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The process of getting to a conclusion based on the available data that varies randomly is called statistical interference. Many times, statistical interference is used to explain various structures or procedures that are applied to draw conclusions or interpretations. These are drawn based on the data sets gathered by the systems that are affected by the random variation. The random variations would include observational errors, random experimentation, and random sampling. There are various procedures used by the system to carry out inference and induction. The initial specifications of the system would be used to get an answer when it is applied to a specific situation. The result that one gets to a specific situation would be used for various other similar situations. The inferential statistics would be used to test hypotheses and also to make the right estimates based on the given sample information.
Statistical inference is widely used in different situations to draw conclusions from the available dataset. It uses various approaches that are available to carry out statistical inference. There is a wide range of tools that are available in the market to estimate parameters. The quite simple and easy method that is used is Likelihood Ratios. The accuracy of statistical inference is used to carry out next-generation medical predictive analysis and artificial intelligence systems...
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Statistical tests of Hypotheses | Null and Alternative hypotheses |
Types of Errors | Critical Region |
Level of Significance | Power and p-values |
Equality of two means | A single variance and the equality of two variances |
Test of Significance of sample correlation coefficient | Interval Estimation |
Confidence Interval and Confidence Coefficient | Exact confidence interval under Normal set-up for a single mean |
Single variance | Large Sample Tests |
Pearsons x2 tests | Homogeneity and independence in a contingency table |
Neyman factorization theorem | Minimal sufficient statistics |
Maximum likelihood method | Method of moments |
Minimum chi-square method | Minimum variance unbiased estimators |
Rao- Blackwell theorem | Cramer – Rao lower bound approach |
One sample u statistics | Two sample u statistics |
Asymptotic distributions of u statistics | Rank test |
Linear rank statistics | One sample location problem |