Business Statistics and Analytics for Decision Making

Syllabus

Code- 1T6

BUSINESS STATISTICS & ANALYTICS FOR DECISION MAKING

Module 1

Measures of Dispersion (Variation) & Symmetry: Significance of Measuring Dispersion, Requisites and Classification of Measures of Dispersion, Distance Measures - Range, Interquartile Range. Average Deviation Measures - Mean Absolute Deviation, Variance and Standard Deviation, Chebyshev’s Theorem, Coefficient of Variation & Its Significance. Concept of Skewness & Kurtosis.

Module 2

Measures of Association: Correlation, Types & Methods of Correlation analysis - Karl Pearson’s Coefficient of Correlation, Spearman’s Rank Correlation, Probable Error, Coefficient of Determination, Standard Error of Coefficient of Correlation. Introduction to Regression Analysis and its Advantages, Types of Regression Models, Methods to Determine Regression Coefficients (Normal Equations).

Module 3

Probability: Basic terminology, types of probability, probability rules, conditional probabilities, Baye’s Theorem. Random Variables, Probability distributions; Binomial distribution, Poisson distribution, Normal distribution. Choosing correct probability distribution.

Module 4

Hypothesis Testing: Introduction, Hypothesis Testing Procedure, Errors in Hypothesis Testing. Power of a Statistical Test. t-test, ANOVA and Chi-Square Test, (Students should be Able to Perform Testing on Spreadsheets)

Module 5

Business Analytics: Introduction to Analytics, Differentiating Descriptive, Predictive, and Prescriptive Analytics, Data Mining vs. Data Analytics, Industrial Problem Solving Process, Decision Needs and Analytics, Stakeholders and Analytics, SWOT Analysis, Business Analytics in Decision Making, Categorization of Analytical Methods and Models. Introduction & Applications of SPSS, R, Python, etc.