Publications by Kitada Smalley
DATA151: Regression (Take a Hike)
Learning Objectives: Students will learn to create scatterplots and describe the relationships between two numeric variables. Example: Take A Hike! STEP 1: Load the data: body_wgt<-c(120, 187, 109, 103, 131, 165, 158, 116) backpack_wgt<-c(26, 30, 26, 24, 29, 35, 31, 28) backpack_df<-data.frame(body_wgt, backpack_wgt) Which variable should be t...
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DATA151: Introduction to Regression Models
Learning Objectives Students will learn how to use linear regression to model relationships between variables and also explore subgroups. Example 1: Climate Change and Fish Habitats As the climate grows warmer, we expect many animal species to move toward the poles in an attempt to maintain their preferred temperature range. Do data on fish in t...
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Advanced Logistic Models
Part 1: Logistic Regression: Probit vs Logit install.packages("titanic", repos = "http://cran.us.r-project.org") ## ## The downloaded binary packages are in ## /var/folders/cc/1pvgy4s11875jgp6h1fml9wr0000gn/T//Rtmp1ZBJsG/downloaded_packages library(tidyverse) library(titanic) # LINEAR MODEL lm_t=lm(Survived ~ Fare, data=titanic_train) ggplot(...
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Help for Sam
nrgcen<-read.csv("/Users/hsmalley/Downloads/Energy Census and Economic Data US 2010-2014.csv", header=TRUE) #head(nrgcen) # at state level # cols for years # might be useful but need to find wanted cols first unemp<-read.csv("/Users/hsmalley/Downloads/unemployment-by-county-us/output.csv", header=TRUE) #head(unem...
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Critical Thinking: MLR Model Extensions
Part I: Numeric Interactions In the past, we have looked at the Boston dataset to explore polynomial regression. We are going to use this dataset for Part I. You will need to load it into this lab session from library MASS. library(MASS) library(ISLR) names(Boston) ## [1] "crim" "zn" "indus" "chas" "nox" "rm" "age" ## ...
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SP20: MATH 239 - MLR Geometry
Exploring the Geometry of Multiple Linear Regression PART I: Warm-Up Example: Predicting Book Weights for Amazon Shipping When you buy a book off of Amazon, you get a quote for how much it costs to ship. This is based on the weight of the book. If you didn’t know the weight a book, what other characteristics of it could you measure to help pr...
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SP20: MATH 239 - Categorical Explanatory Variables
Sales of Child Car Seats We’ll be using the Carseat data set in the ISLR package. Its a simulated dataset about the sales of car seats at different stores. library(ISLR) data(Carseats) names(Carseats) ## [1] "Sales" "CompPrice" "Income" "Advertising" "Population" ## [6] "Price" "ShelveLoc" "Age" "Education" "Ur...
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7A: Adding Curvature
Polynomial Regression ###### library(MASS) ## Warning: package 'MASS' was built under R version 3.6.2 data(Boston) dim(Boston) ## [1] 506 14 mod5<-lm(medv~poly(lstat,5), data=Boston) summary(mod5) ## ## Call: ## lm(formula = medv ~ poly(lstat, 5), data = Boston) ## ## Residuals: ## Min 1Q Median 3Q Max ## -13.5433 -3....
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Introduction to Graphics in R
Introduction to Graphics in R We will be using data from my Spring 2020 classes for these graphics. Please import by logging into your class WISE page, setting the working directory, and calling the dataset. All data have been deidentified. How to load in data enrolled<-read.csv("spring2020_enrolled.csv", header=TRUE) How to...
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Data Wrangling
Data Wrangling The following is the code for class: library(tidyverse) ## Warning: package 'tidyverse' was built under R version 3.4.2 ## ── Attaching packages ─────────────────────────────────────────────────────────────�...
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