MechaCar Statistical Analysis

A statistical study using R to compare vehicle performance.

Overview of the analysis:

The following study contains three technical analysis deliverables and a proposal presented as follows::

1. Deliverable 1: Linear Regression to Predict MPG
2. Deliverable 2: Summary Statistics on Suspension Coils
3. Deliverable 3: T-Test on Suspension Coils
4. Deliverable 4: Design Proposal a Study Comparing the MechaCar to the Competition

Linear Regression to Predict MPG

• We designed a linear model that predicts the mpg of MechaCar prototypes using several variables from the MechaCar_mpg.csv and the resulting model can be viewed on the image below:

  

  SUMMARY

  From the results above:

  • Which variables/coefficients provided a non-random amount of variance to the mpg values in the dataset?

  In this dataset the vehicle_length, ground_clearance (as well as the intercept) provided non-random amounts of variance to the mpg values.

  • Is the slope of the linear model considered to be zero? Why or why not?

  The linear model's slope is not considered to be zero as the p-Value for this model is 5.35e-11 which is significantly smaller than the significance level = 0.05%.

  • Does this linear model predict mpg of MechaCar prototypes effectively? Why or why not?

  This linear model predicts prototypes of mpg MechaCar effectively since the r-squared value is 0.7149, which means that about 71% of all mpg predictions will be effectively determined by this model.

Summary Statistics on Suspension Coils

• The Suspension_Coil.csv file contains data on the results of testing weight capacities of various suspension coils from several production lots to determine overall consistency.

  SUMMARY

  Total Summary

  

  Lot Summary

  

  From the results above:

  The design specifications for the MechaCar suspension coils dictate that the variance of the suspension coils must not exceed 100 pounds per square inch (PSI). Does the current manufacturing data meet this design specification for all manufacturing lots in total and each lot individually? Why or why not?

  For this model, Lot 1 and Lot 2 meet design specifications, having very close mean and median values, with variances of 0.97 and 7.47. However, Lot 3 has the most variance amongst the three, 170.28 and does not meet the manufacturing expectations.

T-Tests on Suspension Coils

• T-tests were conducted on the Suspension_Coil.csv to determine if there is a statistical difference on this dataset. Using the sample population mean of 1500, these are our findings:

  SUMMARY

  

  We can observe from the results that the true mean is 1498.78 which was also on our summary statistics table for Deliverable 2. The t-test shows there is not enough evidence to reject the null hypothesis since the p-value for all manufacturing lots is 0.06028 higher than the common significance level of 0.05.

  Lot 1 Lot 2 Lot 3

  For each individual lot, Lot 1 had a p-value of 1 and Lot 2 had a p-value of 0.60, both statistically similar which means we cannot reject the null hypothesis. For Lot 3, the sample mean is 1496.14 with a p-Value of 0.04, lower than the significance level of 0.05 which indicates that the sample mean and the presumed population mean are not statistically different.

Study Design: MechaCar vs Competition

• It would be interesting to conduct a study that collects data on MechaCar and comparable models from other manufacturers as a popular feature that consumers heavily consider when thinking of purchasing a car is fuel efficiency as it greatly impacts costs of ownership per year.

  Metrics

  • Driving system (2-wheel, 4-wheel)
  • Fuel Capacity / Fuel type (Electric, Hybrid, Gasoline)
  • MPG (Gasoline Efficiency)

  Hypothesis

  Is the MechaCar fuel efficiency performance adequately priced based on its performance or not.

  Statistical Tests

  Conducting a multiple linear regression to determine the metrics that have the most significant correlation and predictability with the best fuel efficiency and its impact on the vehicle price.

Resources

Data Source: MechaCar_mpg.csv, Suspension_Coil.csv

Software: RStudio, R