Business Analytics for Decision Making

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Business Analytics for Decision Making  

Course Objective: The course aims at equipping students with an understanding of the
research process, tools and techniques in order to facilitate managerial decision
making.
Unit I
Introduction to Statistics: Meaning, Importance, Applications of inferential statistics
in managerial decision making. Collection of Data: concept of primary data and
secondary data, sources of primary data and secondary data, Classification and
Tabulation of Data: Concept and types of classification, construction of frequency
distributions, tabulation of data: role of tabulation, parts of table, rules of tabulation,
review of table, types of table.
Sampling: Concept, definitions, census and sampling, probability and non probability
methods of sampling, relationship between sample size and errors.
Unit II
Sampling Distributions: Concept and standard error.
Hypothesis Testing: Formulation of hypothesis, procedure of hypothesis testing, errors
in testing of hypothesis, tests of significance for large samples, tests of significance for
small samples, application of t-test, Z-test, F-test and Chi-square test and Goodness of
fit, ANOVA.
Techniques of association of attributes.
Unit III
Business Forecasting: Introduction, Role of forecasting in business, Steps in
forecasting and methods of forecasting.
Correlation: Partial and Multiple correlation.
Regression Analysis: Multiple regression analysis, Testing the assumptions of
regression: multicolinearity, heteroscedasticity and autocorrelation.
Unit IV
Index Number: Definition, importance of index number in managerial decision
making, methods of construction, tests of consistency, base shifting, splicing and
deflation, problems in construction.
Time Series Analysis: Meaning, component and, methods of time series analysis.
Trend analysis: Least square method, linear and non linear equations, applications of
time series in business decision making.