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Other Names of this Course / Subject
Statistics, Probability, stat
Syllabus
(i) Statistics - definition and scope: definitions of statistics - past and present, its nature and characteristics, population and sample, descriptive and inferential statistics, scope and applications of statistics, abuse of statistics, sources of statistical data, primary and secondary sources. Data collection tools, types, etc. Construction of questionnaire and other field problems of data collection. Types of data, cross-sectional, longitudinal, follow-up, and panel data.
(ii) Presentation of data: graphical presentation of data, details of different types of graphs and charts with their relative merits and dements, the concept of explorative data analysis, stem-and-leaf plot, schematic plots, extremes and median, hinges, outliers, and 5 number summaries, measures of location, dispersion, skewness, kurtosis, and their properties, moments box -and- whiskers plots, trimaran, trimmed mean, interpretation of data with these measures.
(iii) Correlation analysis: bivariate data, scatter diagram, simple correlation, rank correlation, correlation ratio, multiple and partial correlations, intraclass and biserial
correlation.
(iv) Regression analysis: basic concept of regression, regression model, estimation of parameters (OLS method) in the regression model, properties of estimators, interpreting the constants, some ideas of polynomial regression, 3-variable regression, estimation of parameters, standard error, and other properties.
(v) Elements of probability: experiment, random experiment, sample space events, event space, union and intersection of events, and different types of events.
(vi) Basic concepts of probability: different approaches of defining probability- classical, axiomatic, empirical, and subjective, laws and theorems of probability, conditional probability, Bayes' theorem and its importance in statistics.
(vii) Random variable and its probability distribution: discrete and continuous random variables, probability mass function, probability density function, distribution function, the function of the random variable and its distribution, joint distribution, marginal and conditional distributions, and independence of random variables.
(viii) Mathematical expectation: concept, expectations of sums and products of random variables, conditional expectation and conditional variance, moments and moment generating functions, cumulants and cumulant generating functions, the relation between moments and cumulants, probability generating functions, characteristic function.
(ix) Some basic distributions: a detailed study of binomial, Poisson, normal, uniform, geometric, negative binomial, hypergeometric, exponential, gamma, and beta distributions.
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Barishal University: 6th Batch
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