Publications by anangwe
Motivation for Generalized Linear Models
Notes 1 Motivation for Generalized Linear Models Notes 1 Reading companion Three common non-normal situations Erick Anangwe You will have to install the package GLMsData just once: Tools > Install Packages library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.3 ## ── Attaching core tidyverse packages ─�...
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Multiple Linear Regression
Activity03 - Multiple Linear Regression Activity03 - Multiple Linear Regression Part 1 Load the necessary packages Load the data Pairwise relationships The multiple linear regression model Challenge: 3-D plots Part 2 Fitting the overall model Challenge: Multiple levels Part 3 Fitting the overall mod...
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Simple Linear Regression
Activity02 - Simple Linear Regression Activity02 - Simple Linear Regression A typical modeling process Part 1 Part 2 Attribution Erick Anangwe 05-26-2024 A typical modeling process The process that we will use for this activity is: Identify our research question(s), Explore (graphically and with numerical summaries) the variables of...
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Multinomial Logistic Regression
Activity06 - Multinomial Logistic Regression Activity06 - Multinomial Logistic Regression The Data Fitting the model Predicting Erick Anangwe 07-04-2024 The Data Today we will analyze data from an online Ipsos (a consulting firm) survey that was conducted for a \(\texttt{FiveThirthyEight}\) article Why Many Americans Don’t Vote. You c...
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Logistic Regression
Activity06 - Logistic Regression Activity06 - Logistic Regression Load the necessary packages Load the data Probability and odds Logistic regression Challenge: Extending to Mulitple Logistic Regression Relationship Exploration Fitting the model Assessing model fit Erick Anangwe 06-26-2024 Load the necessary packag...
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Bootstrapping
Activity09 - Bootstrapping Erick Anangwe 07-13-2024 Task 2: Load the necessary packages Task 3: Create the data # Set a random seed value so we can obtain the same "random" results set.seed(2023) # Create a data frame/tibble named sim_dat sim_dat <- tibble( # Explain what next line is doing x1 = runif(20, -5, 5), # Explain w...
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Statistical Modeling and Regression (STA 631) Portfolio Project
STA-631-Portfolio-Project STA-631-Portfolio-Project Introduction Course Objectives Data Analysis Load necessary packages Load and explore the dataset Exploratory Data Analysis (EDA) Objective 1: Probability and Inference Objective 2: Generalized Linear Models Objective 3: Model Selection Objecti...
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Fundamentals of Functions in R
Fundamentals of Functions in R Fundamentals of Functions in R Learning Objectives Basics of functions Best practices Conditional execution, warnings, and errors The ifelse() function Logical operators Erick Anangwe 2024-03-21 Learning Objectives Create custom R functions Create conditional statements Use warning and err...
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Manipulating Dates with lubridate in R
Manipulating Dates with lubridate in R Learning Objectives Coercing strings to date / time values Create a date time variable using date time components Extracting date / time components Calculating difference between date / times Erick Anangwe 2024-03-14 Manipulating Dates with lubridate in R Lear...
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Inference of Population Proportions & Categorical Variables
Inference of Population Proportions & Categorical Variables Learning Objectives Stating the hypothesis for χ2-test of independence Next, we implement the X2-test of independence Printing the results of the X2-test of independence State and check assumptions of the X2-test of independence Int...
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