3 edition of **Making statistical inferences about software reliability** found in the catalog.

Making statistical inferences about software reliability

- 109 Want to read
- 17 Currently reading

Published
**1988** by National Aeronautics and Space Administration, Scientific and Technical Information Division, For sale by the National Technical Information Service] in [Washington, DC], [Springfield, Va .

Written in English

- Computer software -- Reliability -- Statistical methods.,
- Fault-tolerant computing.

**Edition Notes**

Statement | Douglas R. Miller. |

Series | NASA contractor report -- 4197., NASA contractor report -- NASA CR-4197. |

Contributions | United States. National Aeronautics and Space Administration. Scientific and Technical Information Division. |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL15370617M |

Statistical inference is defined as the process inferring the properties of the given distribution based on the data. In other words, it deduces the properties of the population by conducting hypothesis testing and obtaining estimates. Here, the data used in the analysis are obtained from the larger population. For our purposes, statistics is both a collection of numbers and/or pictures and a process: the art and science of making accurate guesses about outcomes involving numbers. So, fundamentally, the goals of statistics are To describe variables and data; To make accurate inferences about groups based upon incomplete information. Book Description Taylor & Francis Inc, United States, Hardback. Condition: New. 6th New edition. Language: English. Brand new Book. Prepare Your Students for Statistical Work in the Real WorldStatistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences.5/5(1). On Reliability Approach and Statistical Inference in Demography 39 make a bathtub shape, but now there are some models like the generalized Weibull model which can have bathtub shape (Bagdonavicius and Nikulin ()). The Weibull law is more commonly applicable .

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It described how the living cell works with very good animations presented. Toward the end of the vide. The first step in making a statistical inference is to model the population(s) by a probability distribution which has a numerical feature of interest called a parameter.

The problem of statistical inference arises once we want to make generalizations about the population when only a sample is available. Statistical inference is a process of drawing general conclusions from data in a specific sample.

Typical inferential problems are: Does alternative A give higher return than alternative B. Is drug A more Making statistical inferences about software reliability book than drug B. In both cases solutions are based on observations in a single sample/5(40). The essence of statistical inference is making a conclusion about the large unknown and unknowable (population) from the statistical inferences is accommodating sample The book also covers.

Making statistical inferences about software reliability. By Douglas R. Miller. Abstract. Failure times of software undergoing random debugging can be modeled as order statistics of independent but nonidentically distributed exponential random variables.

Using this model inferences can be made about current reliability and, if debugging Author: Douglas R. Miller. Inferences and decisions. A statistical inference will be defined for the purposes of the present paper to be a statement about statistical populations made from given observations with measured uncertainty.

An inference in general is an uncertain conclusion. Two things mark out statistical inferences. First,Cited by: Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving is assumed that the observed data set is sampled from a larger population.

Inferential statistics can be contrasted with descriptive statistics. Since the s, statistical methods have been developed for making reliability inferences from degradation data.

Initially these were developed by researchers or engineers in need of the methods. Statistical methods for the analysis of degradation data are, however, now beginning to be deployed in commercial statistical software. Models of Randomness and Statistical Inference Statistics is a discipline that provides with a methodology allowing to make an infer-ence from real random data on parameters of probabilistic models that are believed to generate such data.

The position of File Size: 1MB. Methods of statistical inference help us in estimating the characteristics of the entire population based on the data collected from a sample. Statistical techniques are extremely useful in algorithm evaluation, system performance evaluation, and reliability estimation.

DeborahAnn Hall, KarimaSusi, in Handbook of Clinical Neurology, Statistical inference. Statistical inference refers to the process of drawing conclusions from the model estimation. When computing the GLM, a β value is estimated for each regressor (i.e., column in the design matrix).

β values can be used to compare regressors and compute activation maps by creating t statistics and. Title: Statistical Inference Author: George Casella, Roger L.

Berger Created Date: 1/9/ PM. Statistical Inference Definition. Statistical inference is the process of analysing the result and making conclusions from data subject to random variation.

It is also called inferential statistics. Hypothesis testing and confidence intervals are the applications of the statistical inference. Statistical inference is a. Chapter13 Learning to Reason About Statistical Inference Despite all the criticisms that we could offer of the traditional introductory statistics course, it at least has a clear objective: to teach ideas central to statistical inference.

(Konold & Pollatsek,p. ) Snapshot of a Research-Based Activity on Statistical InferenceFile Size: KB. This course introduces the use of statistics for business decision making. After completion of this course, students will be able to explain how to obtain a suitable sample of business data and evaluate its validity and reliability for statistical inferences, produce tables and charts to organize and display business data, interpret numerical business data using measures of central tendency.

Every unit begins with an Initial Task and ends with a Balanced Assessment, both focusing on core mathematics of the unit. The core mathematics is developed through a series of resources around Big Ideas; as you move through the unit, keep students focused on how these ideas are connected and how they address mathematical problem solving.

Before attempting the Balanced Assessment, students. Lecture Notes: Statistical Inference Page 2 ESTIMATION There are two types of estimates of population parameters: point estimate interval estimate A point estimate is a single number used as an estimator of a population parameter.

The problem with using a single point (or value) is that it might be right or wrong. Introduction. Statistics play an important role in all research, but especially in medicine and the biological sciences.

Statistical inference provides the link between the study sample and the source population (hereafter referred to as sample and population for the sake of brevity), and statistics must be used to provide valid inferences about the relationship between an outcome.

Use statistical methods to make an inference Teacher Preparation 3 new. Wrangling Statistics Assesssments Making sense of the meaningful data all around us. – Michael Walden. CensusAtSchool New Zealand is supported by. The third, which sets the stage for statistical inference, is that access to a com-plete set of data is either not feasible from a practical standpoint or is physically impossible to obtain.

To more fully describe statistical inference, it is necessary to introduce sev-eral key terminologies and concepts.

The ﬁrst step in making a statistical in. Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data.

In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every.

Amstat News asked three review editors to rate their top five favorite books in the September issue. Statistical Methods for Reliability Data was among those chosen.

Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products.

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in Info: Course 6 of 10 in the Data.

This book builds theoretical statistics from thefirst principles of probability theory. Startingfrom the basics of probability, the authorsdevelop the theory of statistical inferenceusing techniques, definitions and conceptsthat are statistical and are natural extensionsand consequences of previous ed for first-year graduate students, thisbook can be used for students /5().

Lecture: Sampling Distributions and Statistical Inference Sampling Distributions population – the set of all elements of interest in a particular study. sample – a sample is a subset of the population. random sample (finite population) – a simple random sample of size n from a finiteFile Size: KB.

Learn Improving your statistical inferences from Eindhoven University of Technology. This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect Price: $ Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels.

The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools. Bayesian Modeling, Inference and Prediction 3 Frequentist { Plus: Mathematics relatively tractable.

{ Minus: Only applies to inherently repeatable events, e.g., from the vantage point of (say)PF(the Republicans will win the White House again in ) is (strictly speaking) unde ned. Bayesian. This book builds theoretical statistics from the first principles of probability theory.

Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural 4/5(61). tity of interest.

Inferences are never certain, so any honest presentation of statistical results must include some qualifier, such as “plus or minus $” in the present example. Making the Most of Statistical Analyses: Improving Interpretation and Presentation Gary King Harvard University Michael Tomz Harvard University.

Statistical Inference: How reliable is a survey. Consider a survey with a single question, to which respondents are asked to give an answer of yes or no.

Suppose you pick a random sample of n people, and you ﬁnd that the proportion that answered yes is ˆp. Statistical Inference (PDF) 2nd Edition builds theoretical statistics from the first principles of probability theory.

Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts.

Now updated in a valuable new edition—this user-friendly book focuses on understanding the "why" of mathematical statistics. Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application.

Use statistical software to summarize data numerically and visually, and to perform data analysis. Have a conceptual understanding of the uniﬁed nature of statistical inference.

Apply estimation and testing methods to analyze single variables or the relationship between two variables in. This chapter discusses methods for obtaining statistical inferences in the reliability context. It explains the method of maximum likelihood estimation.

The chapter illustrates the calculation of maximum likelihood estimates in certain special cases, and discusses the generalized gamma model and a mixture model. STATISTICAL METHODS 1 STATISTICAL METHODS Arnaud Delorme, Swartz Center for Computational Neuroscience, INC, University of San Diego California, CA, La Jolla, USA.

Email: [email protected] Keywords: statistical methods, inference, models, clinical, software, bootstrap, resampling, PCA, ICA Abstract: Statistics represents that body of methods by which characteristics of. Ismor Fischer, 1/8/ 6.

Statistical Inference and Hypothesis Testing. One Sample § Mean. STUDY POPULATION = Cancer patients on. A fine blend of the three disciplines, viz.

quality, reliability and maintainability. This book provides a clear understanding of the concepts and discusses their applications using statistical. Amstat News asked three review editors to rate their top five favorite books in the September issue.

Statistical Methods for Reliability Data was among those ng statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products.

Software Reliability is the probability of failure-free software operation for a specified period of time in a specified environment. Software Reliability is also an important factor affecting system reliability.

It differs from hardware reliability in that it reflects the design perfection, rather than manufacturing perfection.Lindgren's book contains a proof that the location-scale family of Cauchy distributions admits no coarser sufficient statistic than the order statistic (i.e. an i.i.d. sample sorted into increasing order); maybe that's not a crucial thing but it's something you find frequently .UNDERSTANDING RESEARCH RESULTS: STATISTICAL INFERENCE.

A FEW TERMS. A FEW TERMS. 9Allow researchers to make inferences about the true differences in populations of scores based on a sample of data from that population making an estimate.

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