Course Archives    Theoretical Statistics and Mathematics Unit

Course: Statistics for Decision Making I Level: Postgraduate Time: Currently not offered

Syllabus Past Exams Syllabus 1. Introduction (8) Definition of Statistics, Descriptive and Inferential Statistics, Basic objectives, Applications to various disciplines with examples, Abuses of statistics, Impact of Computer on Data analysis. Primary and Secondary data, Population and Sample, Representative Sample, Types of data – continuous and discrete, frequency and nonfrequency data, variable and attribute, Types of measurement scale, Likert scale. 2. Descriptive Statistics (16) Scrutiny, classification, and tabulation of univariate data. Data visualization – bar plot, line diagram, column diagram, Histogram, pie chart, Box plot, Multi-vari chart. Descriptive statistics – central tendency, dispersion, quantiles, Skewness and Kurtosis; various properties of these measures and their utility. Bivariate data – definition and real-life examples, marginal and conditional frequency distribution, Scatter diagram, Simple linear regression, correlation, least squares method, Rank correlation. 3. Association of Attributes (8) Definition of attributes with real life examples, Contingency table, Sensitivity and specificity, Types and measures of association, Odds ratio, Cohens Kappa. 4. Sampling Techniques (8) Random sampling, Bias and its sources, Sampling from finite and infinite populations, Estimates and standard error (sampling with replacement and sampling without replacement), Sampling distribution of sample mean, Stratified random sampling, Systematic sampling, Cluster sampling. 5. Fitting probability distribution (6) Empirical distribution function, Kernel density estimation. Fitting probability distributions to observed data. Goodness of fit using Pearson’s χ2, and P-P and Q-Q plots. 6. Simulation (4) Simulation from probability distributions, Monte Carlo simulation. Reference Texts: 1. Spiegelhalter, D. (2019). The art of statistics: How to learn from data. Pelican Books. 2. Holcomb, Z. (1998). Fundamentals of descriptive statistics. Routledge, Taylor & Francis Group. 3. Jones, J. S., & Goldring, J. (2022). Exploratory and descriptive statistics. Sage Publishers. 4. Montgomery, D. C., & Runger, G. C. (2016). Applied statistics and probability for engineers (6th ed.). Wiley. 5. Mood, A. M., Graybill, F. A., & Boes, D. C. (2017). Introduction to the theory of statistics (3rd ed.). McGraw-Hill Education. 6. Goon, A. M., Gupta, M. K., & Dasgupta, B. (1978). Fundamentals of statistics (Vols. 1–2). World Press. 7. Rencher, A. C.,& Christensen, W. F. (2012). Methods of multivariate analysis. Wiley 8. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). Wiley. 9. Thomopoulos, N. T. (2013). Essentials of Monte Carlo simulation. Springer 10. Robert, C. P., & Casella, G. (2004). Monte Carlo statistical methods. Springer. Top of the page Past Exams

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