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Statistical analysis and visualizations was written in R programming, Load, clean up, reshape datasets using Tidyverse. visualize datasets with basic plots such as line, bar, scatter plots using ggplot2, Implemented and evaluated one-sample t-Tests, two-sample t-Tests, and analysis of variance (ANOVA) models for the dataset.

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mechacar's Introduction

MechaCar

Perform multiple linear regression analysis to identify which variables in the dataset predict the mpg of MechaCar prototypes
Collect summary statistics on the pounds per square inch (PSI) of the suspension coils from the manufacturing lots
Run t-tests to determine if the manufacturing lots are statistically different from the mean population
Design a statistical study to compare vehicle performance of the MechaCar vehicles against vehicles from other manufacturers. For each statistical analysis, you’ll write a summary interpretation of the findings.

This consists of three technical analysis deliverables and a proposal for further statistical study.The following are:

Linear Regression to Predict MPG
Which variables/coefficients provided a non-random amount of variance to the mpg values in the dataset?

Answer

On this dataset, which is predicting miles per gallons, the result shows that the probability that each variables/coefficients helps to contributes a non-random amount of variance to the model, the length of the vehicle traveled and the clearance would not be enough to provide a statistical result of variance needed for the model.

Is the slope of the linear model considered to be zero? Why or why not?
No, the slope is not considered to be zero because the p-value on this data is less than 0.05. The p-value of the linear model is one of the factors that would determine the slope of the linear model
Does this linear model predict mpg of MechaCar prototypes effectively? Why or why not?
Summary Statistics on Suspension Coils