Code for Data Analysis| statistics

Code for Data Analysis| statistics

Answer all questions and complete all tasks thorough the document

and follow the directions

attached are the documents and download this one :

http://www.wikiupload.com/UGSP0VJRQYEXZA1

Code for Data Analysis:
In R go to File< New Script (New Document on MAC) – A new script window will pop up. You should write all of your code in a script window and not directly in the console.

The R code can be uploaded directly into R by copying and pasting everything below R Code DA4 into a script window.

Note: Any time you see # this means that R will not read what follows. I will use this to make comments about the following command.

R Code DA4

####################################################################

# Number 1

#####################################################################

# Upload data set called EPAFE2017Data.csv. Let’s call it fueldata.

fueldata = read.csv(file.choose(), header= TRUE)

head(fueldata)

# Make a box plot to compare the combined fuel efficiency of cars made by American companies

# and international companies for 2017.

boxplot(CombFE~International, data = fueldata, horizontal = TRUE, col= c(“coral”, “lightblue”), main = “2017 EPA Estimated Combined Fuel Efficiency:

American vs International Car companies”, xlab = “Miles per Gallon”)

# Get Summary Statistics

# aggregate() Calculates Sample Means, Sample Standard Deviations and Sample Sizes between groups.

aggregate(CombFE~International, data = fueldata, mean) # Means

aggregate(CombFE~International, data = fueldata, sd) # Standard Deviations

aggregate(CombFE~International, data = fueldata, length) # Sample Size

# Perform a Two Sample T Test, with CI level 99%

t.test(fueldata$CombFE~fueldata$International, conf.level=0.99)

#####################################################################

# OPTIONAL Number 2 Data Table

#####################################################################

# Create a 2X2 table of International vs Guzzler Status.

table(fueldata$International, fueldata$Guzzler)

#####################################################################

# Number 3 ANOVA

#####################################################################

# Create a boxplot of Combined Fuel Efficiency vs Drive Types

boxplot(fueldata$CombFE~fueldata$Drive, horizontal = TRUE, col= rainbow(5), main = “Estimated Fuel Efficiency

for 2017 Vehicles among Drive Types”, cex.axis = 0.7, xlab = “MPG”)

# Note if you can’t see the categories of drive expand your graph.

# Get Means, Sds and Sample Sizes for each

aggregate(CombFE~Drive, data = fueldata, mean) # Means

aggregate(CombFE~Drive, data = fueldata, sd) # Standard Deviations

aggregate(CombFE~Drive, data = fueldata, length) # Sample Sizes for each

# Test whether any means differ from each other with an Overall F test.

mod = aov(fueldata$CombFE~fueldata$Drive)

summary(mod)

# IF the F statistic in the ANOVA is significant…

# Perform a multiple comparisons test to see which

# fuel types are significantly different.

# Tukeys multiple comparisons test

TukeyHSD(mod, conf.level = 0.95)

#subset the data to only look at part-time 4 wheel drive vehicles. What type of vehicles are these?

PT4wdonly = subset(fueldata, fueldata$Drive==”Part-time 4-WD”)

# Who makes these vehicles?

PT4wdonly$Division

# What are th ecar names?

PT4wdonly$Carline

# What type of cars are they?

PT4wdonly$CarType

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