## CME371 Syllabus - Advanced Statistics And Data Analytics - 2021 Regulation Anna University

CME371

L T P C

3003

COURSE OBJECTIVES:
1 To introduce the basic concepts of linear regression and multiple regression
2 To introduce exploratory data analysis
3 To study logistic regression models for classification
4 To develop the forecasting techniques for the predictions
5 To introduce the time series analysis for the prediction of future behavior

UNIT I

REGRESSION

9

Introduction – Linear regression - Correlation analysis -Limitations, errors, and caveats of using regression and correlation analyses - Multiple regression and correlation analysis - Inferences about population parameters – Modeling techniques. - Coefficient of determination, Interpretation of regression coefficients, Categorical variables, heteroscedasticity, Multi-co linearity outliers, Ridge regression.

UNIT II

EXPLORATORY DATA ANALYSIS

9

Rise of statistics, Data Wrangling, Data Quality. Visual encoding – Mapping Data to Visual Variables, Encoding Effectiveness, Scales & Axes, Aspect Ratio, Regression Lines, Multidimensional Data, Parallel Coordinates, Dimensionality Reduction.

UNIT III

LOGISTIC AND MULTINOMIAL REGRESSION

9

Logistic function, Estimation of probability using Logistic regression, Variance, Wald Test, Hosmer Lemshow Test, Classification Table, Gini Co-efficient.

UNIT IV

FORECASTING AND CAUSAL MODELS

9

Moving average, Exponential Smoothing, Casual Models.

UNIT V

TIME SERIES ANALYSIS

9

Auto regression (AR), Moving Average(MA) Models, ARMA, ARIMA models , Multivariate Models

TOTAL: 45 PERIODS

OUTCOMES: At the end of the course the students would be able to
1. Develop how to do regression fit for the given data.
2. Visualize the data through explanatory data analysis
3. Classify the given data through logistic regression
4. Analyzing forecasting techniques and causal inferences.
5. Utilize the effective time series analysis to predict/forecast the future behavior of data.

TEXT BOOKS:
1. Douglas C Montgomery and George C Runges, “Applied Statistics and Probability for Engineers”, John Wiley & Sons, 2014.
2. Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulachi, “Introduction to Time Series Analysis and Forecasting” ,Wiley,2015

REFERENCES:
1. David Forsyth, ‘Probability and Statistics for Computer Science’, Springer; 2018
2. Michael J. Evans, Jeffrey S. Rosenthal, ‘Probability and Statistics - The Science of Uncertainty’. W H Freeman & Co, 2010
3. Max Kuhn, Kjell Johnson, “Applied Predictive Modeling”, Springer, 2014.
4 Ronald E. Walpole, Raymond H. Meyers, Sharon L. Meyers, “Probability and Statistics for Engineers and Scientists”, Pearson Education, 2014.
5 Daniel T. Larose, Chantal D. Larose “Data Mining and Predictive Analytics”, Wiley,2015
6. Thomas W.Miller, “Modeling Techniques in Predictive Analytics with Python and R: A guide to Data Science”, Pearson Education, 2014.