Operations Research And Statistics

1. Describe the nature of statistics and its applications.
2. Apply different sampling techniques to collect data.
3. Explain and illustrate various graphs and charts used in data presentation.
4. Apply methods for measuring central tendencies and dispersion.
5. Explain the nature of probability and probability distribution.
6. Apply hypothesis testing procedures.
7. Describe correlation, regression analysis, and operations research techniques.

13 weeks, about 98 hours in total. Yours will differ.

1. Week 1Module 1: Introduction

   • Unit 1: Nature of Statistics · 8 hours

     Read Unit 1: Nature of Statistics, focusing on definitions of statistics, data types, and the data collection process.. Complete Self-Assessment Exercise to reinforce understanding of statistical terms.. Attempt Tutor-Marked Assignment (TMA) questions related to data classification and questionnaire design..

2. Week 2Module 1: Introduction

   • Unit 2: Sampling Techniques · 7 hours

     Study Unit 2: Sampling Techniques, paying attention to probability and non-probability sampling methods.. Work through examples of stratified and systematic sampling.. Reflect on the advantages and disadvantages of different sampling approaches..

3. Week 3Module 1: Introduction

   • Unit 3: Organisation and Presentation of Data · 8 hours

     Review Unit 3: Organisation and Presentation of Data, focusing on data classification, tabulation, and frequency distributions.. Practice constructing histograms, frequency polygons, and pie charts.. Complete Tutor-Marked Assignment (TMA) questions on data presentation methods..

4. Week 4Module 2: Measures of Central Tendency and Variability

   • Unit 1: Measures of Central Tendency · 7 hours

     Study Unit 1: Measures of Central Tendency, focusing on arithmetic mean, median, mode, and geometric mean.. Work through examples of calculating central tendencies for ungrouped and grouped data.. Compare the relationship between mean, median, and mode in different distributions..

5. Week 5Module 2: Measures of Central Tendency and Variability

   • Unit 2: Measures of Distribution · 8 hours

     Study Unit 2: Measures of Distribution, focusing on range, quartiles, mean deviation, standard deviation, and variance.. Practice calculating measures of variability for grouped and ungrouped data.. Understand skewness and kurtosis and their implications for data interpretation..

6. Week 6Module 2: Measures of Central Tendency and Variability

   • Unit 3: Probability · 7 hours

     Study Unit 3: Probability, focusing on basic probability laws, conditional probability, and Bayes' Theorem.. Solve problems involving probability calculations using different approaches (classical, relative, Bayesian).. Understand permutation and combination concepts..

7. Week 7Module 2: Measures of Central Tendency and Variability

   • Unit 4: Probability Distribution · 8 hours

     Study Unit 4: Probability Distribution, focusing on discrete and continuous random variables.. Construct probability distributions from raw data.. Compute means and variances of probability distributions..

8. Week 8Module 3: Statistical Inference

   • Unit 1: Test of Hypothesis · 7 hours

     Study Unit 1: Test of Hypothesis, focusing on null and alternative hypotheses, Type I and Type II errors, and significance levels.. Learn the procedure for hypothesis testing.. Apply hypothesis testing to one and two population means..

9. Week 9Module 3: Statistical Inference

   • Unit 2: Correlation and Regression Analysis · 8 hours

     Study Unit 2: Correlation and Regression Analysis, focusing on simple and multiple regression models.. Learn how to fit a simple linear regression model using the least squares method.. Understand correlation analysis and the coefficient of determination..

10. Week 10Module 3: Statistical Inference

    • Unit 3: Analysis of Variance (ANOVA) · 7 hours

      Study Unit 3: Analysis of Variance (ANOVA), focusing on one-way ANOVA and randomized complete block design.. Learn how to test for significant differences among means.. Apply ANOVA to experimental data..

11. Week 11Module 3: Statistical Inference

    • Unit 4: Analysis of Covariance (ANCOVA) · 8 hours

      Study Unit 4: Analysis of Covariance (ANCOVA), focusing on including covariates in ANOVA models.. Learn how to adjust means for the effect of covariates.. Apply ANCOVA to experimental data..

12. Week 12Module 4: Operations Research

    • Unit 1: Introduction to Operations Research · 7 hours

      Study Unit 1: Introduction to Operations Research, focusing on the definition, history, and characteristics of operations research.. Learn the phases of operations research.. Understand the interdisciplinary team approach in operations research..

13. Week 13Module 4: Operations Research

    • Units 2-6: Operations Research Techniques · 8 hours

      Study Units 2-6: Linear Programming, Transportation Problem, Games Theory, Network Analysis, and Simulation.. Focus on problem formulation and solution techniques for each topic.. Work through examples and case studies to apply the concepts..