# Auto mpg data r

When deciding whether to purchase automobile, there are several factors one might consider including automatic or manual transmissions, horsepower, and such. Data published in Motor Trend US Magazine is comprised of fuel consumption and ten aspects of automobile design and performance for thiry-two automobiles —74 models.

Thirteen cars had manual transmissions while the other nineteen had automatic transmissions. As we might expect from physics, the greatest factor in determining the miles per gallon for a vehicle is the weight of the car and horsepower of the engine. Transmission type is not a significant factor in the fuel economy of the vehicles considered.

In Figure 1, the boxplot shows that when comparing only transmission type vs. In Figure 2, we see that the miles per gallon decreases as the car weight increases. There seems to be seperation between automatic and manual transmisions: the manual transmission vehicles appear to weigh less in general than automatic transmission cars.

This plot suggests that it is necessary to explore the effect of transmission type when controlling for the weight of the car. In Figure 3, the plot shows the miles per gallon decreasing as the horsepower increasing. Again we see a seperation between automatic and manual transmisions: the manual transmissions tend to be in the lower horsepower automobiles. This plot suggests a relationship with mpg and hp that should also be analyzed further.

In Figure 4, a lower number of cylinders corresponds to higher miles per gallon in both automatic and manual transmission cars, but there are more manual transmission cars with a lower number of cylinders than automatic transmissions. The relationship of the transmission type and the number of cylinders could be related as a design factor and should be analyzed in relation to mpg.

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The p-value is 0. In consideration for modeling the variables effect on miles per gallon, we must consider which variables are design factors versus resulting properties of the design. Like MPG, the quarter mile time qsec variable is assumed to be a resulting property of the design and therefore will not be used in the modeling for analysis of MPG Another assumption about our model is that the residuals have constant variance and follow a normal distribution with mean 0.

We need to discover which variables are predictors of mpg. Using a stepwise variable selection approach where we look for minimum AIC, we examine the variables both forwards and backwards to find the significant variables and model that fits the data but does not overfit. The step AIC analysis showed that wt, hp and cyl are the variables which are most significant in predicting mpg.

Transmission type was not selected so as a check to see if transmission type is significant we will force the stepwise variable selection process to consider the transmission type. After performing a step AIC which forces the am variable to be considered, we see that transmission type is not a significant predictor for mpg. Based on the results of the step process, we build models to quantify the dependencies of mpg.

We begin by once again examining whether transmission type is a predictor for mpg by building three models which also include wt, hp and cyl in each successive model. Once again, in all three models transmission type is not significant when we control for wt, hp and cyl.

Next we build models excluding am and examine mpg with wt, hp and cyl as predictors. In the model which includes wt and hp as predictors to mpg, the variables are are both significant and have very small p-values. When the model also includes cyl, the variables hp and cyl both have p-values of 0. In this model, wt is the significant variable. The t test indicated that there is a difference between manual and automatic transmissions with the manual transmission having 7.

However, the stepwise variable selection based on AIC resulted in wt, hp and cyl as the significant variables in the model of the mtcars dataset, recalling that we excluded qsec because it is a result of the design and not an input. Models which included transmission type with these variables also showed that transmission type is not a predictor for mpg when controling for hp and wt and cyl.

In every model which includes weight, the p-value for transmission type is greater than 0. The models which compare mpg to wt, hp and cyl show that when mpg is controled for all 3 variables, the hp and cyl are not significant, possibly because they are colinear. The model that performs the best describes mpg as a function of wt and hp. The Adjusted R-squared is 0. The p-value for the model is 9. In the best fit model which models mpg as a linear combination of wt and hp, we see that the mpg decreases by 3.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. For many of their examples they use the package ISLR. Unfortunately, I struggle with an example: They install the package I have tried it in R and RStudio and execute the following code. Error in file file, "rt" : cannot open the connection In addition: Warning message: In file file, "rt" : cannot open file 'Auto. I also tried to attach the package with the command library ISLR after downloading it - without success.

I am not sure if the issue is related to the path of the package but I don't believe so. At least I tried to save the package in my working directory. I feel a bit stupid as the task looks more than easy. If anyone could help out, it would be much appreciated. We begin by loading in the Auto data set. The following command will load the Auto. Once the file is saved with the name Auto. I found that the best thing to do is to: 1. Download the data set from the internet, which is "Auto.

Then copy it to your current working directory. After that, follow the instruction:. On Windows OS, the file is saved with.

Using list. Save the file with. Learn more. Asked 4 years, 1 month ago. Active 9 months ago. Viewed 18k times. Markus Knopfler Markus Knopfler 2 2 gold badges 3 3 silver badges 13 13 bronze badges.

Install the package once install. If it "doesn't work", provide details. Active Oldest Votes. This is an excerpt from page We begin by loading in the Auto data set. Emphasis added.In this post we will look into the Auto MPG data set and clean it so that it is ready for further use.

This data set shows the mpg of a group of car models produced in the s and the s along with some characteristic information associated with each model. More information about the data set can be found here. Download the auto-mpg. The data has 9 columns features and entries.

Of the 9 columns, 8 are numerical. The labeling on the origin column is 1 for domestic, 2 for Europe and 3 for Asia. This data set is typically used to learn and predict the mpg column using the remaining columns. Upon close inspection we find that there 5 missing values in the horsepower column and that they have been entered as "?

We have another problem. The horsepower column appears as an object. We need to convert it to float or int. One way to fix the missing value problem is to fill in those locations with the mean value of the horsepower data.

To be able to do that we first assign NaN to the missing data locations, change the data type of the horsepower column to float and then assign each missing location the mean value of the column. Now, things are in order and the statistics should come out fine.

We can use the dataframe. Now that the problems associated with the data are ironed out, we can save this version of the file to separate file. The data will be saved to a. Saving the Data as a.It is a very useful and simple form of supervised learning used to predict a quantitative response.

Originally published on Ideatory Blog. Sometimes there may be terms of the form b4x1. The trick is to apply some intuition as to what terms could help determine Y and then test the intuition. A very common use case is predicting sales from advertising spend on various media. If you know any such dataset with media-specific advertising spend and sales for the corresponding period with at over 40 or so rows, do share in the comments.

I found a dataset on mpg miles per gallon on UCI Machine Learning Repository and other car data and regression on that was quite fun. Use this command to plot pairwise scatter plots in RStudio and inspect the result for relationships between the independent variable mpg and the numerical dependent variables. See how quickly a scatter plot helps see the relationships between the variables.

Mpg decreases with increase in number of cylinders, displacement, weight, horsepower and increases with acceleration the variable acceleration represents time taken to acceleration from 0 - 60 mph, so the higher the acceleration value, the worse the actual acceleration. Your plot would also show relationships among mpg, model-year and origin variables.

I excluded them here because the plot image about would become too large to be easily intelligible. Since mpg clearly depends on all the variables, let derive a regression model, which is simple to do in RStudio.

P-values for coefficients of cylinders, horsepower and acceleration are all greater than 0. High p-values for these independent variables do not mean that they definitely should not be used in the model. It could be that some other variables are correlated with these variables and making these variables less useful for prediction check Multicollinearity.

Here we see that both Multiple R-squared and Adjusted R-squared have fallen. When comparing models, use Adjusted R-squared. The formula for Adjusted R-squared includes an adjustment to reduce R-squared.

If the additional variable adds enough predictive information to the model to counter the negative adjustment then Adjusted R-squared will increase. If the amount of predictive information added is not valuable enough, Adjusted R-squared will reduce. The Adjusted R-squared is the highest so far. While creating models we should always bring business understanding into consideration.In this R tutorialwe will be using the highway mpg dataset.

## Highway MPG Dataset Graphical Analysis with R

In this R tutorial, we will use a variety of scatterplots and histograms to visualize the data. Scatterplots will be used to create points between cyl vs. Once these are created, we can visually see the top choices for city and highway driving for the best mpg among 4, 6 and 8 cylinder vehicles. Histograms will be used for the use of different types of drives.

This data will be broken up into subsets and the classes will be identified for the three types of drives. In addition, there will be two additional histograms that will be broken into subsets against cyl vs. The str command displays the internal structure of an R object.

This function is an alternative to summary. When using the str function, only one line for each basic structure will be displayed. The summary function is a basic function that issued to produce the result summary of various model functions. A scatterplot is a comparison between 2 variables. The x-axis and y-axis show an observation between the 2 variables. Below are a series of scatter plots for visual comparison of mpg comparison of cyl versus cty and hwy.

Also, the below scatter plots use a regression line to see the decline of mpg from the 4 cylinder vehicles to the 8 cylinder vehicles. As one can see from the below, the vehicle classes that do well in city; also do well on the highway. The vehicle class that does the best in the subcompact class.

The subcompact class does exceptionally well in both the city and highway mpg. Can you see how the mpg goes down with 4, 6 and 8 cylinders in the city and highway mpg? The above scatter plots give a great view of how mpg decrease with larger cylinder vehicles. Below are histograms with drives drv and also the data is broken up into subsets of classes. In addition, we will identify the classes with the highest number of vehicles with 4, front, and rear wheel drives.

The below histograms will compare cyl, class and drv across all totals. There are a total of 3 histograms for a complete visual of each. In addition, the histograms will identify the classes with the highest number of vehicles with 4, front, and rear wheel drives. Also, a shaded total of each drive in each class with cylinder totals of 4, 6 and 8 within each class.This project is about predicting mpg of a car based on different variables using linear regression.

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The dataset was used in the American Statistical Association Exposition. Variables that are available in the data set are. R file contains all the R code which is used to predict mpg based on different variables. While running the R code make sure that you have included the correct path for the csv to file to be read in the line ''. Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

## Analysis of MPG in the Motor Trends Cars dataset

Sign up. Branch: master. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 4 commits 1 branch 0 tags. Failed to load latest commit information. Problem Statement. R Final Document. View code. Predicting-MPG This project is about predicting mpg of a car based on different variables using linear regression.

Variables that are available in the data set are mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string unique for each instance You can read more about the data set at auto-mpg.

Problem Statement please refer the file "Problem Statement. PDF" contains the final report and conclusions of the project. Running the code While running the R code make sure that you have included the correct path for the csv to file to be read in the line ''.

About This project is about predicting mpg of a car based on different variables using linear regression.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. How can I rename the values in the trans column to be either Auto for automatic and Manual for manual transmissions?

I also attached a picture of the desired graph for reference.

### Download Fuel Economy Data

Here's a good way to relabel the transmission I create a new column named transmissionbut you could just as easily overwrite the existing column. I've left out all your non-standard ggplot stuff because I'm not sure what package s it's from. It doesn't seem related to your issue anyway, so you should be able to just add it back in.

Learn more. The mpg dataset in R Ask Question. Asked 1 year, 4 months ago. Active 3 months ago. Viewed 1k times.

Gavyn Henderson. Gavyn Henderson Gavyn Henderson 27 5 5 bronze badges. Welcome to SO! Could you make your problem reproducible by sharing a sample of your data and the code you're working on so others can help please do not use strhead or screenshot?

You can use the reprex and datapasta packages to assist you with that. Tung they're using the mpg dataset which is in ggplot2. OP, it's hard to help without any code. What have you tried so far, and how hasn't it worked? Here is a link for the full code and a picture of my graph so far. Since Gregor's answer works for you, you should mark it as correct. Since you're the question's author, only you can mark the question as answered. Click on the check mark you see by his answer. Active Oldest Votes.

Gregor Thomas Gregor Thomas