Quantitative Analysis for Business Decisions Term Paper Assignment

Research Paper: Each student is required to submit an independent research paper or applied statistical analysis project that utilizes one or more of the quantitative methods covered in this course (listed below). The selection of the topic is at the discretion of the student. Students employed full time, although not required, are expected to submit a statistical analysis project related to their employment. The research does not have to be unique to this course, that is, you could further develop a research paper previously or concurrently written for another class employing the quantitative methods covered in this course. The expectation is simply that you demonstrate an ability to independently apply one or more of the statistical tools learned in the course.

Each student is required, as part of the requirements of the course, to complete and submit a term paper addressing a topic relevant to our course subject matter. You are expected to demonstrate that you can formulate hypotheses, analyze data secured from independent sources, test the hypotheses, and summarize your results and findings. You are not expected to have earth shattering findings. The paper will be graded on each of the following areas although each area will not receive the same weight in the overall grade:

1. Style and format of the paper. Tables and charts should be numbered and titled with sources given. Footnotes or endnotes should be used.

2. References and data sources listed. Reference lists must include at least one journal article and may include other sources of information. The reference list must include and all data sources.

3. Analytical development, including background information, model/theoretical development, and presentation of research hypothesis.

4. Quantitative analysis, including statistical techniques used, model validation, level and correctness of the analysis, and interpretation of results.

6. Summary and conclusions.

Course Statistical Analyses

I. Review of Basic Statistical Concepts and Procedures

• Descriptive Statistics
• Probability Laws
• Discrete Probability Distributions
• Continuous Probability Distributions
• Sampling Distributions
• Confidence Interval Estimation
• Hypothesis Testing

II. Analysis of Enumerative Data

• Levels of measurements
• Instrument (questionnaire) design
• Bias in survey designs
• Survey sampling techniques
• Multinomial experiment- Chi Square test
• Contingency tables and independence tests

III. Experimental Design and the Analysis of Variance

• Single factor ANOVA
• Single factor ANOVA with blocking
• Two factor ANOVA
• Interaction
• Other experimental design
• Fixed and random effects
• Multifactor models
• Latin squares
• Simple and multiple comparisons and contrasts

IV. Regression and Correlation Analysis

• Simple correlation
• Simple linear regression
• Concepts and estimation
• Measures of goodness of fit
• Significance tests
• Confidence intervals
• Multiple Regression
• Concepts and estimation
• Using matrix algebra
• Simple and multiple correlation
• The ANOVA table
• Significance tests
• Curvilinear regression-Polynomial regression
• Model determination and estimation
• Selecting the appropriate degree
• Test for goodness of fit
• Model Selection
• All possible regressions
• Stepwise regression
• Qualitative and indicator variables in regression
• Dummy variables and covariance analysis
• Dummy variables with interaction piecewise regression
• Binary response models
• Violations of conditions (assumptions)
• Multicolinearity
• Heteroscedasticity
• Autocorrelation of errors

V. Analysis of Time Series Data and Forecasting

• Economic forecasting techniques
• Classical decomposition
• Trend models
• Seasonal adjustment
• Cyclical
• Component
• Irregular
• Single equation model formulation
• Forecast evaluation
• Correcting for first order autocorrelation
• Time series
• Forecasting models
• Naive models
• Moving average models
• Autogressive models
• Mixed autogressive-moving average models

VI. Non Parametric Methods (optional)

• Mann-Whitney U
• Krusgal Wallis
• One Way ANOVA
• Rank Correlation
• Runs test

VII. Decision Making Under Uncertainty Using Decision Theory (optional)

• Payoffs and Opportunity Loss
• Decision rules without probabilities
• Expected monetary value-Expected opportunity loss
• Posterior analysis (Bayesian)
• Pre posterior analysis
• Extensive form analysis
• Normal form analysis
• Risk and utility