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Statistics & Data Management syllabus

STT 5515 units · 16 topicsAcademic year 2083/84
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Statistics & Data Management

5 units

1. Theory of Probability and Probability Distributions (6 LHs)

  1. Theory of Probability
    1. Theory of Probability: Concepts, Approaches to calculation of probability, Rules of addition & multiplication, marginal, joint conditional probability, and Bayes’ theorem
  2. Expected value and standard deviation of probability distribution. Probability Distribution
    1. Expected value and standard deviation of probability distribution. Probability Distribution: Discrete distribution (Binomial distribution and Poisson distribution), Continuous Distributions (Normal Distribution and its application).

2. Sampling and Estimation 6 LHs.

  1. Sampling Methods
    1. Sampling Methods: Objective of sampling
  2. Random sampling
    1. Random sampling (Simple random, Systematic, Stratified, and Cluster, Multi-stage sampling, Non-random sampling (Quota, Convenience, Judgment, Snow-ball), Central Limit Theorem and Standard Error. Estimation: Point and Interval estimation, Confidence Intervals, Characteristics of a good estimator, Interval estimate for mean and proportion, Determination of sample size.

3. Hypothesis Testing (10 LHs)

  1. Fundamentals
    1. Fundamentals: Null and Alternative hypothesis
  2. Type I and Type II Errors
    1. Type I and Type II Errors
  3. Level of Significance. Parametric Tests
    1. Level of Significance. Parametric Tests: Z-test (Single mean and double means, Single proportion and double proportion)
  4. T-test (One-sample, Independent, and Paired)
    1. t-test (One-sample, Independent, and Paired)
  5. And Analysis of Variance (One way ANOVA and two way ANOVA). Non-Parametric Tests
    1. and Analysis of Variance (One way ANOVA and two way ANOVA). Non-Parametric Tests: Chi-square test (Test of independence and goodness of fit)
  6. Kruskal -Wallis test
    1. Kruskal -Wallis test
    2. Mann -Whitney Test.

4. Correlation and Regression Analysis (6 LHs)

  1. Correlation
    1. Correlation: Multiple and partial correlation, Coefficient of multiple and partial determination. Regression: Multiple regression model, Residual analysis
  2. Inferences regarding population regression coefficients
    1. Inferences regarding population regression coefficients, Introduction to multi-variable influence analysis.

5. Foundational Data Management (4 LHs)

  1. Data Life Cycle
    1. Data Life Cycle: Concept and stages of the data life cycle including collection, storage, analysis, archiving, and disposal. Data Quality: Importance of data integrity and accuracy
  2. Techniques for managing data inconsistencies
    1. techniques for managing data inconsistencies, missing values, and outliers. Data Privacy and Ethics: Overview of data protection regulations such as GDPR and CCPA
  3. Ethical principles in data usage and transparency. Data Security
    1. ethical principles in data usage and transparency. Data Security: Fundamentals of data protection
  4. Access control
    1. access control, encryption, and strategies for safeguarding organizational data from unauthorized access.