Use R Markdown, with rmd file and pdf file generated by knit. I will give 4 Home

by | Dec 11, 2022 | R

Use R Markdown, with rmd file and pdf file generated by knit. I will give 4 Homework Solution in files for reference.
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The data set “cars.txt”:, includes fuel consumption and 10 aspects of automotive design and performance for 32 automobiles:
id: Car id
mpg: Miles/(US) gallon (with a gallon ≈ 3.79 liters)
cyl: Number of cylinders
disp: Displacement (cu.in.)
hp: Gross horsepower
drat: Rear axle ratio
wt: Weight (1000 lbs, with 1000 lbs ≈ 453.59 kg)
qsec: 1/4 mile time (with 1/4 mile ≈ 402.34 meters)
vs: Engine (0 = V-shaped, 1 = straight)
am: Transmission (0 = automatic, 1 = manual)
gear: Number of forward gears
carb: Number of carburetors
cars<-read.table(file="cars.txt", head=T) Our goal is to model the response mpg in terms of the rest of the variables (except name). Partition the data set into two sets a training data and a test data. Remove every fifth observation from the data for use as a test sample. Perform an exploratory analysis. Comment on your findings. Perform a regression analysis and come up with the best multiple linear regression model that explains the response mpg in terms of the rest (except name). Comment on your findings and explain the methods and strategies that you employed in order to select the model you picked. Things you have to include in this part: - Model diagnostics - Justification on whether it is necessary or not to do any transformation on the response or the predictors - Variable selection Assess the prediction performance by using the test sample.

1. Data Description and Curiosity Questions about the data: background or the co

by | Dec 1, 2022 | R

1. Data Description and Curiosity Questions about the data:
background or the context of data selected – sources, description of how it was collected, time period it represents, context in it was collected if available,
reason(s) why you selected it?
Description of the data:how big is it (number of observations, variables),
how many numeric variables,
how many categorical variables,
description of the variables, if available
Are there any missing values?
Any duplicate rows?
Compute summary statistics (mean, median, mode, standard deviation, variance, range).
Select one categorical variable, compute these statistics on a numeric variable by grouping on a categorical variable
Record your observation. What did you find the most fascinating from your descriptive analysis.
2. Descriptive Statistics and Visualization (at least two out of the four listed below)
Relationship between variables
Trend
Distribution of the variable(s)
Spatial data representation
Comparison of summary statistics across categories
3. Generate at least one hypothesis and perform hypothesis test.
4. Summarize your observations
Please add as much observations as you can and comments, I will provide the data with the files