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Record Nr. |
UNINA9911006600703321 |
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Autore |
Natrella Mary Gibbons |
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Titolo |
Experimental Statistics |
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Pubbl/distr/stampa |
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Newburyport, : Dover Publications, c2005 |
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ISBN |
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9780486154558 |
0486154556 |
9781628704365 |
1628704365 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (1007 p.) |
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Collana |
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Dover Books on Mathematics |
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Disciplina |
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Soggetti |
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Mathematical statistics |
Experimental design |
Mathematics |
Physical Sciences & Mathematics |
Mathematical Statistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Cover; Title Page; Copyright Page; Preface; Notice; Foreword; Contents; Section 1 Basic Statistical Concepts and Standard Techniques for Analysis and Interpretation of Measurement Data; Chapter 1 Some Basic Statistical Concepts and Preliminary Considerations; 1-1 Introduction; 1-2 Populations, Samples, and Distributions; 1-3 Statistical Inferences and Sampling; 1-3.1 Statistical Inferences; 1-3.2 Random Sampling; 1-4 Selection of a Random Sample; 1-5 Some Properties of Distributions; 1-6 Estimation of m and σ; 1-7 Confidence Intervals; 1-8 Statistical Tolerance Limits |
1-9 Using Statistics to Make Decisions1-9.1 Approach to a Decision Problem; 1-9.2 Choice of Null and Alternative Hypotheses; 1-9.3 Two Kinds of Errors; 1-9.4 Significance Level and Operating Characteristic (OC) Curve of a Statistical Test; 1-9.5 Choice of the Significance Level; 1-9.6 A Word of Caution; Chapter 2 Characterizing the Measured Performance of a Material, Product, or Process; 2-1 Estimating Average Performance From a Sample; 2-1.1 General; 2-1.2 Best Single Estimate; 2-1.3 Some Remarks on Confidence Interval Estimates |
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2-1.4 Confidence Intervals for the Population Mean When Knowledge of the Variability Cannot Be Assumed2-1.4.1 Two-sided Confidence Interval; 2-1.4.2 One-sided Confidence Interval; 2-1.5 Confidence Interval Estimates When We Have Previous Knowledge of the Variability; 2-2 Estimating Variability of Performance From a Sample; 2-2.1 General; 2-2.2 Single Estimates; 2-2.2.1 s2 and s; 2-2.2.2 The Sample Range as an Estimate of the Standard Deviation; 2-2.3 Confidence Interval Estimates; 2-2.3.1 Two-sided Confidence Interval Estimates; 2-2.3.2 One-sided Confidence Interval Estimates |
2-2.4 Estimating the Standard Deviation When No Sample Data are Available2-3 Number of Measurements Required to Establish the Mean with Prescribed Accuracy; 2-3.1 General; 2-3.2 Estimation of the Mean of a Population Using a Single Sample; 2-3.3 Estimation Using a Sample Which is Taken In Two Stages; 2-4 Number of Measurements Required to Establish the Variability with Stated Precision; 2-5 Statistical Tolerance Limits; 2-5.1 General; 2-5.2 Two-sided Tolerance Limits for a Normal Distribution; 2-5.3 One-sided Tolerance Limits for a Normal Distribution |
2-5.4 Tolerance Limits Which are Independent of the Form of the Distribution2-5.4.1 Two-sided Tolerance Limits (Distribution-Free); 2-5.4.2 One-sided Tolerance Limits (Distribution-Free); Chapter 3 Comparing Materials or Products With Respect to Average Performance; 3-1 General Remarks on Statistical Tests; 3-2 Comparing the Average of a New Product With That of a Standard; 3-2.1 To Determine Whether the Average of a New Product Differs From the Standard; 3-2.1.1 Does the Average of the New Product Differ From the Standard (σ Unknown)? |
3-2.1.2 Does the Average of the New Product Differ From the Standard (σ Known)? |
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Sommario/riassunto |
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Formulated to assist scientists and engineers engaged in army ordnance research and development programs, this well-known and highly regarded handbook is a ready reference for advanced undergraduate and graduate students as well as for professionals seeking engineering information and quantitative data for designing, developing, constructing, and testing equipment. Topics include characterizing and comparing the measured performance of a material, product, or process; general considerations in planning experiments; statistical techniques for analyzing extreme-value data; use of transformations |
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