Economic Regression Analysis

Economic Project

Regression Estimation & Analysis

For

Data Set (D)

Determinants of U.S. Domestic Price of Copper

Requested By \ Prof. Abdelhamid Mahboub

Prepared By The Team:

1- Akram Ahmed

2- Fawaz AL-Ghamdi

3- Hasan Hanjour

4- Nawaf AL-Zahrani

Done in 20 / 0 3 / 2016

Regression Analysis

For

Determinants of U.S. Domestic Price of Copper

Introduction

Regression Analysis is a statistical technique for finding the best relationship between a dependent variable (here: the quantity demanded), and selected explanatory variables (here: own price, prices of related goods, income, advertisement, etc.)

Hence we will use it to analyze the Determinants of U.S. Domestic Price of Copper.

Procedure Of Regression Analysis:

1. Specify the regression model

The model we are using here to be estimated is the linear model

Y = a + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + ε

Where: a = the constant and b1… b5 = the slope coefficients

Y is the output and X1...X5 are the variables inputs.

Y = Twelve-Month Average U.S. Domestic Price of Copper, Cents per Pound

X1 = Annual Gross National Product, Billions of pounds.

X2 = Twelve-Month Average Index of Industrial Production

X3 = Twelve-Month Average London Metal Exchange Price of Copper

X4 = Number of Housing Starts per Year, Thousands of Unit

X5 = Twelve-Month Average Price of Aluminum, Cents Per Pound.

ε = the error term, random variable with zero mean and constant variance.

2. Obtain data on the variables:

The data has been given by Dr.AbdulALhameed Mahboub.

They were collected by Gary R. Smith from sources such as American Metal Market, Metals Week, and U.S. Department of Commerce publications.

Regression Estimation And Analysis For Data Set D |

Determinants of U.S. Domestic Price of Copper |

Year | Y | X1 | X2 | X3 | X4 | X5 |

1951 | 21.89 | 330.2 | 45.1 | 220.4 | 1491 | 19 |

1952 | 22.29 | 347.2 | 50.9 | 259.5...