Computer-generated errors in statistical analyses.

by Donald F. Burrill in [Ithaca, N.Y.]

Written in English
Published: Pages: 158 Downloads: 113
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Subjects:

  • Numerical analysis,
  • Numerical calculations -- Data processing

Edition Notes

Thesis (Ph.D.)--Cornell Univ., Sept. 1969.

The Physical Object
Paginationv, 158 l.
Number of Pages158
ID Numbers
Open LibraryOL18802889M

Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors. Reading: Analysis of Errors Revised 2/9/13 1 ANALYSIS OF ERRORS Precision and Accuracy Two terms are commonly associated with any discussion of error: "precision" and "accuracy". Precision refers to the reproducibility of a measurement while accuracy is a measure of the closeness to true Size: KB. This book is dynamite: George E. P. Box, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building It starts from zero knowledge of Statistics but it doesn't insult the reader's intelligence. It's incredibly practical but with no loss of rigour; in fact, it underscores the danger of ignoring underlying assumptions (which are often false in real life) of common. One-way analysis of variance. Linear regression and least squares Simple examples, *Use of software*. Recommended books D. A. Berry and B. W. Lindgren, Statistics, Theory and Methods, Duxbury Press, , ISBN G. Casella and J. O. Berger, Statistical Inference, 2nd Edition, Brooks Cole, , ISBN File Size: KB.

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The t-test and Basic Inference Principles The t-test is used as an example of the basic principles of statistical inference. One of the simplest situations for which we might design an experiment is the case of a nominal two-level explanatory variable and a quantitative outcome File Size: KB. The Two Main Types of Statistical Analysis In the real world of analysis, when analyzing information, it is normal to use both descriptive and inferential types of statistics. Commonly, in many research run on groups of people (such as marketing research for defining market segments), are used both descriptive and inferential statistics to. Many of the books have web pages associated with them that have the data files for the book and web pages showing how to perform the analyses from the book using packages like SAS, Stata, SPSS, etc. Please see our Textbook Examples page. Statistics - Statistics - Hypothesis testing: Hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter or a population probability distribution. First, a tentative assumption is made about the parameter or distribution. This assumption is called the null hypothesis and is denoted by H0.

Computer-generated errors in statistical analyses. by Donald F. Burrill Download PDF EPUB FB2

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Library of Congress Cataloging-in-Publication Data: Good, Phillip I. Common errors in statistics (and how to avoid them)/Phillip I.

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It discusses how to include EDA techniques. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features').

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One important modern variant of Bayesian thinking, which now receives attention within the error-statistical framework, is the falsificationist Bayesianism of Andrew Gelman, which received its major formulation in Gelman and Shalizi ().

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Statistical errors can be a costly affair, if not checked or looked into it carefully. Biased Data: Bias in statistics can be termed as over or underestimating the true value. Below are some most common sources or reasons for such errors. Measurement instruments that are systematically off and causing such bias.

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Introductory Statistics includes innovative practical applications that make the text. 4 USES OF UNCERTAINTY ANALYSIS (I) • Assess experimental procedure including identification of potential difficulties – Definition of necessary steps – Gaps • Advise what procedures need to be put in place for measurement • Identify instruments and procedures that control accuracy and precision – Usually one, or at most a small number, out of the large set ofFile Size: KB.Outliers can affect any statistical analysis, thereby outlier should be investigated and deleted, corrected, or explained as appropriate.

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The graph of the distribution is File Size: KB.