Dependencies: AM 1001

Syllabus: Descriptive Statistics: Types of data (qualitative, quantitative, continuous, discrete, etc.); scales of measurement (nominal, ordinal, interval, ratio, etc.); data summarization: frequency table, cum. frequency table, histogram, bar chart, pie chart, percentiles, quartiles, 5 –number summary, Box plot, outliers; measures of location: mean, trimmed mean, median, mode; measures of dispersion: range, inter quartile range, variance, standard deviation; coefficient of variation, skewness, kurtosis, Probability: Probability definitions; counting rules, permutations and combinations, finite sample space, events, probability rules, conditional probability, independence, multiplication rule, Bayes’ theorem, One dimensional random variables; probability density function and probability (mass) function, cum. distribution function, expected value, variance, associated theorems, and moment generating function, distribution of functions of random variables. Discrete distributions: Uniform, Bernoulli, Binomial, Poisson, and applications; continuous distributions: Uniform, Exponential, Normal; central limit theorem with applications.

Evaluation Criteria: End-of-semester examination and assignments

Suggested Readings: Introductory Statistics (Perm S. Mann), Concise Course in A-Level Statistics (J. Crawshaw, J. Chambers), Statistics for Business and Economics (Joseph G. Van Matre, Glenn H. Gillreath)

Syllabus: Data collection: Importance of data collection, reasons of collecting data for a specific purpose, major steps in the data transformation process,Entering data: Data entry formats, types of data, exporting/importing data, handling data using spread sheets Data Organization: Variables and measurements: Accuracy of measurements (quality of scientific data), rounding numbers, levels of measurements, describe the form of the most significant relationships, primary and secondary data, advantages of using secondary data, Manipulation of data: Coding, sorting and ranking of data, calculations using data, merging files, Stacking/concatenating data sets, Organizing the data: Tabular display: The data array, frequency distributions, proportion and percentage distributions, cumulative distributions Presenting data in graphs: Frequency polygons, histograms, line charts, bar charts, pie charts, Scatter plots, Box plots, Examples using small data sets

Evaluation Criteria: End-of-semester examination and assignments

Suggested Readings: Statistics: An Introduction (Roger E. Kirk)

Syllabus: Planning of a survey, Questionnaire designing, Problems arising in the execution of a survey. Relationship between census and samples; steps involved in developing a sample survey, types of sampling methods, principles governing the design of questionnaires; major components of questionnaires. Mortality, Crude death rate, Standardization, Morbidity, Prevalence, Incidence, Life tables.

Evaluation Criteria: End-of-semester examination and assignments

Suggested Readings: Elements of Sampling Theory (Vic Barnet), Sampling Techniques (William Cocharn), Statistical Methods in Medical (Armitage)

Dependencies: ST1002

Syllabus: Data analysis based on basic distributions; introduction to random numbers; generation of random numbers; methods of simulation; application of Central Limit Theorem; outlier detection; identification of distributions. Demonstration using a real data set

Evaluation Criteria: End-of-semester examination and assignments

Suggested Readings: Statistics: An Introduction (Roger E. Kirk)

Dependencies: ST1006

Syllabus: The aim of this course is to familiarize the students with practical aspects of the theory course ST1006. This would include data analysis, exercises and familiarize with a statistical package.

Evaluation Criteria: Take-Home and In-Class practical assignments

Dependencies: AM 1001

Syllabus: Distribution of functions of a random variable; Geometric, Negative Binomial, and Hyper geometric distributions; Gamma, Chi-squared, and Beta distributions; relationships between distributions; two –dimensional random variables: joint distribution (discrete, continuous), marginal and conditional distributions, independence, Bivariate normal distribution, covariance, correlation, conditional expectation, expectation of functions of random variables; bivariate transformations (discrete and continuous); some important distributions: t and F. Order statistics.

Evaluation Criteria: End-of-semester examination and assignments

Suggested Readings: An Outline of Statistical Theory (Goon, Gupta and Dasgupta), Fundamentals of mathematical Statistics (Gupta and Kapoor)