Publications by Kitada Smalley
DATA252: Variable Selection and Regularization
Learning Objectives In this lesson students will learn how to implement… Best Subset Selection Stepwise Selection (Backward and Forward) Ridge Regression LASSO Regression Ex: Health and Biostatistics Use the following data for these examples: library(tidyverse) #install.packages("faraway") library(faraway) set.seed(1212) data("fat") head(fat)...
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BONUS: Time Series
Learning Objectives In this lesson students will learn how the basics of time series… What is a time series? Why and when should we use time series? How do you decompose a time series? Motivation Last class we saw how polynomial regression was used for model COVID-19 data in 2020; however, this model is inherently flawed because it assumes inde...
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DATA252: Beyond Linearity
Learning Objectives In this lesson students will learn how to… Fit non-linear models (polynomial, gam, and loess) Choose the appropriate amount of curvature using p-values Choose hyper parameters via testing and training 0. Import the Data During the COVID-19 Pandemic several researchers published work on estimating/forecasting the number of ca...
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DATA252: Multiple Linear Regression
Learning Objectives In this lesson students will learn how to… Fit a multiple linear regression model Create graphics to explore relationships between two variables Engineer features 0. Import the Data These data for `insurance’ charges come from the “US Health Insurance Dataset” on Kaggle. Souce: https://www.kaggle.com/datasets/teertha...
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DATA252: Model Fitting
Learning Objectives Students will learn how to use R to generate random variables and become acquainted with the trade-offs inherent in models building. Generating Random Variables Normal Distribution ## using functions built into R ## generating random variables ?rnorm() ## parameters # mean mu<-0 # sd sigma<-1 ## arguments # n rnorm(n=50, m...
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Linear Model Optimization
Shiny applications not supported in static R Markdown documents ...
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Processing Data in Tidyverse
Learning Objectives Students will learn how to work with real data to prepare it to perform machine learning functions using the tidyverse. Importing Data ### use raw file from github laptop_price <- read.csv("https://raw.githubusercontent.com/kitadasmalley/DATA252/main/Data/laptop_price.csv") Looking at Data Structure ## LOOK AT THE DATA ### s...
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DATA252: Laptop SLR
Learning Objectives In this lesson students will review the main concepts of Simple Linear Regression. How to fit a model Graphics a model Checking model fit and diagnostics Model inference Import the Data These laptop price data come from Kaggle. ### use raw file from github laptop_price <- read.csv("https://raw.githubusercontent.com/kitadasma...
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Introduction to Working with Data in R
Learning Objectives: In this session students will learn the basics of the R programming environment and how to work with data sets through an experimental design case study. How to install a package: install.packages() How to call a library: library() How to use a data set built into R: data() Where to find help with R functions and object: hel...
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DATA151: Basics of Programming
Learning Objective: In this session students will learn how to create an R markdown document that basics of programming through a case study of creating a randomized experiment. How to create an R Markdown document: .Rmd Creating headers at different levels to organize and navigate the document Creating and working with code chunks Basics of p...
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