Utilized R, Excel, & Tableau to independently conduct a full Econometric Research Project through: Gathering Data & Creating Datasets, Cleaning Datasets, Conducting Exploratory Data Analysis, Creating a variety of models and tests, then visualizing the data and writing a 20 page research paper.

This project focused on how the World Happiness Index was related to a variety of variables, which I narrowed down to seven in the project. I utilized SLR, MLR, log transforms, A-B testing, and F-Tests in the project. E-mail me at mab1096unh@gmail.com for the full paper!

This project focused on identifying the relationship between education, poverty, and economic growth. There were two parts of this project, the first where I worked with a group to research the problem and do the Economic Analysis & Literature Review areas, and the second where I worked independently to conduct the full Data Science/Econometric process. We each recognized our strengths and worked together cohesively to complete the project and present at the UNH Paul College Undergraduate Research Conference.

In this project I utilized R, Excel, & Tableau to independently gather data, create datasets, clean the data, conduct exploratory data analysis, create models and tests, then visualize and present the results in a clear understandable way at the PCBE Undergraduate Research Conference.

The types of models created were: SLR’s, MLR’s, & Log-Transforms, which were then ran through tests.

E-mail me → mab1096unh@gmail.com for the full research paper.

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Created models to analyze how Income is related to Education & Work Experience. Created multiple two-sided t-tests on each variable in their respective SLR models and added 9 occupation groups to the model to adjust for different pay across occupations, along with different education and experience requirements. After this I conducted further analysis, then created different restricted and unrestricted models to conduct 2 f-tests on each primary model and finally visualized the data using Tableau.

Metrics Final - Data retrieved from "Exam_data5_port".

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Multiple Econometrics Projects
Part 1:
CigInc Health Effects Project: Regression Models (SLR & MLR) on how Cigarette use, Family Income Levels, & Birth weight influence one another. Data retrieved from the Wooldridge package in the "bwght" dataset.
Part 2:
House Price Estimation/Prediction: Regression model (MLR) on whether an individual paid over or under the estimated/predicted house price based on the size and number of bedrooms. Data retrieved from the Wooldridge package in the "hprice" dataset.
Part 3:
Predicting Attendance Rates based on ACT score and GPA: Regression model (MLR) on predicted attendance rated based on ACT score and GPA. Data retrieved from the Wooldridge package in the "attend" dataset.
Part 4:
How Net Financial Assets is correlated with Annual Income and Age Increase: Regression model (MLR) on showing the relation between Net Financial Assets, Annual Income, and Age Increase, along with another model on single households. Data retrieved from the Wooldridge package in the "401ksubs" dataset.
Part 5:
Mathematical Regression based on set parameters Regression models (3 total, 1 SLR, 2 MLR) on given mathematical problems, and practicing using variables in R, dataset created in R.

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Utilized Power B.I., Power Query, Power Pivot, SQL, and Excel to organize, automate, and create responsive visualizations that are updated as new data comes in.

This project was from my Advanced Excel Data Analytics class. The Sales & Logistics Analysis project is based on common situations that Data and Business Analysts encounter when analyzing data and effective ways to solve these problems through creating responsive datasets that update directly from multiple databases and sources, responsive data paths and configurations, and responsive visualizations.

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The Credit Analysis with KNN project was one of the final projects for my Predictive Modeling class.

In this project I utilized R to conduct a K Nearest Neighbor regression model and used Credit data to identify the likelihood of default based on a variety of characteristics and creditworthiness based on the defined parameters. I then visualized the data with Tableau.

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In this Housing Financial Analysis project, Logistic Regression was used to analyze how House Prices change as a result of a variety of characteristics/parameters: the size in square feet, number of bedrooms, lot size, and distance from newly built negative external structures (like an incinerator built) and how each of these influence the projected house price.

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The Healthcare project was a part of another final project for my Predictive Modeling class where I utilized: R, Excel, and Tableau to analyze Healthcare outcomes and rates based on the results.

Log. Transforms were utilized to fit the data to a Normal Distribution since the context of the given healthcare data fits into this format well without overfitting/confounding the data.

The Test & Training models along with the Logistic Regression aim to identify the related outcomes and rates according to the available data in the context of Healthcare Insurance Analysis.

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