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Statistics
Sampling Theory
1. Foundations of Sampling Theory
2. Probability Sampling Methods
3. Non-Probability Sampling Methods
4. Estimation from Samples
5. Advanced Estimation Techniques
6. Sample Size Determination
7. Handling Non-Sampling Errors
8. Advanced Sampling Topics
9. Survey Design and Implementation
Estimation from Samples
Point Estimation
Estimator Properties
Unbiasedness
Efficiency
Consistency
Sufficiency
Minimum Variance
Population Mean Estimators
Sample Mean
Weighted Mean
Ratio Estimators
Population Total Estimators
Expansion Estimators
Weighted Total Estimators
Population Proportion Estimators
Sample Proportion
Weighted Proportion
Population Variance Estimators
Sample Variance
Unbiased Variance Estimators
Interval Estimation
Confidence Interval Concepts
Confidence Level
Margin of Error
Coverage Probability
Confidence Intervals for Means
Known Population Variance
Unknown Population Variance
t-Distribution Applications
Confidence Intervals for Proportions
Normal Approximation
Exact Methods
Continuity Corrections
Confidence Intervals for Totals
Expansion from Mean Intervals
Direct Total Estimation
Central Limit Theorem Role
Asymptotic Normality
Sample Size Requirements
Confidence Level Interpretation
Frequentist Interpretation
Common Misconceptions
Variance and Standard Error
Variance of Sample Mean
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Variance of Sample Proportion
Binomial Variance
Design-Based Variance
Standard Error Concepts
Standard Error of Mean
Standard Error of Proportion
Standard Error of Total
Finite Population Correction
FPC Factor Calculation
When to Apply FPC
Impact on Standard Errors
Variance Estimation in Complex Designs
Linearization Methods
Replication Methods
Bootstrap Methods
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3. Non-Probability Sampling Methods
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5. Advanced Estimation Techniques