Aims and Objective
The aim of this assessment is to develop and evaluate data-driven models based on simple and multiple regression models. It allows students to develop
and demonstrate the application of the methods of ordinary least squares using Excel. The assessment will consist of statistical analysis, graphs, analysis
and written report explaining your results and findings. This should be no more than 1500 words and should be typed, using ICP house style.

 Use an introduction to set the aim of the report explaining the problem you are examining.
 Structure the main body which should comprise of a discussion of your results.
 Summarise the main regression results including the estimated regression line, estimated regression coefficients, standard errors, t-ratios, coefficient of
determination and present regression summary analysis.
 Carry out hypothesis tests on regression coefficients and interpret your findings.
 Explain your graphs of regression line and statistical results clearly in the report.
 Summarise your findings/conclusion at the end of the report.
 Use references based on all the literature you have used in compiling this report. Use APA referencing system.
 Pay attention to the overall presentation, structure and ensure logical development of ideas.

Assessment Criteria
 Demonstrating competence in the production and presentation of results from Microsoft EXCEL
 Understanding of methods employed,
 Providing appropriate analysis, explanation and interpretation of results,
 Structuring and presenting the report clearly,
Coursework Brief

Assignment;
Examine a time series data of demand for coffee in United Kingdom
from 1990 to 2016. As part of this assignment you will examine the market demand of
coffee model and evaluate the significance of variables influencing consumer behaviour.
In section A, use a simple two variable model to investigate the relationship between
demand and real price of coffee and real disposable income separately. You are expected to
analyse the regression results and comment on your findings.
In section B, you are expected to use multiple regression analysis and comment on your
findings.

Data
The following data is provided in an excel file and can be downloaded. The table show a
time series data of demand for coffee, the real price of coffee, and the real personal
disposable income of the consumer over the years (1990 to 2016) in the United Kingdom.
A and B. Remember you are expected to conduct descriptive statistics and inference
statisitics for the both sections
Section (A) Simple Linear Regression Model [40 marks]
1). Plot a separate scatter diagrams of demand for coffee, Y, against, ?1, real price of coffee
and for demand for coffee Y, and real personal disposable income, ?2. Comment on kind of
relationship that exit?
2). Assuming that the demand for coffee, ?, and real price of coffee, ?1, are linked by a
linear relationship, estimate this regression by Ordinary Least Squares (OLS) method, clearly
showing all your calculations (Excel can used for all the computations). [10 marks]
?? = ?(?? ) + ?? = ?? + ???1? + ?? 
3). Estimate the coefficient of determination – ?2 and comment on its value. Carry out an
appropriate test at 5% significance level for the explanatory power of the model.

4). Assuming that annual demand for coffee, ?, and real personal disposable income level,
?2 are linked by a linear relationship, estimate this regression by Ordinary Least Squares
(OLS) method using Excel:
?? = ?(?? ) + ?? = ?2 + ?2?2? + ?? 
5). Carry out an appropriate test at 5% significance level for the explanatory power of the
model, using the information in the regression summary output. [4 marks]
Section (B) Multiple Regression Analysis
It is reasonable to assume that demand for coffee depends on both the real price of coffee
and real disposable income. Use multiple regression analysis to investigate the relationship
between ? ??? ?1 ??? ?2 .
6). Estimate the following linear regression model using the data set:
?? = ?(?? ) + ? = ?3 + ?3?1 + ?4?2 ?? 
7). Compare the estimated coefficient for the real price of coffee, ?1, from the regression
equation (1) in section A and multiple regression equation (3) in section (B). Are they