Lectures – UMD Biostatistics

Welcome to the course lectures. Click on any card below to view the slides and materials for that topic, or use the search bar to find a specific lecture.

How an ANOVA IS A REGRESSION WIHT DUMMY VARIABLES

This document demonstrates how Analysis of Variance (ANOVA) is mathematically equivalent to a regression model with dummy variables using an example with R code and…

Lecture 01

Zar (1999) - “analysis and interpretation of data with view towards objective evaluation of conclusions based on the data”

Lecture 02

Lecture 03

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Lecture 04: Probability and Inference

Let’s explore the Arctic grayling data from lakes I3 and I8. Use the grayling_df data frame to create basic summary statistics.

Lecture 05: Probability and Statistical Inference

Lecture 06

Lecture 07

we will always cover parametric tests

Lecture 08

Lecture 09 Correlation and Regression

Lecture 10 - Multiple Regression

Lecture 11 - Single factor analysis of variance - ANOVA

Analysis of variance: single and to come multi-factor designs

Lecture 12 - Factorial ANOVA of Penguin Mass Balanced

a low light and high light treatment might affect biomass

Lecture 13 - Nested ANOVA

Lecture 14 - Generalized Linear Models

Homogeneity of variance - Levenes or Bartletts Test

Lecture 15 - ANCOVA

Lecture 17 - Principal Component Analysis (PCA)

Eigenvectors, eigenvalues and components

Lecture 18 - Multivariate Community Analysis

instead of trying to preserve exact distances or maximize variance explained

Lecture 19 - Logistic Regression

Logistic regression is used when: - The response variable is binary (yes/no, 1/0, present/absent) - Data follows a binomial distribution (not normal) - We want to model the p…

Principal Component Analysis (PCA)

Darlingtonia californica (Cobra Lily)