IMMG 11033

>< Title : Business Statistics
>< Type/Status : Core Course
>< Teaching and learning objectives:
The aim of this course is to provide students an introductory survey of many business applications of descriptive and inferential statistics. This course prepares the students to utilize probabilistic models in the analysis of managerial decision problems and uses case study approaches. Theories learnt would be applied and analyzed in actual situations related to problems in industry. It is also designed to acquaint the student with various ways of summarizing distributions of populations and sample data and to show the relationship between sample statistics and population parameters.
On successful completion of this course, students will be able to carry out statistical analysis and make managerial decisions for given sets of data.
>< Course Content:

Descriptive statistics: Compilation, Classification, Tabulation and Diagrammatic and Graphical Representation of various types of Statistical Data, Skewness and Kurtosis, Frequency Distributions, Measures of Location and Dispersion.

Elements of Probability Theory: Set Theory, Concepts of Probability, Sample Space, Field of Events and Generalised Addition Theorem, Conditional Probability, Independence, Bayes’ Theorem, Random Variables, Distribution Theory, Expectation, Variance, Normal, Exponential, Binomial and Poisson Distributions.

Sampling Distribution: Population Parameters and Statistics, Type of Samples, Probability Distribution of Sample Means, Distribution of Linear Combination of Random Variables and the Central Limit Theorem, General Statistical Hypothesis testing and General Statistical Estimation for Population Parameters.

Regression Theory: Simple Linear Regression Model, Least Square Method of estimating the parameters, Hypothesis Testing, Testing of Lack of Fit, Residual Analysis, Multiple Linear Regression Models, Estimation of Model Parameters.

Nonparametric methods: Chi-Square applications, Goodness- of -Fit test; equal expected and unequal expected analysis of ranked data.

Index numbers, Time Series and Forecasting: Secular trend, linear trend and nonlinear trend.

>< Methodology:
The course will be delivered in a combination of lectures, case discussions, tutorials, and applications of statistical software packages of EXCEL and SPSS.
>< Scheme of Evaluation:
End-of-semester examination, practical test and continuous assessment.
>< Recommended Reading:
1. Robert D. Mason, Douglas A. Lind and William G. Marchal “Statistical Techniques in Business and Economics”, 1999, McGraw-Hill.
2. James R. Evans, David Louis Olson, “Statistics, Data Analysis, and Decision Modelling”, 2002, Prentice Hall.
3. David M Lavine, Thimothy C Krehbiel, Mark L Berson, “Business Statistics: A First Course”, 3rd Edition, 2002, Prentice Hall.

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