Syllabus

Unit 1. Introduction to Statistics and Data Collection: Business Statistics, Collection of Data, Populations and Samples, Nature of Statistical Data and Measurement Levels, Scales of Measurement, Types of Statistical Data. (Lecture Hours - 3)

Unit 2. Univariate Analysis: (i) Measures of Central Tendency: Introduction, Central Tendency, Requisites of a Good Average, Measures of Central Tendency (ii) Measures of Variation: Variability and Decision Making, Absolute and Relative Variation, Measures of Variation (iii) Measures of Symmetry and tailedness: Skewness, Difference Between Dispersion and Skewness, Kurtosis (Lecture Hours - 6)

Unit 3. Bivariate Analysis: Correlation, Simple Regression (Lecture Hours - 3)

Unit 4. Multivariate Analysis: Exploratory Factor Analysis, Cluster Analysis, Multiple Regression, Discriminant Analysis, Logistic Regression, Perceptual Mapping, Correspondence Analysis, Conjoint Analysis, Multivariate Analysis of Variance and Covariance (Lecture Hours - 9)

Unit 5. Statistical Quality Control: Introduction, Control Charts, Control Charts for Variables, Control Charts for Attributes, Acceptance Sampling (Lecture Hours - 6)

Unit 6. Probability: Introduction, Marginal Probability, Joint Probability, Conditional Probability, Bayes’ Theorem (Lecture Hours - 3)

To develop an in-depth understanding about Statistics and all its key concepts like inferential statistics, sampling, probability, hypothesis testing, and different types of statistical analysis.

To make the students familiar with the usage of statistical tools like MS Excel, IBM SPSS, and IBM AMOS.

To enable the usage of statistical techniques in personal and professional life

After completion of this course, the pupils shall be able to –
(i) Describe all the key concepts of Statistical analysis with ease
(ii) Organize, manage and present data independently
(iii) Analyse statistical data using measures of central tendency, dispersion and location
(iv) Calculate coefficient of correlation independently
(v) Build the simple linear regression Model without the help of Excel and SPSS
(vi) Calculate Coefficient of Determination (R2) without the help of Excel and SPSS
(vii) Build the multiple and logistics regression Model on MSExcel and SPSS
(viii) Calculate the utilities through Conjoint Analysis
(ix) Perform discriminant and correspondence analysis independently.
(x) Perform MANOVA independently
(xi) Prepare control charts with ease
(xii) Explain the concept of probability with ease
(xiii) Apply Joint Probability, Conditional Probability and Bayes’ theorem to calculate the likelihood of event(s).

Ken Black (2013), Business Statistics for Contemporary Decision Making, Wiley , Edition - 7th. Reprint: 2016

Richard I. Levin, and David S. Rubin (2017), Statistics for Management, Pearson , Edition - 8th

Joseph F. Hair Jr., William C. Black, Barry J. Babin, Rolph E. Anderson (2019), Multivariate Data Analysis, Cengage , Edition - 8th

D. N. Gujarati, D. C. Porter, & S. Gunasekar (2012), Basic Econometrics, Tata McGraw Hill Education Pvt. Ltd. , Edition - 5th