Information Systems & Decision Sciences with EXCEL
These academic projects and assignments are based off of relevant Information Systems & Market Research courses I've taken at California State University of Fullerton. Feel free to download them to review further.
03
Multiple Regression Analysis
Used in the analysis of relationship between dependent and multiple independent variables and formula is represented by the equation Y is equal to a plus bX1 plus cX2 plus dX3 plus E where Y is dependent variable, X1, X2, X3 are independent variables, a is intercept, b, c, d are slopes,
04
Forecast/TimeSeries Analysis
Anyone who has consistent historical data can analyze that data with time series analysis methods and then model, forecasting, and predict. For some industries, the entire point of time series analysis is to facilitate forecasting
05
Linear Programming
A form of mathematical optimisation that seeks to determine the best way of using limited resources to achieve a given objective. The key elements of a linear programming problem include: Decision variables: Decision variables are often unknown when initially approaching the problem
06
Profit & Loss Projections
The Profit and Loss forecast is a key element of the financial forecast. It enables the project owner to anticipate the financial aspects of his business and potential investors to evaluate the fundamental elements of business, which are its growth, profitability and cost structure.
07
MRI-Simmons Research
MRI-Simmons combines the two largest and most respected consumer survey companies in the US (MRI and Simmons Research). With thousands of attitudinal and behavioral data points, gathered through ongoing surveys and passive measurement, MRI-Simmons empowers advertisers, agencies and media companies with deeper insights into the “why” behind consumer behavior.
08
CSUF Qualtrics Student Survey Research Project
In this project, our group was tasked with understanding the various factors influencing students' decision-making processes when selecting courses. The aim was to identify key determinants such as scheduling flexibility, course content relevance, instructor reputation, and peer recommendations.
Methodology:
Questionnaire Development: Designed a comprehensive questionnaire to capture both quantitative and qualitative data regarding student preferences and priorities. The questionnaire included demographic questions, Likert-scale questions for preference ratings, and open-ended questions to gather qualitative insights.
Sampling Technique: Implemented stratified random sampling to ensure representation across different majors, years, and other demographic variables. This approach helped in minimizing sampling bias and enhancing the reliability of our findings.
Focus Groups: Conducted multiple focus group sessions to dive deeper into the qualitative aspects of student decision-making. These sessions helped in understanding nuanced perspectives that the questionnaire might not fully capture.
Data Analysis:
Excel Procedures: Utilized Excel for initial data cleaning and basic analytics, including frequency distribution and crosstab analysis.
Statistical Analysis: Applied ANOVA to understand if there were significant differences in decision-making factors across different student groups. Chi-square tests were used to examine the relationships between categorical variables.
IBM SPSS Statistics: Employed SPSS for more complex statistical analysis, focusing on correlation and regression analysis to identify significant predictors of course selection.
