Climate Change Problem Assignment

Assignment Task

As climate change is becoming a real threat, many governments are taking action to mitigate greenhouse gas emissions. The automobile industry, in particular, is targeted for regulations, and in many instances, fuel economy standards are set in place for car manufacturers to meet. If manufacturers continue to produce cars with poor gas mileage despite the norm, they are often made to pay a tax. Engineers and businesses must devise creative solutions to reduce fuel consumption without compromising performance.

Since this issue is relevant to today's engineering challenges and protecting the environment, XYZ co. Has decided to analyze data for better prospects. To do this, they used the Vehicle data set, which has data on the design, performance, and fuel economy of 32 automobiles from 1973 to 1974.

The dataset has 32 observations on 11 (numeric) variables:

mpg    Miles/(US) gallon

cyl       Number of cylinders

disp    Displacement (cu.in.)

hp        Gross horsepower

drat      Rear axle ratio

wt         Weight (1000 lbs)

qsec     1/4 mile time

vs         Engine (0 = V-shaped, 1 = straight)

am       Transmission (0 = automatic, 1 = manual)

gear     Number of forward gears

carb     Number of carburetors

1. Load the required package and view the dataset. Check out the class, dimension, and structure of the dataset.

2. To have a gist of the data, explore it graphically, and plot some histograms, first for mpg, the second for the number of cylinders, and the third for hp. Also, check the summary of the data. Also, look at the head and tail of the data.

3. Run the LPM model with wt, disp, hp, and gear variables as predictors and check the summary of the model.

4. Given certain limitations of LPM, run the logit model on the above dataset. Check the summary of the model and also calculate margins. Further, check the dataset's proportion of straight (0) or v-shaped (1).

5. Run the probit model on the above dataset. Check the summary of the model and also calculate margins. Further, check the dataset's proportion of v-shaped (0) or straight (1).

6. Using the default threshold limit, compute the misclassification error (for LPM, Logit, and Probit). Convert the accuracy of the models using the accuracy measure and identify which model gives the best results.

7. Create a performance object (for LPM, Logit, Probit) using tpr and fpr measure (ROCR curve) and plot the same with a central heading. Now fit a line with intercept 0 and slope 1(line type= l, line width =2, color=green). Also, calculate another statistic named the area under the curve (auc) to check the accuracy.

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