How Long to Read Introduction to Statistical Modelling and Inference

How Long Does it Take to Read Introduction to Statistical Modelling and Inference?

It takes the average reader to read Introduction to Statistical Modelling and Inference by Murray A. Aitkin

Assuming a reading speed of 250 words per minute. Learn more

Description

"The complexity of large-scale data sets ("Big Data") has stimulated the development of advanced computational methods for analyzing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory, while the model-free method does not require a probability model, likelihood or Bayesian theory. These two approaches are based on different philosophical principles of probability theory, espoused by the famous statisticians Ronald Fisher and Jerzy Neyman Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some extensions of these, including finite mixtures. A wide range of examples from different application fields are also discussed and analyzed. No special software is used, beyond that needed for maximum likelihood analysis of generalised linear models. Students are expected to have a basic mathematical background of algebra, coordinate geometry and calculus. Features Probability models are developed from the shape of the sample empirical cumulative distribution function, (cdf) or a transformation of it. Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf. Bayes's theorem is developed from the properties of the screening test for a rare condition. The multinomial distribution provides an always-true model for any randomly sampled data. The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel - the Bayesian bootstrap - based on the always-true multinomial distribution. The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model. This book is aimed at students in a wide range of disciplines including Data Science. The book is based on the model-based theory, used widely by scientists in many fields, and compares it, in less detail, with the model-free theory, popular in computer science, machine learning and official survey analysis. The development of the model-based theory is accelerated by recent developments in Bayesian analysis"--

How long is Introduction to Statistical Modelling and Inference?

Introduction to Statistical Modelling and Inference by Murray A. Aitkin is 0 pages long, and a total of 0 words.

This makes it 0% the length of the average book. It also has 0% more words than the average book.

How Long Does it Take to Read Introduction to Statistical Modelling and Inference Aloud?

The average oral reading speed is 183 words per minute. This means it takes to read Introduction to Statistical Modelling and Inference aloud.

What Reading Level is Introduction to Statistical Modelling and Inference?

Introduction to Statistical Modelling and Inference is suitable for students ages 2 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

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