LEADER 05445nam 2200637Ia 450 001 9911019901403321 005 20230331010148.0 010 $a1-283-29449-4 010 $a9786613294494 010 $a0-470-31677-2 010 $a0-470-31744-2 035 $a(CKB)2550000000056551 035 $a(EBL)695349 035 $a(SSID)ssj0000555556 035 $a(PQKBManifestationID)11342810 035 $a(PQKBTitleCode)TC0000555556 035 $a(PQKBWorkID)10519520 035 $a(PQKB)10626858 035 $a(MiAaPQ)EBC695349 035 $a(OCoLC)761318479 035 $a(EXLCZ)992550000000056551 100 $a19910201d1991 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical intervals$b[electronic resource] $ea guide for practitioners /$fGerald J. Hahn, William Q. Meeker 210 $aNew York $cWiley$dc1991 215 $a1 online resource (422 p.) 225 1 $aWiley series in probability and mathematical statistics. Applied probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-88769-2 320 $aIncludes bibliographical references and indexes. 327 $aStatistical Intervals: A Guide for Practitioners; Contents; 1. Introduction, Basic Concepts, and Assumptions; 1.1. Statistical Inference; 1.2. Different Types of Statistical Intervals: An Overview; 1.3. The Assumption of Sample Data; 1.4. The Central Role of Practical Assumptions Concerning ""Representative Data""; 1.5. Enumerative versus Analytic Studies; 1.6. Basic Assumptions for Enumerative Studies; 1.7. Additional Aspects of Analytic Studies; 1.8. Convenience and Judgment Samples; 1.9. Sampling People; 1.10. Infinite Population Assumptions; 1.11. Practical Assumptions: Overview 327 $a1.12. Practical Assumptions: Further Example1.13. Planning the Study; 1.14. The Role of Statistical Distributions; 1.15. The Interpretation of a Statistical Interval; 1.16. Comments Concerning Subsequent Discussion; 2. Overview of Different Types of Statistical Intervals; 2.1. Choice of a Statistical Interval; 2.2. Confidence Intervals; 2.3. Prediction Intervals; 2.4. Statistical Tolerance Intervals; 2.5. Which Statistical Interval Do I Use?; 2.6. Choosing a Confidence Level; 2.7. Statistical Intervals versus Significance Tests 327 $a3. Constructing Statistical Intervals Assuming a Normal Distribution Using Simple Tabulations3.1. Introduction; 3.2. Numerical Example; 3.3. Two-Sided Statistical Intervals; 3.4. One-Sided Statistical Bounds; 4. Methods for Calculating Statistical Intervals for a Normal Distribution; 4.1. Introduction; 4.2. Confidence Interval for the Mean of a Normal Distribution; 4.3. Confidence Interval for the Standard Deviation of a Normal Distribution; 4.4. Confidence Interval for a Percentile of a Normal Distribution; 4.5. Confidence Interval for the Proportion Less (Greater) than a Specified Value 327 $a4.6. Statistical Tolerance Intervals to Contain a Proportion of a Population4.7. Prediction Interval to Contain a Single Future Observation or the Mean of m Future Observations; 4.8. Prediction Interval to Contain All of m Future Observations; 4.9. Prediction Interval to Contain the Standard Deviation of m Future Observations; 4.10. The Assumption of a Normal Distribution; 4.11. Assessing Distribution Normality and Dealing with Nonnormality; 4.12. Inferences from Transformed Data; 5. Distribution-Free Statistical Intervals; 5.1. Introduction 327 $a5.2. Distribution-Free Confidence Intervals for a Percentile5.3. Distribution-Free Tolerance Intervals and Bounds to Contain a Specified Percentage of a Population; 5.4. Distribution-Free Prediction Intervals to Contain at Least k of m Future Observations; 5.5. Prediction Intervals to Contain a Specified Ordered Observation in a Future Sample; 6. Statistical Intervals for Proportions and Percentages (Binomial Distribution); 6.1. Introduction; 6.2. Confidence Intervals for the (True) Proportion Nonconforming in the Sampled Population (or Process) 327 $a6.3. Confidence Intervals for the Probability That the Number of Nonconforming Units in a Future Sample is Less Than or Equal to (or Greater than) a Specified Number 330 $aPresents a detailed exposition of statistical intervals and emphasizes applications in industry. The discussion differentiates at an elementary level among different kinds of statistical intervals and gives instruction with numerous examples and simple math on how to construct such intervals from sample data. This includes confidence intervals to contain a population percentile, confidence intervals on probability of meeting specified threshold value, and prediction intervals to include observation in a future sample. Also has an appendix containing computer subroutines for nonparametric stati 410 0$aWiley series in probability and mathematical statistics.$pApplied probability and statistics. 606 $aMathematical statistics 606 $aStatistics 615 0$aMathematical statistics. 615 0$aStatistics. 676 $a519.5 700 $aHahn$b Gerald J$010188 701 $aMeeker$b William Q$028070 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019901403321 996 $aStatistical intervals$94422696 997 $aUNINA