MGT6203 Data Analytics in Business Question With Verified Answers
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Course
Introduction to Modeling for Business
Institution
Georgia Institute Of Technology
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)>...
(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 were created, in
comparison with the control group?
Now create dummy variable for control group
> Plantdata <- PlantGrowth %>% mutate(Treat_ind = ifelse(group == "ctrl",
0,1))
Create two data sets one for treatment group 1 and one for treatment group 2
> Plantdata1 <- Plantdata[1:20,]
> Plantdata2 <- Plantdata[c(1:10,21:30),]
Use linear regression to calculate the difference estimator
lmt1 <- lm(weight ~ Treat_ind, data = Plantdata1)
> summary(lmt1)
Call:
lm.default(formula = weight ~ Treat_ind, data = Plantdata1)
Residuals:
Min 1Q Median 3Q Max
-1.0710 -0.4938 0.0685 0.2462 1.3690
Residual standard error: 0.6964 on 18 degrees of freedom
Multiple R-squared: 0.07308, Adjusted R-squared: 0.02158
F-statistic: 1.419 on 1 and 18 DF, p-value: 0.249
Here the difference estimator is -0.3710 for treatment group 1. (meaning weight is
0.3710 less for treatment group1 vs the control group)
Use linear regression to calculate the difference estimator for group 2
> lmt2 <- lm(weight ~ Treat_ind, data = Plantdata2)
> summary(lmt2)
Call:
lm.default(formula = weight ~ Treat_ind, data = Plantdata2)
Residuals:
Min 1Q Median 3Q Max
-0.862 -0.410 -0.006 0.280 1.078
Coefficients:
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