Category: R

The table below displays catalog-spending data for the first few of 200 randomly selected individuals from a very large (over 20,000 households) data base.1 The variable of particular interest is catalog spending as measured by the Spending Ratio (SpendRat). All of the catalog variables are represented by indicator variables; either the consumer bought and the…

Apply the linear regression models including Multiple Linear Regression, Stepwise Variable Selection, and LASSO, to analyze a dataset of Major League Baseball player statistics and predict their salaries. View the three files to get started: Assignment and dataset The report template where you will paste your screenshots and written answers The starter R script to…

Data Mining / RStudio: Topic: Linear Regression, Shrinkage Regression Shrinkage Regression (Ridge Regression and LASSO Regression). Variable Selection using LASSO. Obtain the optimal tuning parameter through Cross Validation. Aimed outcomes: Explain the difference in objective function between Ridge Regression and LASSO Regression. Understand why LASSO can be used for variable selection. Conduct cross-validation to obtain…

library(tidyverse) library(tidyr) library(dplyr) ###PRACTICE MERGING### #1. Read in each file from the folder NH-2020-M5 #2. Perform some exploration of the data and understand how these datasets join #3. Join these datasets to create a masters NIBRS dataset #4. Use this dataset to answer questions for your R Lab Quiz #5. Use this dataset to create…

Overview For your final project, you will use R to solve problems about probability distributions. Specifically, you will make use of the d, p, q, and r functions built into R for working with probability distributions. In most cases, you will need to determine the type of probability distribution that is described and use R…

Apply the fundamental R programming concepts, including vectors, data frames, subsetting, and basic visualization, to analyze a dataset of housing values in the suburbs of Boston. Uses an external CSV file that contains some missing values. A key part of the task will be identifying and handling these data issues before performing your analysis. Download…

Overview In this project, you will use R to solve counting and probability problems. To gain the most benefit from this project, avoid calculating numeric values and entering them into R. Instead, use R to do all necessary calculations. This project analyzes trends in U.S. housing prices and affordability using open-source data. The goal is…

The zipfile contains 2 CSV data set, Instruction for the assignment in words and template for the assignment in R. Please read the instruction in the word document, use 2 CSV data set for the assignment in R, and follow template in R studio answer all the questions in instructions Word. Requirements: 1 page

PART ONE For this assignment you will write an R program to complete the tasks given below. You will hand in two files for this assignment. A File with your R program. This file should contain only the code (no output) and must have the typical r extension. No other file extensions will be accepted.…

Overview Asking appropriate questions of data is an important part of data analytics, as is interpreting the results of the analysis. In this assignment, you will familiarize yourself with a dataset and begin thinking about key questions you could answer from the data. Instructions To complete this assignment, you will produce and submit two files:…