PGDM Core Subject
Statistical Modeling with R
Course Objective
Primary Mapping: PO2 (Critical Thinking) & PO6 (Global Perspective). Textbook: The Art of R Programming by Norman Matloff.
Evaluation Scheme
- 6 Assignments: 30 Marks (5 Marks each).
- Class Participation: 20 Marks.
- Mid Term: 20 Marks.
- End Term: 30 Marks.
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Session |
Session Name |
Pre-Reading (Approx. Pages) |
Assignment (5M) |
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1 |
R Ecosystem & Environment |
Ch 1: Introduction (pp. 1-20) |
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2 |
Vectors & Matrices in R |
Ch 2-3: Data Structures (pp. 25-55) |
A1: Vector Logic |
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3 |
Lists & Data Frames |
Ch 4-5: Handling Data (pp. 60-90) |
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4 |
Programming Structures (Loops) |
Ch 7: Control Flow (pp. 140-165) |
A2: Custom Function |
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5 |
Descriptive Stats in R |
Ch 8: Math/Stats (pp. 190-210) |
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6 |
Probability Distributions |
Ch 8: Simulation (pp. 211-230) |
A3: Normal Dist. |
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7 |
Hypothesis Testing (T-tests) |
Statistics Note: P-values |
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8 |
ANOVA in R |
Statistics Note: Variance Analysis |
A4: ANOVA Report |
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9 |
Non-Parametric Tests |
Statistics Note: Chi-Square |
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10 |
Mid Term Exam (Theory/Lab) |
Review Sessions 1-9 |
Mid Term (20M) |
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11 |
Linear Regression in R |
Ch 10: Modeling (pp. 240-260) |
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12 |
Multiple Regression Analysis |
Ch 10: Advanced Modeling (pp. 261-280) |
A5: Multi-Reg Case |
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13 |
Logistic Regression & Binary |
Technical Note: GLM Models |
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14 |
Time Series Forecasting |
Ch 14: Time Series (pp. 320-340) |
A6: Market Forecast |
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15 |
Cluster Analysis |
Ch 11: Unsupervised (pp. 285-305) |
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16 |
Visualization with ggplot2 |
ggplot Manual: Layered Grammar |
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17 |
Dealing with Missing Data |
Technical Note: Imputation |
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18 |
R for Global Economic Data |
Technical Note: World Bank API |
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19 |
Optimization & Simulation |
Ch 13: Simulation (pp. 310-330) |
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20 |
End Term Final Submission |
Project Defense |
End Term (30M) |