CS MGT6203 Data Analytics for Business

Graded Homework #1: Part 2 # For setting options for output formatting library(knitr) library(formatR) opts_chunk$set(=list(f=60),tidy=TRUE) # For creating tibble representation of data suppressWarnings(suppressMessages(library(tidyverse))) # Read data files # For Q1 airbnb_data = ('D:GATECH6203HWairbnb_') airbnb_data<- as_tibble(airbnb_data) # For Q2 direct_marketing_data = ('D:GATECH6203HWdirect_') direct_marketing_data <- as_tibble(direct...

CS MGT6203 Data Analytics for Business Midterm Exam Spring 2023

MGT6203 Data Analytics in Business Grade Homework#2 Part 2 Instructions for Q1 For parts A and B: PlantGrowth is a dataset in R that contains crop weights of a control group and two treatment groups. Clear the environment and get data > rm(list = ls()) > (1) > library(datasets)> > data(PlantGrowth) > force(PlantGrowth) Perform the following operations: (i) Create two separate datasets, one with datapoints of treatment 1 group along with control group and other with...

Q1) For this problem, load the College dataset from the ISLR package library("ISLR") data("College") Please estimate a linear regression model (using the lm function) with Personal as the dependent variable and Room.Board as the independent variable. What are the model’s R-squared and adjusted R-squared values respectively? a) 0.00549, 0.048 b) 0.0143, 0.022 c) 0.0398, 0.0385 d) 0.0325, 0.0336 Answer: C (Week 1 Lesson 4) library("ISLR") data("College") summary(lm(College$...

Homework 2 – Part2 Q1.) For part A and B PlantGrowth is a dataset in R that contains crop weights of a control group and two treatment groups. #Code to Get Data library(datasets) data(PlantGrowth) Perform the following operations: (i) Create two separate datasets, one with datapoints of treatment 1 group along with control group and other with datapoints of treatment 2 group with the control group. A) Now compute the difference estimator for treatment 1 and treatment 2 datasets that...

Assumptions of OLS Linearity Assumption: E[Y] = β0 + β1x. We are assuming the expected value of Y for any given X is a linear function of X (approximates to a straight line) 2. Assumption about errors: We assume the error terms are independently and identically distributed (iid) normal random variables with 0 mean and variance σ. 3. Assumptions about predictors: For multiple regression (more than 1 predictor) we assume the predictors are linearly independent. Heteroskedasticity might requi...

Self Assessment : Week 12 1. How would you best describe Operations Management? A. Direction and control of process that transform inputs into finished goods and service B. How to make operating machines C. Scheduling of workers on the manufacturing floor D. Deciding what to make Ans: A 2. Which is not a question typically concerned with Operations Management? A. What benefits package should we offer our employees? B. What types of queues should we employ? C. How much capacity do we ne...