The purpose of statistical inference is to obtain information about a population form information contained in a sample.
statistical inference
The importance of statistical modeling is obvious because we often need modelling for the purpose of prediction, to describe the phenomena and many procdures in statistics are based on assumption of a statistical model. Modeling is also important for statistical inference and make decision about population parameter. M. Yousaf Khan
Statistical sampling is an objective approach using probability to make an inference about the population. The method will determine the sample size and the selection criteria of the sample. The reliability or confidence level of this type of sampling relates to the number of times per 100 the sample will represent the larger population. Non-statistical sampling relies on judgment to determine the sampling method,the sample size,and the selection items in the sample.
The term usually applies to statistics that are compiled by unofficial organisations or by organisations whose activities are not reviewed by reputable statistical bodies.
Statistical inference occurs when
Define statistical inference and give an example
What is the use of statistical inference in technology?
The purpose of statistical inference is to obtain information about a population form information contained in a sample.
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The homonym of "inference" is "inference." A homonym is a word that sounds the same as another word but has a different meaning.
The science term is "inference".
statistical inference
It emphasizes the role of computation as a fundamental tool of discovery in data analysis, of statistical inference and for development of statistical theory and methods.
Rupert Griel Miller has written: 'Simultaneous statistical inference'
Deduction.
Shelemyahu Zacks has written: 'The theory of statistical inference' -- subject(s): Mathematical statistics 'Parametric statistical inference' -- subject(s): Mathematical statistics 'Stochastic visibility in random fields' -- subject(s): Random fields, Visibility, Mathematical models