Regression Analysis

Introduction

This presentation on Regression Analysis willing tie to a candid retroversion model. Initially, the regression model and the regression equation will be explored. As well, there will be a brief look into estimated regression equation. This fictional character study that will be used involves a mammoth Chinese Food restaurant chain.

Business Case

In this instance, the restaurant chains management wants to chance the best locations in which to expand their restaurant business. So remote the most successful locations have been near college c ampereuses. This opinion is ground on the positive numbers that quarterly sales (y) gleam and the size of the school-age child cosmos (x). Managements mindset is that over all, the restaurants that ar within close proximity to college campuses with large student bodies give more sales than restaurants located near campuses with small student bodies.

In the sample box below, xi is the size of the student population (in thousands) and yi is the quarterly sale (in thousands of dollars).



The value for xi and yi for all of the 10 Chinese Food restaurants given in the sample be reflected as follows:

Sample Data:

(measured in 1,000s)(measured in $1,000s)

Restaurant Student PopulationQuarterly Sales

(i) (xi)(yi)

1258

26105

3888

48118

512117

616137

720157

820169

922149

1026202

Methodology

Given the circumstances, in the simple linear regression model, y is a linear spot of x (the ?0 + ?1 Xi part) plus ?i. ?0 and ?1 are referred to as the parameters of the model, and ? (the Greek letter epsilon) is a random variable referred to as the error term

(Anderson, Sweeney & Williams, 2000, pg. 450). To state the simple linear equation succinctly; yi = ?0 +...

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