statsdept

Paper: (BC 406): BUSINESS STATISTICS-II Paper: BC 406 Max. Marks: 100 HPW: 4 with  Credits: 4 OBJECTIVE: To inculcate analytical and computational ability among the students. UNIT-I: REGRESSION: Introduction - Linear and Non Linear Regression – Correlation Vs. Regression - Lines of Regression – Derivation of Line of Regression of Y on X - Line of Regression of X on Y - Using Regression Lines for  Prediction. UNIT-II: INDEX NUMBERS: Introduction - Uses - Types - Problems in the Construction of Index Numbers - Methods of Constructing  Index Numbers - Simple and Weighted Index Number (Laspeyre - Paasche, Marshall – Edge worth) – Tests of Consistency of Index Number: Unit Test - Time Reversal Test - Factor Reversal Test –  Circular Test - Base Shifting - Splicing and Deflating of Index Numbers. UNIT-III: TIME SERIES: Introduction - Components – Methods-Semi Averages - Moving Averages – Least Square Method – Deseasonalisation of Data – Uses and Limitations of Ti

OSMANIA UNIVERSITY B.Sc. III Year, VI Semester (CBCS): Statistics Syllabus Paper-VIII A: Operations Research (DSE-2F) (3 HPW with 3 Credits and 60 Marks) Unit –I Operations Research: Meaning and scope of OR. Convex sets and their properties. Definition of general LPP. Formulation of LPP. Solution of LPP by graphical method. Statements of Fundamental theorem of LPP and other related theorems. Simplex algorithm. Concept of artificial variables. Big – M /Penalty method and two-phase simplex methods. Concept of degeneracy and resolving it. Unit –II Concept of duality of LPP. Dual Primal relationship, Statement of Fundamental Theorem of Duality. Definition of Transportation problem, TPP as a special case of LPP, Initial basic feasible solutions by North-West Corner Rule, Matrix minimum method and VAM. Optimal solution through MODI tableau and Stepping Stone method for balanced and unbalanced transportation problem. Unit –III Degeneracy in TP and resolving

B.Sc. III Year, VI Semester (CBCS): Statistics Syllabus Paper-VII: Design of Experiments, Vital Statistics, Official Statistics and Business Forecasting (DSC-2F) (3 HPW with 3 Credits and 60 Marks) Unit –I Analysis of Variance and Design of Experiments: Concept of Gauss-Markoff linear model with examples, statement of Cochran’s theorem, ANOVA – one-way, two-way classifications with one observation per cell.Expectation of various sums of squares, Statistical l analysis, Importance and applications of design of experiments. Principles of experimentation: Analysis of Completely randomized Design (C.R.D), Randomized Block Design (R.B.D) Unit –II Latin Square Design (L.S.D) including one missing observation, expectation of various sum of squares. Comparison of the efficiencies of above designs. Official Statistics : Functions and organization of CSO and NSSO. Agricultural Statistics, area and yield statistics. National Income and its computation, utility and difficult

B.Sc. II Year, IV Semester (CBCS): Statistics Syllabus Paper-IV: Inference (DSC-2D) (4 HPW with 4 Credits and 100 Marks) Unit –I Concepts of statistical hypothesis, null and alternative hypothesis, critical region, two types of errors, level of significance and power of a test. One and two tailed tests, test function (nonrandomized and randomized). Statement and Proof of Neyman-Pearson’s fundamental lemma for Randomized tests. Examples in case of Binomial, Poisson, Exponential and Normal distributions and their powers. Unit II Large sample tests for single sample mean, difference of means, single sample proportion, difference of proportions and difference of standard deviations. Fisher’s Z-transformation for  population correlation coefficient(s ) and testing the same in case of one sample and two samples . Definition of order statistics and statement of their distributions. Unit – III Tests of significance based on χ 2 : χ 2 - test for specified variance, go

B.Sc- I Year, II Semester (CBCS): Statistics Syllabus Paper-II: Probability Distributions (DSC-2B) (4 HPW with 4 Credits and 100 Marks) UNIT-I Discrete distributions – I : Uniform and Bernoulli distributions : definitions, mean, variance and simple examples. Definition and derivation of probability function of Binomial distribution, Poisson distribution definition, properties of these distributions such as median, mode, m.g.f, c.g.f., p.g.f., c.f., and moments up to fourth order, reproductive property, wherever exists, and their real life applications. Poisson approximation to Binomial distribution. UNIT-II Discrete distributions – II : Negative binomial, Geometric distributions : Definitions and physical condition, properties of these distributions such as m.g.f, c.g.f., p.g.f., c.f. and moments up to fourth order, reproductive property, wherever exists, lack of memory property for Geometric distribution and their real life applications. Poisson approximation to N