## Statistics For Management Syllabus

 Course Code: MB0040 Course Title: Statistics for Management  (4 Credits)

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Course Contents

Unit 1- Introduction to Statistics:  Introduction to Statistics, Importance of Statistics in modern business environment. Definition of Statistics, Scope and Applications of Statistics Characteristics of Statistics, Functions of Statistics, Limitations of Statistics, Statistical Softwares.

Unit 2- Statistical Survey: Introduction, Definition of Statistical Survey, Stages of Statistical Survey - Planning of a Statistical Survey- Execution of Statistical Survey, Basic Terms used in Statistical Survey - Units or Individuals - Population or Universe –Sample -Quantitative  -Characteristic - Qualitative Characteristic – Variable, Collection of Data- Primary Data - Secondary Data - Pilot survey , Scrutiny and Editing of Data

Unit 3- Classification, Tabulation and Presentation of Data: Introduction , Functions of Classification - Requisites of a good classification - Types of classification - Methods of classification ,Tabulation - Basic difference between classification and tabulation -Parts of a table -Types of table , Frequency and Frequency Distribution - Derived frequency distributions - Bivariate and multivariate frequency distribution - Construction of frequency distribution , Presentation of Data – Diagrams, Graphical Presentation -  Histogram -   Frequency polygon -   Frequency curve -  Ogives

Unit 4- Measures of Central Tendency and Dispersion: Introduction, Objectives of statistical average, Requisites of a Good Average, Statistical Averages - Arithmetic mean -  Properties of arithmetic mean - Merits and demerits of arithmetic mean ,Median - Merits and demerits of median , Mode - Merits and demerits of mode , Geometric Mean , Harmonic Mean , Appropriate Situations for the Use of Various Averages , Positional Averages , Dispersion – Range - Quartile deviations, Mean deviation ,Standard Deviation -Properties of standard deviation Coefficient of Variance

Unit 5- Theory of Probability: Introduction - Definition of probability - Basic terminology used in probability theory,  Approaches to probability , Rules of Probability - Addition rule - Multiplication rule , Conditional Probability, Steps Involved in Solving Problems on Probability , Bayes’ Probability , Random Variables

Unit 6- Theoretical Probability Distributions: Introduction - Random variables , Probability Distributions - Discrete probability distributions - Continuous probability distributions , Bernoulli Distribution -    Repetition of a Bernoulli experiment  , Binomial Distribution - Assumptions for applying a binomial distribution - Examples of binomial variate - Recurrence formula in case of binomial distribution - Case study on binomial distribution Poisson Distribution - Assumptions for applying the Poisson distribution -Real life examples of Poisson variate - Recurrence relation -Case study on Poisson distribution , Normal Distribution - Standard Normal Distribution

Unit 7- Sampling and Sampling Distributions: Introduction , Population and Sample - Universe or Population - Types of Population – Sample , Advantages of Sampling, Sampling Theory - Law of Statistical Regularity - Principle of Inertia of Large Numbers - Principle of Persistence of Small Numbers - Principle of Validity - Principle of Optimization , Terms Used in Sampling Theory , Errors in Statistics , Measures of Statistical Errors , Types of Sampling - Probability Sampling - Non-Probability Sampling, Case let on Types of Sampling, Determination of Sample Size, Central Limit Theorem

Unit 8- Estimation: Introduction ,  Reasons for Making Estimates , Making Statistical Inference, Types of Estimates - Point estimate - Interval estimate , Criteria of a Good Estimator – Unbiasedness – Efficiency – Consistency – Sufficiency, Point Estimates ,Interval Estimates, Case study on calculating estimates - Making the interval estimate Interval Estimates and Confidence Intervals - Interval estimates of the mean of large samples - Interval estimates of the proportion of large samples - Interval estimates using the Student’s ‘t’ distribution , Determining the Sample Size in Estimation

Unit 9- Testing of Hypothesis in Case of Large and Small Samples: Introduction – Large Samples – Assumptions , Testing Hypothesis - Null and alternate hypothesis - Interpreting the level of significance - Hypotheses are accepted and not proved , Selecting a Significance Level - Preference of type I error - Preference of type II error - Determine appropriate distribution, Two – Tailed Tests and One – Tailed Tests -  Two – tailed tests - Case study on two –tailed and one-tailed tests, Classification of Test Statistics - Statistics used for testing of hypothesis - Test procedure  - How to identify the right statistics for the test , Testing of Hypothesis in Case of Small Samples - Introduction – small samples, ‘t’ Distribution , Uses of ‘t’ test

Unit 10- Chi – Square Test: Introduction ,  Chi-Square as a Test of Independence - Characteristics of 2 test - Degrees of freedom - Restrictions in applying 2 test - Practical applications of 2 test - Levels of significance - Steps in solving problems related to Chi-Square test - Interpretation of Chi-Square values , Chi-Square Distribution - Properties of 2 distribution - Conditions for applying the Chi-Square test - Uses of 2 test , Applications of Chi-Square test - Tests for independence of attributes - Test of goodness of fit - Test for specified variance

Unit 11- F – Distribution and Analysis of Variance (ANOVA): Introduction, Analysis of Variance (ANOVA), Assumptions for F-test - Objectives of ANOVA - ANOVA table - Assumptions for study of ANOVA, Classification of ANOVA - ANOVA table in one-way ANOVA - Two way classifications

Unit 12- Simple Correlation and Regression: Introduction , Correlation - Causation and Correlation - Types of Correlation -  Measures of Correlation - Scatter diagram - Karl Pearson’s correlation coefficient - Properties of Karl Pearson’s correlation coefficient - Factors influencing the size of correlation coefficient , Probable Error - Conditions under which probable error can be used , Spearman’s Rank Correlation Coefficient , Partial Correlations , Multiple Correlations ,   Regression - Regression analysis - Regression lines - Regression coefficient , Standard Error of Estimate ,  Multiple Regression Analysis , Reliability of Estimates , Application of Multiple Regressions