QuestionQuestion

* Must include a written (paper submission, not electronic) report
* 2-3 pages total in length, Times New Roman font, 12-point
* Use of graphs (where appropriate) to show results
* Use 4 sections

Introduction – Statement of the problem,
Methods
Describe sampling / randomization
Describe how the survey/experiment was conducted
Hypothesis and testing procedure
Results - EDA
Summary of output of statistical analysis

Must include a graph of the results
Discussion
Overall summary of the project and what was learned.

* Choose one of following project options
a) Comparing two independent sample means or proportions
b) Paired data, two dependent sample means
c) Categorical (frequency count) data
e) Correlation
f) Linear Regression
g) ANOVA (comparing 2 or more means)

* Data can be obtained from
1) datasets on the internet (e.g. type in keywords and ‘datasets’ in Google),
2) other class activities where data are generated,
3) by your own experiment, or
4) by simulation.
Note, however, that sources such as Cengage or StatCrunch that provide complete worked examples, are NOT acceptable as sources of data for this assignment.

Solution PreviewSolution Preview

These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice. Unethical use is strictly forbidden.

1 Introduction
In this project assignment, we are going to discuss the applications of a linear regression model to deter-mine the “best” linear relationship among the two variables (one independent and the other dependent). The problem is to study the linear relationship between the mortgage interest rates (in percentage) and the home prices (in dollars) in some city. We treat the home prices as the dependent variable and denote it by Y . Similarly, the interest rates are treated as independent variable denoted by X.

2 Methodology
Model
A linear regression model tries to find a best linear relationship between Y and X using least square estimation, i.e., a relationship of the form
Y = a + bX
where a is the intercept and b is the slope parameter....

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