trajectories
  • Introduction to Survival Analysis
  • Models for Survival Analysis with Covariates
  • Patterns In Time Series Analysis
  • Autoregressive Models
  • Introduction to ARIMA models
  • Seasonal ARIMA models
  • Growth Mixture Models (downloads PowerPoint file)
  • Latent Transition Analysis (downloads PowerPoint file)
• Miscellaneous Resources
  • UCLA Institute for Digital Research and Education
  • Statistics Subreddit
  • Stack Overflow
  • StatQuest with Josh Starmer - YouTube Channel: StatQuest breaks down complicated statistics and machine learning methods into small, bite-sized pieces that are easy to understand.
• Multivariate Analysis

  A guide to choose different statistical techniques of multivariate analysis

  MANOVA

  • A handout on MANOVA
  • Practical Guide to ANOVA and MANOVA for SAS

  Principle Component Analysis

  • A Tutorial on Principal Component Analysis
  • The Difference Between Principal Component Analysis and Factor Analysis
  • Linear Discriminant Analysis

  Factor Analysis

  • Lecture slides on factor analysis
  • What is Rotating in Exploratory Factor Analysis?
  • Exploratory and Confirmatory Factor Analysis
  • How Many Factors?

  Online Courses

  • Applied Multivariate Statistical Analysis, Penn State Online Course 
• Non-parametric Tests

  Non-Parametric Alternatives to Parametric Tests

  Type of Design	Parametric Test	Non-parametric Test
  Two Independent Samples	Independent-samples t-test	Mann-Whitney U or Wilcox /n Rank Sums Test
  Two Dependent Samples	Dependent-samples t-test	Wilcoxon T-test
  Three or more Independent Samples	Between-subjects ANOVA	Kruskal-Wallis H Test
  Three or more Dependent Samples	Within-subjects ANOVA	Friedman x2 Test

  Mann-Whitney U (Wilcoxon-Rank Sums Test) and Kruskal-Wallis (independent samples)

  Background information and tutorial

  • Mann-Whitney U and Kurskal-Wallis
  • Wilcoxon-Rank Sums only
  • Kruskal-Wallis Tutorial in R
  • Excel calculator (downloads Excel file)

  Wilcoxen Signed-Rank Test and Friedman x2 Test (dependent samples)

  Basic information and tutorials

  • Wilcoxon Signed-Rank Test
  • Friedman Test

  Basic R code for basic Non-parametric Testing

  • Nonparametric Tests of Group Differences

  Non-parametric regression

  • Nonparametric Inference

  Generalized Additive Modeling (nonparametric analog to GLMs)

  • Journal article on what additive models are
  • Background and basic information on additive modeling (1)
  • R code for additive modeling in R
  • R package information
• Omics Data
  • MetaboAnalyst – interfaces with R to analyze and display metabolomics data
  • MicobiomeAnalyst – analyze and display microbiome data
  • OmicsNet – create and explore relationships among genes, proteins, metabolites, etc.in 3-D space
• Open Source Textbooks
  • General Statistics Textbook
  • Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
  • Basic Probability Theory by Robert B. Ash
  • Probability: Theory and Examples by Rick Durrett
  • Experimental Design for Behavioral and Social Sciences
  • A First Course in Design and Analysis of Experiments
  • Introduction on Statistical Learning with Applications in R
• Probability Theory
  • Probabilities and random variables
  • Conditional probability
  • Distributions and densities
  • Expected value and variance
  • Law of Large Numbers
  • Central Limit Theorem

  Online Courses:

  • Probability Theory and Mathematical Statistics, Penn State Online Course

  Books available online:

  • Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
  • Basic Probability Theory by Robert B. Ash
  • Probability: Theory and Examples by Rick Durrett
• Sampling Theory and Methods
  • Sampling Theory and Methods, Penn State Online Course
• Structural Equation Modeling
  • What is a Latent Variable?
  • Overview of Structural Equation Modeling
  • Structural Equation Models with Latent Variables
  • Confirmatory Factor Analysis
  • Item Response Theory
  • Latent Class Analysis Mathematical Model
  • Latent Class Analysis Applied Example
  • Mixture Models
  • Multilevel Structural Equation Modeling