1. Population mean
2. Sum of the population
3. Population variance
4. Population coefficient of variation

A sample is selecting a part of the universe (all items with something common). Sampling theory draws relations between these parameters and statistics to estimate the properties of population and precision of estimates. This movement from particular (sample) towards general (universe) is statistical inference/induction. In order to draw this inference, we follow an argument in which we investigate the behaviour of samples drawn from an imagined universe.

It is important to understand several core concepts before one can understand sampling theory.

1. Probability theory
2. Statistical parameters
3. Population
4. Sampling Frame
5. How to make a statistical estimation
6. Testing hypothesis
7. Making statistical inference

Our “instant help in homework writing” also covers the following topics to help students understand sampling theory.

1. Sampling of attributes
2. The sampling of variables (small and large)
3. Methods of sampling
4. Types of sampling (and their pros and cons)

The following topics of this subject are also discussed under the sampling theory:-

1. Central limit theorem
2. Difference between any two sample proportions
3. Sample proportions
4. Mean and variance
5. Sample distributions
6. Statistics for normal random variables

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