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( 37 )

198 pages

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English

Statistical inference is a process of drawing general conclusions from data in a specific sample.

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Statistical inference is a process of drawing general conclusions from data in a specific sample. Typical inferential problems are: Does alternative A give higher return than alternative B? Is drug A more effective than drug B? In both cases solutions are based on observations in a single sample.

To solve inferential problems one has to deal with the problems: (i) How to find the best estimate of an unknown quantity, (ii) How to find an interval that covers the true unknown value and (iii) How to test hypothesis about the value of an unknown quantity. The treatment of these issues can be found in a large amount of statistical textbooks. The present book differs from the latter since it focuses on problem solving and only a minimum of the theory needed is presented.

- Introduction
- Purpose of this book
- Chapter content and plan of the book
- Statistical tables and facilities

- Basic probability and mathematics
- Probability distributions of discrete and continuous random variables
- Some distributions
- Mathematics
- Final words

- Sampling Distributions
- Some exact sampling distributions
- Sample moments
- Asymptotic and approximate results in sampling theory
- Final words

- Point estimation
- Concepts
- Requirements on estimators
- Estimation methods
- Final words

- Interval estimation
- Concepts
- CIs in small samples by means of pivotal statistics
- Approximate CIs in large samples based on Central Limit Theorems
- Some further topics
- Final words

- Hypothesis Testing
- Concepts
- Methods of finding tests
- The power of normally distributed statistics
- Adjusted p-values for simultaneous inference
- Randomized tests
- Some tests for linear models
- Final wjords

Util para pesquisa cientifica.

March 6, 2019 at 11:56 AM