Data Analysis and modeling are essential tools used across a wide range of disciplines to understand, represent, and solve complex problems. These processes help break down intricate systems into manageable components, analyze their behavior, and create mathematical or computational models that predict outcomes or optimize decisions.
In the context of business, analysis and modeling enable organizations to make informed decisions by understanding market trends, consumer behavior, and operational efficiency. In the fields of science and engineering, they are used to simulate real-world systems and forecast future events, helping to improve design, innovation, and problem-solving.
Table of Contents
Syllabus of Data Analysis and Modeling
Course Objectives of Data Analysis and Modeling
This course aims to acquaint students with major statistical and quantitative tools used in the modeling and analysis of business decisions involving choices.
Course Description of Data Analysis and Modeling
The component of the course includes regression analysis and models, time series analysis, and forecasting, linear programming models and applications, transportation and assignment models, and network models.
Course Outcomes of Data Analysis and Modeling
By the end of this course, students will be able to
- Calculate and interpret the meaning of correlation coefficient to measure the strength of the relationship between two numerical variables,
- Calculate and interpret the meaning coefficient of determination to measure the predictive power of the simple as well as multiple regression,
- Forecast the future values using various models, and
- Optimize the resources in the business decision-making process.
Course Contents of Data Analysis and Modeling
Unit I Simple Correlation and Regression Models:
Measuring and Predicting Relationships 8 hours
Correlation: Meaning, Scatter plot, Karl Pearson correlation coefficient, Test of correlation coefficient. Simple Linear Regression: Predicting of One Variable from Another Statistical model, Least square regression- assumptions, Standard error of estimate, Coefficient of determination, Residual Analysis, Testing of regression coefficient.
Unit II Multiple Regression Models:
Predicting One Factor from Several Others 8 hours
Multiple regression model, Standard error of estimate, Coefficient of determination, Significance of regression model, Test of significance of regression coefficients (Which variables are significant and explaining the most?), Model building, Curvilinear models, Qualitative variables, Stepwise regression, Residual analysis, Multi-co linearity.
Unit III Index Number and its Construction Models 5 hours
Introduction, Definition of an index number, Uses of an index number, Types of an index number, Methods of constructing index number, Base shifting, Deflation, Cost of living index.
Unit IV Time Series and Forecasting Models 10 hours
Index number, Understanding time series analysis, Decomposition of time series, Cyclic variation, Seasonal variation, Depersonalizing the time series data (Ratio to moving average method), Choosing the appropriate forecasting technique, Moving average, Exponential smoothing, Regression based linear and curvilinear trend models, Measures of forecast accuracy (MAD, MAPE, and MSE).
Unit V Introduction to Optimization Models 12 hours
Review of Linear Programming Model: Problem formulation, Graphical solution, special cases, Duality in LP Transportation Model: Vogel’s Approximation Method only Assignment Model: Hungarian Method only
Unit VI: Network Models 5 hours
Introduction, Critical Path Method (CPM), Project Evaluation and Review Technique (PERT), Network diagram, Probability in PERT analysis
Basic Texts
1. Davis, G., & Pecar, B. Business Statistics using Excel. New Delhi: Oxford University Press
3. Eppen, G. D., Gould, F. J. & Schmidt, C.P. Introductory Management Science. New Delhi: Prentice Hall
References
1. Levin, R. I., & David S. R. Statistics for Management. New Delhi: Prentice Hall of India.
2. Panneerselvam, R. Research Methodology. New Delhi: PHI Learning Private Limited.
3. Allbright, S. C., Winston, W., & Zappe, C. J. Data Analysis and Decision Making with Microsoft Excel. Pacific Grove: Duxubury Press.
4. Argyrous, G. Statistics for Research with a Guide to SPSS. New Delhi: Sage South India Edition
5. Whigham, D. Business Data Analysis using Excel. New Delhi: Oxford University Press
Conclusion
Analysis and modeling are powerful tools that enable individuals and organizations to tackle complex problems and make informed decisions. By breaking down systems into manageable components, analyzing data, and creating predictive models, we can gain deeper insights into behavior, optimize processes, and forecast future outcomes.
In business, science, engineering, and other fields, effective analysis and modeling lead to better decision-making, improved efficiency, and innovative solutions. The ability to apply these techniques is crucial in today’s data-driven world, where accurate predictions and optimized strategies are key to success. As technologies continue to evolve, the scope and potential of analysis and modeling will only grow, providing new opportunities for problem-solving and growth across various industries.
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