Cars93 Dataset Download _TOP

The 93CARS dataset contains information on 93 new carsfor the 1993 model year. Measures given include price, mpgratings, engine size, body size, and indicators of features. The 26 variables in the dataset offer sufficient variety toillustrate a broad range of statistical techniques typically found in introductory courses.

1 The 1993 New Car data was inspired by a similar dataset for 1989 model cars which has been included among the sample data forthe Student Edition of Execustat (PWS-KENT 1990). We have used Execustat's CARS89data to demonstrate many points in both introductory and secondlevel courses in applied statistics. In what follows we give abrief description of the updated and expanded 93CARS dataset and suggest several ways it might be used in class.

Cars93 Dataset Download

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9 This is a multi-purpose dataset which can be used at many points in a course. We have often used Execustat's similar CARS89 data as an initial example for demonstrating the statistical package to students in the second week of an introductory course. This class typically is held in a classroomequipped with a computer and projection system, with theinstructor "driving" the software. Despite having only studiedsome descriptive techniques, students are easily drawn into adiscussion of the interesting features of the data. They tend tobe familiar with most of the variables (and specific car models). They anticipate relationships between the variables, are quickto generate both questions and explanations, and enjoy guessingat the identity of outliers in the plots. Inevitably, the classperiod ends long before the stream of questions is exhausted.

We will be working with the dataset called Cars93 found in the package, MASS. Using that dataset, we will draw the scatterplot and regression line of the weight of the car versus the miles per gallon achieved in the city. These will be done in basic R and ggplot2.

The above result shows the dataset has many Factor variables which can be considered as categorical variables. For our model we will consider the variables "AirBags" and "Type". Here we aim to find out any significant correlation between the types of car sold and the type of Air bags it has. If correlation is observed we can estimate which types of cars can sell better with what types of air bags.

in R please provide the code for the Test for Homogeneity using the Cars93 dataset:

a. Load the Cars93 data set

b. Create a new data frame that has the counts of the number of cars of each type with each drivetrain using the table function. View new table

c. Conduct a chi-squared test to see if the distribution of cars with each drivetrain is the same for each car type. Clearly state the hypothesis, test results, conclusion and justification

We want our functions to work for all datasets by passing a pandas dataframe as an input to our function instead of manually having to change the code for each dataframe. Not even once, in any of the functions, we will use anything specific to the dataset we have taken as an example.

One of the reasons for R's continued popularity is its strong statistical analysis capabilities. R was designed specifically for statistical computing and provides a rich ecosystem of packages for data analysis and visualization. This makes R a powerful tool for data scientists who need to analyze large datasets and perform complex statistical modeling.

Use the mtcars R dataset. Let's say that you wanted to find the best-fit model to explain mpg. However, many of the variables we used in the model are likely correlated (you can actually see this if you plot(mtcars)). So perhaps it might be more beneficial to find principal components that aggregate some of the measures because these principal components will be uncorrelated.

Standardize this dataset; PCA Question xXlsX; and perform principal component analysis.

What proportion of total variance is accounted for by the first three principal components? (Hint: This is a cumulative value). Round to two decimal places.

What are the weights for computing the second principal component scores for each variable? Round all answers to four decimal places.

Note that in ggplot you build up the elements of the graph by connecting the parts with the plus sign, +. So we add further graphical elements by stringing together phrases. You can see this in the following code, which uses the built-in mtcars dataset and plots horsepower versus fuel economy in a scatter plot, shown in Figure 10.4

The built-in iris dataset contains paired measures of Petal.Length andPetal.Width. Each measurement also has a Species property indicatingthe species of the flower that was measured. If we plot all the data atonce, we just get the scatter plot shown in Figure 10.14:

Your dataset contains (at least) two numeric variables and a factor or character field defining a group. Youwant to create several scatter plots for the numeric variables, with onescatter plot for each level of the factor or character field.

The Cars93 dataset contains 27 variables describing 93 car models asof 1993. Two numeric variables are MPG.city, the miles per gallon inthe city, and Horsepower, the engine horsepower. One categoricalvariable is Origin, which can be USA or non-USA according to where themodel was built.

The UScereal dataset from the MASS package contains many variables regarding breakfastcereals. One variable is the amount of sugar per portion and another isthe shelf position (counting from the floor). Cereal manufacturers cannegotiate for shelf position, placing their product for the best salespotential. We wonder: Where do they put the high-sugar cereals? We can produce Figure 10.43 andexplore that question by creating one boxplot per shelf:

Many datasets are included in a package called datasets which is distributed with R so these datasets are instantly available to you for use. For example, two datasets namely cars and pressure are included in this default datasets package. So, you can access their data by using functions such as head(cars), summary(cars), etc.

Input: BostonHousing dataset# Solutionimport datatable as dtdf = dt.fread(' ')df.head(5)3. How to read first 5 rows of pydatatable Frame ?Difficulty Level: L1Question: Read first 5 rows of datatable Frame.Input URL for CSV file:

Input: BostonHousing dataset# Inputimport datatable as dtdf = dt.fread(' ')# Solutiondf[:,"new_column"] = df[:, dt.f.age + dt.f.rad]6. How to get the int value of a float column in a pydatatable Frame?Difficulty Level: L1

Difficulty Level: 2Questions:Delete the cell at position 2,1. Delete the 3rd row.Delete the chas column.Delete rows where column zn is having 0 value.Input: BostonHousing dataset

Difficulty Level: L1Question: Get data types of all the columns in the datatable Frame.Input: BostonHousing datasetDesired Output:crim : stype.float64zn : stype.float64indus : stype.float64chas : stype.bool8nox : stype.float64rm : stype.float64age : stype.float64dis : stype.float64rad : stype.int32tax : stype.int32ptratio : stype.float64b : stype.float64lstat : stype.float64medv : stype.float64Show Solution# Solutionimport datatable as dtdf = dt.fread(' ')for i in range(len(df.names)): print(df.names[i], ":", df.stypes[i])crim : stype.float64zn : stype.float64indus : stype.float64chas : stype.bool8nox : stype.float64rm : stype.float64age : stype.float64dis : stype.float64rad : stype.int32tax : stype.int32ptratio : stype.float64b : stype.float64lstat : stype.float64medv : stype.float6416. How to get summary stats of each column in datatable Frame?Difficulty Level: L1Questions:For each column:Get the sum of the column values. Get the max of the column values.Get the min of the column values.Get the mean of the column values.Get the standard deviation of the column values.Get the mode of the column values.Get the modal value of the column values.Get the number of unique values in column.Input: BostonHousing dataset

Please provide an introduction for how to analyze contingency tables with SQL.Demonstrate how to derive contingency tables from a simple dataset. Also, providea SQL framework for analyzing a contingency table via the Chi Square test statistic.

Do you want to leverage your dataset and SQL scripting skills for data scienceprojects? Well, this tip is one place for you to start doing just that.An article on the DataCamp website motivated this tip; the DataCamp websiteis a tutorial and training resource for data science development skills. The articlefeatured an introduction to contingency tables using the popular R language withthe Chi Square test and other statistical tools. This tip aims to illustrate howyou can create contingency tables and analyze them with a SQL script and the ChiSquare test. A major goal of the tip is to illustrate one way to leverage SQL scriptingin a typical data science project.

In this tip, you will see how to use a classic data science source (hereandhere) and SQL Server scripting features in a data science project. In additionto using a classic data science source, this tip also compares SQL and R for performingmultiple steps in a data science project from accessing a dataset, to performingexploratory data analysis, to computing a contingency table, to computing a ChiSquare statistic.

This tip uses the Cars93 dataset that ships with the R programming language package.This dataset has one row per make (comprised of a manufacturer and a model) witha top row of field names. This dataset is available from numerous sources; thistip demonstrates how to import the dataset into SQL Server from thissource. When you click a link labeled "download this file" from thesource, a csv file downloads to your computer, but the file has a non-standard formatrelative to typical Windows csv files. The following screen shot shows an excerptfrom a Notepad++ session with the file after some minor editing. The editing removeddouble quote marks around field values in lines 2 through 94 and added ID as thefirst field name to line 1. 2351a5e196