# Estimate the following two equations using OLS regression. Freeze and name the equations’ results. (5%) Yi = β1 + β2 Ni + β3 Pi + β4 Ii + ui (1) lnYi = β1 + β2 lnNi + β3 lnPi + β4 lnIi + ui (2) – Essaylink

Aims and learning outcomes:
This project will enable you to apply econometrics using Eviews. On completion of this coursework, you will be able to analyse data, interpret the Eviews output and apply statistics analysis skills to your own research.
Plagiarism Warning:
If plagiarism is suspected, your project will receive close scrutiny. All projects found to contain identical text will receive a mark of zero.
Submission Information (IMPORTANT):
Your project should be word-processed and well-organised in the original word document.
Download the data WOODY.xls and rename the data with your student number st_ _ _ _ _ .xls.
Screenshot your result/graph (with your unique workfile name in each picture) as the answer of the question.
Turnitin submission is required.

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Use the data in WOODY.xls for this question.

Economic theory indicates that the number of customers served in Woody’s restaurant (Y) could be affected by the following factors: the number of direct market competitors within a two-mile radius of the Woody’s location (N), the number of people living within a three-mile radius of the Woody’s location (P), and the average household income of people living within a three-mile radius of the Woody’s location (I).

Note: Answer will be marked according to assessment grading (below), however, content should demonstrate the understanding of the statistical methods.

Create a new workfile by importing data from Excel yourstudentnumber.xls file. (5%)

b) Obtain the descriptive statistics for all the variables. Freeze and name the obtained table. (5%)

In the space provided below, hypothesize the relationship (positive (+), negative (-), or no relationship (N/A)) between:

c) the number of customers served (Y) and the number of direct market competitors within a two-mile radius of the Woody’s location (N) (5%)

d) the number of customers served (Y) and the number of people living within a three-mile radius of the Woody’s location (P) (5%)

e) the number of customers served (Y) and the average household income of people living within a three-mile radius of the Woody’s location (I) (5%)

f) To visually confirm the hypothesized relationship, plot Y against N using a scatter graph with a regression line and Axes & Scaling/Data Scaling option set to linear – force zero. Keep the record of your graph by freezing and naming it. (5%)

g) Does the graph support your hypothesis or not? Why? (5%)

h) Estimate the following two equations using OLS regression. Freeze and name the equations’ results. (5%)
Yi = β1 + β2 Ni + β3 Pi + β4 Ii + ui (1)

lnYi = β1 + β2 lnNi + β3 lnPi + β4 lnIi + ui (2)

i) Report the results from equation (2) with 3 decimal points in the following table. (5%)

Equation (2)

Variable Coefficient Std error t-stat p-value
constant
lnN
lnP
lnI

j) Which of the estimated coefficients (excluding constant term) are statistically significant at 5% level? Why? (10%)

k) Interpret the estimated coefficients of lnP and lnI in equation (2). (10%)

l) In Equation (2), Does household income (I) have a significant effect on determing the number of customers served in Woody’s restaurant (Y) at 10% significance level? Why? (10%)

m) Report the confidence intervals with an Eviews screenshot for β2 at 90% and 99%? (10%)

n) What is the meaning of r-square in equation (2)? (10%)

o) Why most researchers automatically use adjusted r-square instead of r-square when evaluating the fit of their estimated regressions equations? (5%)

The post Estimate the following two equations using OLS regression. Freeze and name the equations’ results. (5%) Yi = β1 + β2 Ni + β3 Pi + β4 Ii + ui (1) lnYi = β1 + β2 lnNi + β3 lnPi + β4 lnIi + ui (2) appeared first on Accredited Research Writers.