FULLY SOLVED BOOK LASY 5 YEARS PAPERS SOLVED PLUS GUESS
STATISTICS FOR MANAGEMENT Unit 11) Introduction to Statistics-Overview, origin and development and Managerial Applications of statistics, Measures of Central Tendency, Dispersion, Skewnes and Kurtosis.2) Introduction to Probability-Concepts and Definitions of Probability–Classical, Relative frequency, subjective and axiomatic. Addition and multiplication theorems, Statistical independence, Marginal, Conditional and joint Probabilities.3) Baye’s theorem and its applications. Unit 21) Probability Distribution-Random Variable (RV), Expectation and Variance of a RV. Probability distribution function, properties, Continuous and Discrete Probability distribution functions.2) Discrete Probability distributions: Binomial Distribution, Properties and applications; Poisson distribution, Properties and applications.3) Continuous Probability Distributions-Normal Distribution, Standard Normal Distribution, Properties, applications and importance of Normal Distribution. Unit 31) Sampling Theory-The basics of sampling-Sampling procedures-Random and Non-Random methods-Sample size determination-Sampling distribution, Standard Error, Central Limit Theorem.2) Hypothesis Testing-Statistical Estimation, Point and Interval Estimation, Properties of a good estimator, confidential interval.3) Large Sample tests-Test for one and two proportions, Test for one and two means, Test for two S.D.’s. Unit 41) Small Sample Tests- t-Distribution-properties and applications, testing for one and two means, paired t-test.2) Analysis ofVariance-One WayandTwo WayANOVA (with and without Interaction).3) Chi-Square distribution: Test for a specified Population variance, Test for Goodness of fit, Test for Independence of Attributes. Unit 51) Correlation Analysis-Scatter diagram, Positive and Negative correlation, limits for coefficient of correlation, Karl Pearson’s coefficient of correlation, Spearman’s Rank correlation, concept of multiple and partial Correlation.2) Regression Analysis-Concept, least square fit of a linear regression, two lines of regression, properties of regression coefficients.3) Time Series Analysis-Components, Models of Time Series–Additive, Multiplicative and Mixed models; Trend analysis-Free hand curve, Semi averages, moving averages, Least Square methods.
