Understanding statistics and statistical myths : how to become a profound learner /: how to become a profound learner. (2015)

• Record Type: Book
• Further Information: Note: Kicab Castañeda-Méndez.
• Authors: Castañeda-Méndez, Kicab
• Contents: Myth 1: Two Types of Data—Attribute/Discrete and Measurement/Continuous; Background; Measurement Requires Scale; Gauges or Instruments vs. No Gauges; Discrete, Categorical, Attribute versus Continuous, Variable: Degree of Information; Creating Continuous Measures by Changing the "Thing" Measured; Discrete versus Continuous: Half Test; Nominal, Ordinal, Interval, Ratio; Measurement to Compare; Scale Type versus Data Type; Scale Taxonomy; Purpose of Data Classification; ; Myth 2: Proportions and Percentages Are Discrete Data; Background; Denominator for Proportions and Percentages; Probabilities; Classification of Proportions, Percentages, and Probabilities; ; Myth 3: s = √[Σ(Xi - x)2 /(n- 1)] The Correct Formula for Sample Standard Deviation ; Background; Correctness of Estimations; Estimators and Estimates; Properties of Estimators; ; Myth 4: Sample Standard Deviation √[Σ(Xi -x)2 /(n- 1)] Is Unbiased; Background; Degrees of Freedom; t Distribution; Definition of Bias; Removing Bias and Control Charts; ; Myth 5: Variances Can Be Added but Not Standard Deviations; Background; Sums of Squares and Square Roots: Pythagorean Theorem; Functions and Operators; Random Variables; Independence of Factors; Other Properties; ; Myth 6: Parts and Operators for an MSA Do Not Have to Be Randomly Selected; Background; Types of Analyses of Variance; Making Measurement System Look Better than It Is: Selecting Parts to Cover the Range of Process Variation; Selecting Both Good and Bad Parts; ; Myth 7: % Study (% Contribution, Number of Distinct Categories) Is the Best Criterion for Evaluating a Measurement System for Process Improvement; Background; % Contribution versus % Study; P /T Ratio versus % Study; Distinguishing between Good and Bad Parts; Distinguishing Parts That Are Different; ; Myth 8: Only Sigma Can Compare Different Processes and Metrics; Background; Sigma and Specifications; Sigma as a Percentage; ; Myth 9: Capability Is Not Percent/Proportion of Good Units; Background; Capability Indices: Frequency Meeting Specifications; Capability: Actual versus Potential; Capability Indices; Process Capability Time-Dependent; Meaning of Capability: Short-Cut Calculations; ; Myth 10: p = Probability of Making an Error; Background; Only Two Types of Errors; Definition of an Error about Deciding What Is True; Calculation of p and Evidence for a Hypothesis; Probability of Making an Error for a Particular Case; Probability of Data Given Ho versus Probability of Ho Given Data; Non-probabilistic Decisions; ; Myth 11: Need More Data for Discrete Data than Continuous Data Analysis; Background; Discrete Examples When n = 1; Factors That Determine Sample Size; Relevancy of Data; ; Myth 12: Nonparametric Tests Are Less Powerful than Parametric Tests; Background; Distribution Free versus Nonparametric; Comparing Power for the Same Conditions; Different Formulas for Testing the Same Hypotheses; Assumptions of Tests; Comparing Power for the Same Characteristic; Converting Quantitative Data to Qualitative Data; ; Myth 13: Sample Size of 30 Is Acceptable (for Statistical Significance); Background; A Rationale for n = 30; Contradictory Rules of Thumb; Uses of Data; Sample Size as a Function of Alpha, Beta, Delta, and Sigma; Sample Size for Practical Use; Sample Size and Statistical Significance; ; Myth 14: Can Only Fail to Reject Ho, Can Never Accept Ho ; Background; Proving Theories: Sufficient versus Necessary; Prove versus Accept versus Fail to Reject: Actions; Innocent versus Guilty: Problems with Example; Two-Choice Testing; Significance Testing and Confidence Intervals; Hypothesis Testing and Power; Null Hypothesis of ≥ or ≤; Practical Cases; Which Hypothesis Has the Equal Sign?; Bayesian Statistics: Probability of Hypothesis; ; Myth 15: Control Limits Are ±3 Standard Deviations from the Center Line; Background; Standard Error versus Standard Deviation; Within- versus between-Subgroup Variation: How Control Charts Work; I Chart of Individuals; ; Myth 16: Control Chart Limits Are Empirical Limits; Background; Definition of Empirical; Empirical Limits versus Limits Justified Empirically; Shewhart’s Evidence of Limits Being Empirical; Wheeler’s Empirical Rule; Empirical Justification for a Purpose; ; Myth 17: Control Chart Limits Are Not Probability Limits; Background; Association of Probabilities and Control Chart Limits; Can Control Limits Be Probability Limits?; False Alarm Rates for All Special Cause Patterns; Wheeler Uses Probability Limits; Other Uses of Probability Limits; ; Myth 18: ±3 Sigma Limits Are the Most Economical Control Chart Limits; Background; Evidence for 3–Standard Error Limits Being Economically Best; Evidence against 3–Standard Error Limits Being the Best Economically; Counterexamples: Simple Cost Model Other Out-of-Control Rules—Assignable Causes Shewhart Didn’t Find but Exist; Small Changes Are Not Critical to Detect versus Taguchi’s Loss Function; Importance of Subgroup Size and Frequency on Economic Value of Control Chart Limits; Purpose to Detect Lack of Control—3–Standard Error Limits Misplaced; ; Myth 19: Statistical Inferences Are Inductive Inferences; Background; Reasoning: Validity and Soundness; Induction versus Deduction; Four Cases of Inductive Inferences; Statistical Inferences: Probability Distributions; Inferences about Population Parameters; Deductive Statistical Inferences: Hypothesis Testing; Deductive Statistical Inferences: Estimation; Real-World Cases of Statistical Inferences; ; Myth 20: There Is One Universe or Population If Data Are Homogeneous; Background; Definition of Homogeneous; Is Displaying Stability Required for Universes to Exist?; Are There Always Multiple Universes If Data Display Instability?; Is There Only One Universe If Data Appropriately Plotted Display Stability?; Control Chart Framework: Valid and Invalid Conclusions; ; Myth 21: Control Charts Are Analytic Studies; Background; Enumerative versus Analytic Distinguishing Characteristics; Enumerative Problem, Study, and Solution; Analytic Problem, Study, and Solution; Procedures for Enumerative and Analytic Studies; Are Control Charts Enumerative or Analytic Studies?; Cause–Effect Relationship; An Analytic Study Answers "When?"; ; Myth 22: Control Charts Are Not Tests of Hypotheses; Background; Definition and Structure of Hypothesis Test; Control Chart as a General Hypothesis Test; Statistical Hypothesis Testing: Alpha and p ; Analysis of Means; Shewhart’s View on Control Charts as Tests of Hypotheses; Deming’s Argument: No Definable, Finite, Static Population; Woodall’s Two Phases of Control Chart Use; Finite, Static Universe; Control Charts as Nonparametric Tests of Hypotheses; Utility of Viewing Control Charts as Statistical Hypothesis Tests; Is the Process in Control? versus What Is the Probability the Process Changed?; ; Myth 23: Process Needs to Be Stable to Calculate Process Capability; Background; Stability and Capability: Dependent or Independent?; Actual Performance and Potential Capability versus Sta
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2015
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.5; Statistics; Problem solving -- Statistical methods
• Languages: English
• ISBNs: 9781498727464
• Related ISBNs: 9781498727457
• Notes: Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.