Estimation And Inferential Statistics Pdf
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Estimation and Inferential Statistics
Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In machine learning , the term inference is sometimes used instead to mean "make a prediction, by evaluating an already trained model";  in this context inferring properties of the model is referred to as training or learning rather than inference , and using a model for prediction is referred to as inference instead of prediction ; see also predictive inference. Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.
Essentials of Inferential Statistics
Published on September 4, by Pritha Bhandari. Revised on March 2, While descriptive statistics summarize the characteristics of a data set, inferential statistics help you come to conclusions and make predictions based on your data. When you have collected data from a sample , you can use inferential statistics to understand the larger population from which the sample is taken. Table of contents Descriptive versus inferential statistics Estimating population parameters from sample statistics Hypothesis testing Frequently asked questions about inferential statistics.
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The purpose of this chapter is to explain the basic reasoning of inferential statistics, and then to show how confidence statements are to be made and interpreted. The calculations of the margins of error and the relationship between the confidence level and the margin of error are also shown. Inferential Statistics: Estimation Search form. Show page numbers Download PDF.
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