MGT 340 M7A2

Regression Analysis

Regression Analysis

Introduction

The study is related to chain of restaurants in the US that is Appetizer which particularly focuses on two important aspects that are expenditure by from stores and restaurants. The phenomenon is important to study as it will help in controlling the expenditures of the restaurants. Therefore, it is important that the association between the expenditures in restaurants should be compared with the expenditures by people on purchasing food from the stores.

Variables

The variables that are incorporated in the study include:

Restaurant Expenditure which is the dependent variable;

Food Store Expenditure which is the independent variable.

Results

Model Summary b

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df 1

df 2

Sig. F Change

1

.333 a

.111

.093

2.21008

.111

6.004

1

48

.018

a. Predictors: (Constant), Food Store Expenditure

b. Dependent Variable: Restaurant Expenditure

From the table of model summary, it is found that the value of R-square is 0.111 and the value of adjusted R-square is 9.3% which shows the least square regression line for the restaurant expenditure and food store expenditure that means there is a relationship between the dependent variable that is restaurant expenditure with the independent variable that is food store expenditure which presents there is an impact of food store expenditure on restaurant expenditure. In relation to this, regression analysis is a method of modeling the measured data and the study of their properties. The data consist of pairs of values of the dependent variable which is a response variable and independent variable which is an explanatory variable. Regression model is a function of the independent variable and parameters added a random variable (Keith, 2006). The model parameters are adjusted so that the model best approximates the data. The criterion for the quality of the approximation (objective function) is usually the standard error; the sum of the squares of the difference of the ...