## Course Detail

 Course Name Statistical Analysis Course Code 23GM105C Program MBA Credits 3 Course Category Foundation Core Area General Management

### Syllabus

##### Module 1

Module 1: Able to use probability theory in decision-making in business (6 hours)

• Basic Probability concepts
• Conditional Probability
• Types of distribution, Rando Variables, Use of Expected Value in Decision Making
• Binomial Distribution & Poisson distributions
• Normal distributions – Normal distribution table, Normality test & Uniform
##### Module 2

Module 2: Sampling Distribution and Interval Estimation (6 hours)

• Introduction to Sampling, Random Sampling, Non-Random Sampling, errors
• Introduction to Sampling Distributions, Conceptual Basis for Sampling Distributions, Sampling from Normal Non-Normal Populations & The Central Limit Theorem Little’s law & applications
• Introduction, Interval Estimates-Basic Concepts, Interval Estimate and Confident Intervals & Interval Estimates of the Mean from Large Samples
• Calculating Interval Estimates of the Proportion from Large Samples
• Interval estimates using t distributions & Determining the sample size in Estimation
##### Module 3

Module 3: Hypotheses Testing: Two-Sample Tests and Analysis of Variance (6 hours)

• Hypothesis testing of Differences between Means and Proportions, Testing for difference between Means: Small & Large Sample sizes
• Testing Differences between Means with Dependent Samples
• Testing for Differences between Proportions: Large Sample Size
• The Completely Randomized Design: One-Way ANOVA
• The Factorial Design: Two-Way ANOVA, Randomized Block Design, Effects
##### Module 4

Module 4: Regression Analysis and Inference (6 hours)

• Simple Linear Regression – Types of Regression, Determining the Simple Linear Regression Equation, Measures of Variation, Inferences About the slope and Correlation Coefficient, Potential Pitfalls in Regression.
• Introduction to Multiple Regression – Developing a Multiple Regression Adjusted R2, and the Overall F Test, Residual Analysis for the Multiple Regression Model, Inferences Concerning the Population Regression Coefficients.
• Testing Portions of the Multiple Regression Model, Using Dummy Variables and interaction Terms in Regression Models
• Logistic Regression
##### Module 5

Module 5: Nonparametric Methods (6 hours)

• Chi-Square Test as a Test of Independence & Goodness of Fit.
• Testing the Appropriateness of a Distributions
• Introduction to Nonparametric Statistics.
• The Sign Test for Paired Data Rank Sum Tests: The Mann–Whitney U & The One-Sample Runs Test.

### Course Description & Outcomes

#### Course Description

The focus of this course will be on Descriptive and Predictive data analytics. Through this course, we attempt to develop fundamental knowledge and skills for applying appropriate statistical tools and techniques for business decision-making. The theory sessions to discuss the concepts will be supplemented by tutorials where the students will be guided to apply the theory learnt to various problems in different settings and contexts for enhanced understanding.

#### Course Outcomes & Learning Levels

The course aims to impart to a student several data-driven decision-making techniques that would be useful for him/her as a manager in his/her career. The entire course is divided into five modules with distinct learning outcomes for each. On completion of the course, the students should be able to:

1. Able to estimate the likelihood of important events in business (Evaluate)
2. Able to make inferences about populations based on suitable samples (Evaluate)
3. Able to evaluate the hypothesis through suitable statistical tests (Evaluate)
4. Able to develop models to identify causal relationships between variables (Create)
5. Able to make inferences about distribution-free population (Evaluate)

Evaluation Pattern

 # Assessment Component Percentage of Marks 1 Continuous Assessment * 40 2 Mid –Term Examination 20 3 End –Term Examination 40

*Based on assignments / Tests / Quizzes / Case Studies / Project / Term paper / Field visit report.

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