{"id":3185,"date":"2023-12-06T13:24:54","date_gmt":"2023-12-06T10:24:54","guid":{"rendered":"https:\/\/sebhau.edu.ly\/sc\/?p=3185"},"modified":"2023-12-28T12:00:17","modified_gmt":"2023-12-28T09:00:17","slug":"description-of-courses-statistics","status":"publish","type":"post","link":"https:\/\/sebhau.edu.ly\/sc\/en\/description-of-courses-statistics\/","title":{"rendered":"Course Description"},"content":{"rendered":"

Descriptive of Compulsory Statistics Courses<\/u><\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

ST101 : General Statistics I\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
    \n
  • General concepts, Concept of statistics and its relation ship to other science.<\/li>\n
  • Statistical data and methods of collecting data.<\/li>\n
  • Frequency distribution, Cumulative frequency dist.<\/li>\n
  • Geometrical representation of data.(graphical)<\/li>\n
  • Measures of central tendency (Mean, Mode, Median, geometric mean, harmonic mean percentiles.)<\/li>\n
  • Measures of dispersion (range, mean deviation, variance, standard deviation, standard error of the mean pooled variance).<\/li>\n
  • Properties of (Variance, Coefficient of variation, Standard Scores, Moments, Skew ness, Kurtosis).<\/li>\n
  • Simple regression and correlation (fundamental idea).<\/li>\n
  • Concept of probability (fundamental idea).<\/li>\n
  • Binomial distribution.<\/li>\n
  • Normal distribution.<\/li>\n<\/ul>\n

    ST201 : General Statistics II<\/u><\/strong> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0\u00a0 4<\/u> <\/strong>\u2022<\/h2>\n

    General review of (ST101), Sampling distribution of the sample mean. General concepts and properties of Chi-squarer, Student-t, Snedecor-F distribution, relationship between t and F, population parameters. Concept of interval estimation, Confidence limits for single mean, difference between two means, single proportion, difference between two proportions.<\/p>\n

      \n
    • Correlation: general concepts, simple, partial, multiple correlation coefficient. Test and confidence limits concerning simple correlation.<\/li>\n<\/ul>\n

      Spearman\u2019s rank correlation coefficient, contingency and association coefficients. Chi-square test for independency and association contingency tables. Regression: general Concepts, linear regression (with no more than two independent variables), estimation, test and confidence limits concerning regression parameters.<\/p>\n

      \u00a0<\/strong><\/p>\n

      ST202 : Probability I<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/u> <\/strong>\u2022<\/h2>\n

      Counting methods (basic counting method, permutations, combinations, (with replacement and without replacement)).<\/p>\n

        \n
      • Random experiment.<\/li>\n
      • <\/li>\n
      • Probability theory (definitions, axioms and theorems).<\/li>\n
      • Conditional Probability<\/li>\n<\/ul>\n

        Laws of probability.(Addition & Multiplication law)<\/p>\n

          \n
        • Bayes theorem.<\/li>\n
        • <\/li>\n
        • Random variables (definitions).<\/li>\n
        • Probability Mass Function. & Probability Density Function<\/li>\n
        • Distribution function.<\/li>\n
        • Properties of random variable (expectation and its properties, variance and its properties, moments and moment generating function, probability generating function).<\/li>\n
        • Simple idea about Chebyshev\u2019s Inequalities.<\/li>\n
        • Simple idea about Binomial, Poisson, Normal dist.<\/li>\n<\/ul>\n

          ST301 : Probability II<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 4<\/u> <\/strong>\u2022<\/h2>\n

          Introduction (a simple review of ST202).<\/p>\n

            \n
          • Probability generating function.<\/li>\n
          • Characteristic function.<\/li>\n
          • Some measures about distributions ( mode, median, mean, variance, skew ness, kurtosis)<\/li>\n
          • Some parametric families of discrete distributions and their properties: (Bernoulli, Binomial, Poisson, Geometric, Negative Binomial, Hyper Geometric).<\/li>\n
          • Some parametric families of continuous distributions ( Uniform, Normal, Log-Normal, Gamma, Exponential, Beta)<\/li>\n
          • Joint, marginal and conditional distributions.<\/li>\n
          • Conditional expectations, covariance, conditional variance Multinomial dist.<\/li>\n<\/ul>\n

            ST302 : Statistical Method I<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0\u00a0 4<\/u> <\/strong>\u2022<\/h2>\n

            Introduction, samples: sample and population, statistics and parameter, mean, variance and standard error of a sample, estimator of population variance, pooled variance.<\/p>\n

              \n
            • Linear combinations of random variables. Calculating the mean and variance of a linear combination.<\/li>\n
            • Sampling from Normal Population, the distribution of linear combination. of normal dist.<\/li>\n
            • The mean and variance of a sample.<\/li>\n
            • Test of Hypothesis : Null and Alternative hypothesis, critical region, type I & type II error, level of significance, degree of freedom.<\/li>\n
            • Using statistical tables for\uf0632 <\/sup><\/strong>, t, F, and Z.<\/li>\n
            • General concepts and notations concerning test of significant, Some significant tests based on:<\/li>\n<\/ul>\n

              Normal dist. (test for single mean, difference between two means).<\/p>\n

              t-dist. (test for single mean, difference between two means). \uf0632 <\/sup><\/strong>– dist. (test for single variance, goodness of fit, the homogeneity of several variances).<\/p>\n

              F- dist. (test for the ration between two variances, one and two way analysis of variance). Test for regression coefficients.<\/p>\n

              \u00a0<\/strong><\/p>\n

              ST401 : Mathematical Statistics I\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
                \n
              • Introduction to distribution theory.( Cauchy, Laplace, Weibull, Logistic, Pareto and Gambel dist.)<\/li>\n
              • Distributions of functions of random variables.<\/li>\n
              • Expectations of functions of random variables( with referring to the mean and variance of such functions).<\/li>\n
              • Concluding of distributions using.\n
                  \n
                1. Distribution function.<\/li>\n
                2. Moment generating<\/li>\n
                3. Transformation techniques.<\/li>\n<\/ol>\n<\/li>\n
                4. Exact sampling distributions:<\/li>\n<\/ul>\n

                  \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Derivation of chi-square, student-t and F-distributions.<\/p>\n

                    \n
                  • Exact sampling distribution of the sample mean and variance from normal population.<\/li>\n
                  • Introduction to Order statistics, Weak law of large number, Central limit theorem.<\/li>\n<\/ul>\n

                    ST402 : Sampling Techniques I\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
                      \n
                    • Definitions<\/li>\n
                    • Purpose & advantages of sampling<\/li>\n
                    • Census versus sample surveys<\/li>\n
                    • Various stages of sample surveys<\/li>\n
                    • Random and non-random samples( with and without replacement )<\/li>\n
                    • Simple random sampling(properties of the estimates, tables of random numbers, sampling of proportions and percentages, confidence limits for the populations estimates, determination of sample size).<\/li>\n
                    • Stratified random sampling (notations, properties of the estimate, determination of sample size using proportional allocations, optimum allocation, Neyman allocation, confidence limits for the estimates, sampling of proportions and percentages)<\/li>\n
                    • relative precision of stratified sampling versus simple random sampling<\/li>\n
                    • Estimation of means and standard errors<\/li>\n
                    • Estimation of proportions<\/li>\n<\/ul>\n

                      Estimation of sample size for a given precision.<\/p>\n

                      ST403 : Correlation & Regression Analysis\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
                        \n
                      • Bivariate Dist., Correlation<\/li>\n
                      • Scatter Diagram<\/li>\n
                      • Karl Person coefficient of correlation<\/li>\n<\/ul>\n

                        Limits for Correlation coefficient<\/p>\n

                        Assumptions \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 underlying \u00a0 Karl \u00a0 Person \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 coefficient \u00a0 of correlation<\/p>\n

                          \n
                        • Probable Error of Correlation coefficient<\/li>\n
                        • Rank Correlation Repeated Ranks<\/li>\n<\/ul>\n

                          Limits for the Rank Correlation coefficient<\/p>\n

                            \n
                          • Concept and Assumptions of regression analysis.<\/li>\n
                          • Least square estimates and their properties including Gauss-Markov theorem.<\/li>\n
                          • Linear regression analysis<\/li>\n
                          • Point estimate of the parameters and their confidence intervals<\/li>\n
                          • Test of linearity of regression<\/li>\n
                          • Correlation coefficient between observed and Estimated Value, Correlation Ratio<\/li>\n
                          • Multiple and Partial Correlation ( properties, multiple Correlation can be expressed in terms of total and partial Correlation)<\/li>\n<\/ul>\n

                            \u00a0<\/strong><\/p>\n

                            \u00a0 ST404 : Time Series Analysis \u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0 3<\/h2>\n
                              \n
                            • Concept of time series, Objective of time series analysis, Kinds and components of time series, Time series Models.<\/li>\n
                            • Analysis of time series<\/li>\n
                            • Smoothing, Linear trend (Curve fitting, Filtering, differencing),<\/li>\n<\/ul>\n

                              Moving average, Seasonal fluctuations, Cyclical fluctuations, Autocorrelation, Autocorrelation function (ACF), Interpreting the ACF\u00a0 .<\/p>\n

                                \n
                              • Tests for linearity of time series, prediction, calculation of seasonal index.<\/li>\n
                              • Non-linear trend (2nd and 3rd degree, logarithmic, exponential, and logistic models)<\/li>\n
                              • Index numbers:<\/li>\n
                              • Concept, methods of constructions of index numbers<\/li>\n
                              • Sources of errors in constructing index numbers.<\/li>\n
                              • Standard of living.<\/li>\n
                              • Different tests of index numbers.<\/li>\n<\/ul>\n

                                \u00a0 ST501 : Sampling Techniques II \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0 3<\/h2>\n
                                  \n
                                • Introduction<\/li>\n
                                • Ratio and regression methods of estimation with and without stratification, Combined Ratio estimate<\/li>\n
                                • Difference and product estimation<\/li>\n
                                • Unbiased Ratio type estimate<\/li>\n
                                • Census verses sample surveys<\/li>\n
                                • Unbiased ratio type estimate<\/li>\n
                                • Systematic sampling<\/li>\n
                                • Single stage and cluster sampling<\/li>\n
                                • Estimation of means, totals & variance for simple random and stratified sampling<\/li>\n
                                • Ideas of unequal Probability sampling<\/li>\n<\/ul>\n

                                  \u00a0 ST502 : Theory Of Estimation \u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0 3<\/h2>\n
                                    \n
                                  • Introduction<\/li>\n
                                  • definitions of sample, population, parameter, statistic and estimator. Types of estimators<\/li>\n
                                  • Point estimation :<\/li>\n
                                  • Properties of estimator: unbiased, consistency, efficiency and sufficiency, minimum variance unbiased estimation<\/li>\n
                                  • Methods of estimation: Method of Moments, Method of Maximum likelihood, Minimum variance, Least squares and Minimum chi-square, Mention of Bayesian method<\/li>\n
                                  • Rao-Cramer Inequality Cauchy – Schwart\u2019s Inequality<\/li>\n
                                  • Interval estimation<\/li>\n
                                  • Some fundamental notions of interval estimation, applications for obtaining confidence limits for parameters (means , variances and proportions, equal & Unequal, known & unknown population Variances) of some standard distributions (Normal).<\/li>\n
                                  • Linear function of means of independent random sample,<\/li>\n
                                  • Bayesian estimator and their uses<\/li>\n<\/ul>\n

                                    \u00a0<\/strong><\/p>\n

                                    ST503 : Statistical Method II\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0 3<\/h2>\n
                                      \n
                                    • Scientific method of social survey<\/li>\n
                                    • Natural and purpose of social survey<\/li>\n
                                    • Important of research, formulation of research problem & hypothesis Field experiments and surveys, Types of survey, Designing survey.<\/li>\n
                                    • Natural of Designing of laboratory experimentation, Collection of data.<\/li>\n
                                    • Kind of questionnaires and canvass.<\/li>\n<\/ul>\n

                                      The problem of non-response, Field organization.<\/p>\n

                                        \n
                                      • Training and supervision of field investigation.<\/li>\n
                                      • Presentation of report, Familiarity with large sample surveys.<\/li>\n<\/ul>\n

                                        \u00a0<\/strong><\/p>\n

                                        \u00a0 \u00a0ST504 : Analysis Of Variance \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0 4<\/h2>\n
                                          \n
                                        • Concept and assumptions underlying ANOVA<\/li>\n
                                        • One-way Analysis of Variance<\/li>\n
                                        • Mathematical model<\/li>\n
                                        • Fixed, mixed and random models.<\/li>\n
                                        • Equal & unequal sample size, ANOVA tables, Expected of mean square<\/li>\n
                                        • Test for homogeneity, Bartlette and Cochran transformations, Comparisons and contrasts, Least significant difference(LSD), Tukey method, Sheffe\u2019 method, Duncan multiple range test.<\/li>\n
                                        • Two-way Analysis of Variance<\/li>\n
                                        • Mathematical model (without interaction & with interaction)<\/li>\n
                                        • Fixed, random and mixed models.<\/li>\n
                                        • ANOVA tables, Expected of mean square<\/li>\n
                                        • Three way ANOVA<\/li>\n
                                        • Mathematical model(without interaction \u00a0 & \u00a0\u00a0\u00a0\u00a0\u00a0 with interaction)<\/li>\n
                                        • Fixed, random and\u00a0 mixed models.<\/li>\n
                                        • ANOVA tables, Expected of mean square\u00a0 One-way-two factor ANOVA with and without replications.<\/li>\n
                                        • Analysis of covariance one-way-one factor ANOCOV, two-way-one factor ANOCOV.<\/li>\n<\/ul>\n

                                          ST506 : Quality Control & Reliability\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0 3<\/h2>\n
                                            \n
                                          • Meaning and objective of Statistical Quality Control<\/li>\n
                                          • Different techniques of achieving Statistical Quality Control<\/li>\n
                                          • Different types Quality measures<\/li>\n
                                          • Rational subgroups and technique of Quality Control charts (3\uf073 limits and probability limits, control charts for mean, S.D. and range , control charts for number of defectives, fraction defective and percent defective<\/li>\n
                                          • Control Charts for attributes (P-chart & C-chart)<\/li>\n
                                          • Control Charts for variables (P-chart & C-chart)<\/li>\n
                                          • Operating characteristic curve. Acceptance sampling plans (single, double plans).<\/li>\n
                                          • Meaning of Reliability<\/li>\n<\/ul>\n

                                            ST601 : Test Hypotheses \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0 3<\/h2>\n
                                              \n
                                            • Introduction, definitions and notations. Concept of simple and composite hypothesis, test of statistical Hypothesis, types of errors, power of a least, power function, Critical region.<\/li>\n
                                            • Most powerful test and Uniformly most powerful lest, Neyman-Pearson lemma. Likelihood ratio test,<\/li>\n
                                            • Determination of sample size, BCR, MP.<\/li>\n
                                            • Applications (Tests about means, variances, proportions, regression, correlation coefficients Chi-square test: goodness of fit &independence).<\/li>\n
                                            • Idea about SPRT, approximation expected sample size of a SPRT.<\/li>\n<\/ul>\n

                                              \u00a0 ST602 : Experimental Design \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0 4<\/h2>\n
                                                \n
                                              • definitions<\/li>\n
                                              • Model, treatments, experimental unit, block, replication and experimental error<\/li>\n
                                              • The rule of statistics: interpretation and analysis, Replication, randomization, Control of measurement of efficiency.<\/li>\n
                                              • Completely Randomized designs: model, partitioning of Total<\/li>\n<\/ul>\n

                                                SS, ANOVA, Estimation of parameters, Multiple contrasts<\/p>\n

                                                (LSD, Tukey, Duncan, Scheffe\u2019), (Equal & unequal replication), Analysis of covariance.<\/p>\n

                                                  \n
                                                • Randomized Blocks designs: model, partitioning of Total SS, ANOVA, (Equal & unequal replication), Estimation of parameters, Missing observations. Relative efficiency. Multiple contrasts, Analysis of covariance.<\/li>\n
                                                • Latin Squares: model, orthogonal Latin Squares, partitioning of TSS, ANOVA, Estimation of parameters, Missing observations, relative efficiency.<\/li>\n
                                                • Factor Experiments analysis of 2p<\/sup><\/strong> factorial experiments<\/li>\n
                                                • Split-plot designs, incomplete Latin squares designs, balanced incomplete block designs.<\/li>\n<\/ul>\n

                                                  \u00a0<\/strong><\/p>\n

                                                  ST603 : Regression Analysis<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/u> <\/strong>\u2022<\/h2>\n

                                                  Introduction.<\/p>\n

                                                    \n
                                                  • Simple Regression model E(Y\/X)<\/strong> & assumptions, estimation of, and inference (estimation & test) about the parameters, Angle between two lines of regression, Test of linearity of regression.<\/li>\n
                                                  • Multiple Regression. Matrix treatment. Estimation and inference about the parameters (estimation & test). Coefficient of determination. Polynomial regression with one regressors (predictor). Estimation of the parameters.<\/li>\n<\/ul>\n
                                                      \n
                                                    • Orthogonal polynomial, determination of degree using ANOVA.<\/li>\n
                                                    • Search for best set of regressors (predictor) using Step-wise Method (Forward, Backward & Best\u00a0 R2<\/sup><\/strong> ).<\/li>\n
                                                    • Duality Method<\/li>\n<\/ul>\n

                                                      \u00a0<\/strong><\/p>\n

                                                      ST604 : Numerical Analysis\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3 \u2022<\/h2>\n

                                                      Introduction<\/h2>\n
                                                        \n
                                                      • Source of error<\/li>\n
                                                      • Round errors and instability<\/li>\n
                                                      • Estimation of errors<\/li>\n
                                                      • Solution of Equations in one Variable<\/li>\n
                                                      • The bisection Algorithm<\/li>\n
                                                      • Fixed-point Iteration<\/li>\n
                                                      • The Newton-Raphson method<\/li>\n
                                                      • Zero of real Polynomials<\/li>\n
                                                      • Interpolation and polynomial Approximation<\/li>\n
                                                      • The Taylor Polynomials<\/li>\n
                                                      • Interpolation and lagrange Polynomial<\/li>\n
                                                      • Divided differences<\/li>\n
                                                      • Finite differences<\/li>\n
                                                      • Curve Fitting<\/li>\n
                                                      • Discrete least squares method<\/li>\n
                                                      • Cubic-spline method<\/li>\n
                                                      • Solving Linear systems<\/li>\n
                                                      • Gaussian Elimination and pivoting<\/li>\n
                                                      • Linear Algebra and Matrix Inversion<\/li>\n
                                                      • Direct Factorization of Matrices<\/li>\n
                                                      • Gauss iterative method<\/li>\n
                                                      • Numerical Solution of Non-Linear Systems<\/li>\n
                                                      • Fixed-point for function of several Variables – Newton\u2019s method<\/li>\n
                                                      • Numerical Differentiation and Integration<\/li>\n
                                                      • Numerical differential –\u00a0 Higher derivatives and extrapolation<\/li>\n
                                                      • Elements of numerical integration –\u00a0 Composite numerical integration<\/li>\n
                                                      • Adaptive Quadratic Methods –\u00a0 Romberg Integration<\/li>\n<\/ul>\n

                                                        ST702 : Mathematical Statistics II\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
                                                          \n
                                                        • Introduction: Distributions of order statistics, Complex Numbers<\/li>\n
                                                        • Characteristic function (properties and related theorems Inversion theorem), Multivariate characteristic function<\/li>\n
                                                        • Infinite divisible law.<\/li>\n
                                                        • Chebyshev\u2019s Inequality.<\/li>\n
                                                        • Concept of absolutely continuous random variables, sequence of random variables.<\/li>\n
                                                        • Modes of convergence:<\/li>\n
                                                        • Convergence in distribution.<\/li>\n
                                                        • Convergence in probability.<\/li>\n
                                                        • Mean square Convergence.<\/li>\n
                                                        • Limiting distribution and Stochastic Convergence.<\/li>\n
                                                        • Limiting moment generating function.<\/li>\n
                                                        • Laws of large numbers (Weak Law of large numbers WLLN).<\/li>\n
                                                        • Bernoulli\u2019s law of large numbers.<\/li>\n
                                                        • Applications of law of large numbers.<\/li>\n
                                                        • Central limit theorem and it’s various forms.:<\/li>\n
                                                        • De-Moivere\u2019s Laplace theorem.<\/li>\n
                                                        • Lindeberg-Levy theorem.<\/li>\n
                                                        • Liapounoff\u2019s central limit theory.<\/li>\n
                                                        • Applications of Central limit theorem.<\/li>\n<\/ul>\n

                                                          \u00a0<\/strong><\/p>\n

                                                          ST703 : Demography\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
                                                            \n
                                                          • Introduction to vital statistics,<\/li>\n
                                                          • Collection of vital Statistics, Census, Mortality and fertility rates.<\/li>\n
                                                          • Construction of life table.<\/li>\n
                                                          • Types of population : Stationary, Stable and Dynamic Populations. Reproduction rates, Growth of population<\/li>\n
                                                          • Population projections and estimations.<\/li>\n
                                                          • Migration and distribution of population.<\/li>\n<\/ul>\n

                                                            \u00a0<\/strong><\/p>\n

                                                            ST708 : Stochastic Processes<\/u><\/strong>\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0CREDITS\u00a0\u00a0\u00a0 3<\/u> <\/strong>\u2022<\/h2>\n

                                                            Stochastic processes (definition & examples)<\/p>\n

                                                              \n
                                                            • Markov Chain: one \u2013two & multiple stats Markov chains, Transition functions, and initial distribution.<\/li>\n
                                                            • Transition and reconnect Stats.<\/li>\n
                                                            • Absorption Probabilities.<\/li>\n
                                                            • Continuous time Markov chains.<\/li>\n
                                                            • Birth & death chain.<\/li>\n
                                                            • Random walk (simple and general)<\/li>\n
                                                            • Unrestricted random walk with one or two absorbing barriers<\/li>\n
                                                            • Branching processes<\/li>\n<\/ul>\n

                                                              \u00a0<\/strong><\/p>\n

                                                              ST803 : Sampling Distribution<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/u> <\/strong>\u2022<\/h2>\n

                                                              Introduction:<\/p>\n

                                                                \n
                                                              • Gamma & Beta Distribution:( pdf, moments, moment generating function, Characteristic function, mode, Inflection points, skew ness, Additive property for Gamma Dist.)<\/li>\n
                                                              • Chi-Square distribution:( pdf, moments, moment generating function, Characteristic function, mode, Inflection points, skew ness, Additive property, test for population variance, Chi-Square\u00a0 test of independence & homogeneity, Chi-Square for pooling the probabilities, Yate\u2019s correction)<\/li>\n
                                                              • Non-Central Chi-Square distribution: :(with non-central parameter, moment generating function, Additive property, Cumulate of nonCentral Chi-Square distribution)<\/li>\n
                                                              • Student\u2019s t- distribution: (Derivation of Student\u2019s t- distribution, pdf, moments, moment generating function, Characteristic function, mode, Inflection points, Critical values of t, Application of t-Dist.)<\/li>\n
                                                              • Non-Central Student\u2019s t- distribution (properties)<\/li>\n
                                                              • F- distribution (Derivation of F- distribution, pdf, moments, moment generating function, Characteristic function, mode, Inflection points, Critical values of t, Application of F-Dist.)<\/li>\n
                                                              • Non-Central F- distribution (properties)<\/li>\n<\/ul>\n

                                                                \u00a0<\/strong><\/p>\n

                                                                ST804 : Multivariate Analysis\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/h2>\n
                                                                  \n
                                                                • Introduction : Data Matrix (Mean vector, Covariance Matrix, Centering Matrix, Sample correlation Matrix, Linear Combinations, Transformation)<\/li>\n
                                                                • Basic properties of random vectors (cumulative dist., Joint density function, conditional dist., Expectation, Conditional moments, Characteristic function,)<\/li>\n
                                                                • Multi-normal Distribution (Quadratic form ,properties<\/li>\n
                                                                • Estimation of Mean vector and Covariance Matrix (Maximum likelihood estimators )<\/li>\n
                                                                • The distribution of the sample Mean Vector<\/li>\n
                                                                • Test and Confidence regions for mean vector<\/li>\n
                                                                • Non central Chi-Square distribution (Multivariate)<\/li>\n
                                                                • The General (Hotelling\u2019s) T2<\/sup><\/strong>– Statistics (properties) Wishart Distribution (properties)<\/li>\n
                                                                • Canonical Analysis.<\/li>\n<\/ul>\n

                                                                  ST806 : Operation Research<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/u> <\/strong>\u2022<\/h2>\n

                                                                  Introduction: meaning and importance of Operation research<\/p>\n

                                                                    \n
                                                                  • Linear Programming: General linear programming Problem , graphic solution, simplex method, Computational Procedure, Problem of degeneracy, duality in linear programming, Transportation problem, Assignment Problem, inventory problems.<\/li>\n
                                                                  • Games theory : Kind of games, Two person-zero sum game, Stable Vs Unstable Game, Dominate Strategy.<\/li>\n
                                                                  • Network Analysis: the critical path method CPM, PERT,<\/li>\n
                                                                  • Queuing System: characteristics, Poisson & Exponential queues, models with numerical Examples.<\/li>\n<\/ul>\n

                                                                    \u00a0<\/strong><\/p>\n

                                                                    ST809 : Applied Linear Models<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3<\/u> <\/strong>\u2022<\/h2>\n

                                                                    Introduction in Econometrics.<\/p>\n

                                                                      \n
                                                                    • Problems of liner regression( autocorrelation- multicolinerity- heterscedasticity- error in variable).<\/li>\n
                                                                    • Simultaneous equations.<\/li>\n
                                                                    • Regression on :((Estimation of parameters in each case)):<\/li>\n
                                                                    • Dummy variables (Dummy independent variables, Dummy dependent variable).<\/li>\n<\/ul>\n

                                                                      ST812 : Project I<\/u><\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 4<\/u> <\/strong><\/h2>\n

                                                                      In this course the student (more than one student) chooses a problem for purpose of research. The statistical methods that he developed during the stages of his specialized studies are used.<\/p>\n

                                                                      \u00a0<\/strong><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"Descriptive of Compulsory Statistics Courses \u00a0 ST101 : General Statistics I\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CREDITS\u00a0\u00a0\u00a0 3 General concepts, Concept of statistics and its relation ship to other science. Statistical data and methods of collecting data. Frequency distribution, Cumulative frequency dist. Geometrical representation of data.(graphical) Measures of central tendency (Mean, Mode, Median, geometric mean, harmonic mean percentiles.) Measures of [...]","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[17],"tags":[],"class_list":["post-3185","post","type-post","status-publish","format-standard","hentry","category-17"],"aioseo_notices":[],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/posts\/3185","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/comments?post=3185"}],"version-history":[{"count":6,"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/posts\/3185\/revisions"}],"predecessor-version":[{"id":3245,"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/posts\/3185\/revisions\/3245"}],"wp:attachment":[{"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/media?parent=3185"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/categories?post=3185"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sebhau.edu.ly\/sc\/en\/wp-json\/wp\/v2\/tags?post=3185"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}