INTRODUCTION

This statistical project focuses on vehicles. These vehicles are a sample of 93 vehicles from a population of different models of cars by different manufactures in the USA. The 93 vehicles are categorized into six types being compact, small, mid-size, large, sporty and van respectively. To analyze the data in the data set of vehicles provided, two aims set out as guidelines are stipulated.

The first aim is to compare price and mpg across categories of vehicles being compact, small, mid-size, large, sporty and van. To archive the first aim, price variables of both basic and top price are to be grouped according to the six car types. The mpg variables of mpg town and mpg best are also to be grouped according to the six car types. The Analysis Toolpak in Tools within Excel is then to be used to create tables for the price variables showing all descriptive statistics with the mean, median, standard deviation, range, skew measure, standard error, sum, count and coefficient of variation which is to be manually added in one table. There is to be an interpretation at the end of each table which includes concerns about the differences in the mean prices and mean mpg across vehicle types and the possible causes depending on whether it’s a mpg or price table. The distribution of price and mpg shapes across each vehicle type and what it means. Confidence intervals are to be used to talk about prices in the population of cars. Several differences of means tests on both prices and mpg are to be taken where there is no much difference between the means of different car types, in both prices and mpg. To find the likelihood of a cheap car of an efficient car across categories probability is to be used and its pattern is to be tested using chi squared tests.

The second aim is to predict the mpg. This is to be done by using correlation to look for linear