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The Resource Converting Data into Evidence : A Statistics Primer for the Medical Practitioner, by Alfred DeMaris, Steven H. Selman, (electronic resource)
Converting Data into Evidence : A Statistics Primer for the Medical Practitioner, by Alfred DeMaris, Steven H. Selman, (electronic resource)
Resource Information
The item Converting Data into Evidence : A Statistics Primer for the Medical Practitioner, by Alfred DeMaris, Steven H. Selman, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item Converting Data into Evidence : A Statistics Primer for the Medical Practitioner, by Alfred DeMaris, Steven H. Selman, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 Converting Data into Evidence: A Statistics Primer for the Medical Practitioner provides a thorough introduction to the key statistical techniques that medical practitioners encounter throughout their professional careers. These techniques play an important part in evidencebased medicine or EBM. Adherence to EBM requires medical practitioners to keep abreast of the results of medical research as reported in their general and specialty journals. At the heart of this research is the science of statistics. It is through statistical techniques that researchers are able to discern the patterns in the data that tell a clinical story worth reporting. The authors begin by discussing samples and populations, issues involved in causality and causal inference, and ways of describing data. They then proceed through the major inferential techniques of hypothesis testing and estimation, providing examples of univariate and bivariate tests. The coverage then moves to statistical modeling, including linear and logistic regression and survival analysis. In a final chapter, a userfriendly introduction to some newer, cuttingedge, regression techniques will be included, such as fixedeffects regression and growthcurve modeling. A unique feature of the work is the extensive presentation of statistical applications from recent medical literature. Over 30 different articles are explicated herein, taken from such journals. With the aid of this primer, the medical researcher will also find it easier to communicate with the statisticians on his or her research team. The book includes a glossary of statistical terms for easy access. This is an important reference work for the shelves of physicians, nurses, nurse practitioners, physician’s assistants, medical students, and residents.
 Language

 eng
 eng
 Edition
 1st ed. 2013.
 Extent
 1 online resource (231 p.)
 Note
 Description based upon print version of record
 Contents

 Preface; Contents; Chapter 1: Statistics and Causality; What Is Statistics?; What Statistics Is; An Example; Populations and Samples; Probability vs. Nonprobability Samples; Sampling "to" a Population; Statistics and Causal Inference; A Mathematical Definition of "Causal Effect"; How Do We Estimate the ACE?; Example of Latent SelfSelection; Internal vs. External Validity: A Conundrum; Chapter 2: Summarizing Data; Descriptive Statistical Techniques; Quantitative vs. Qualitative Data; Describing Data; Measuring Center and Spread of a Variable's Distribution; The Mean
 Percentiles and the MedianDispersion; Data from the General Social Survey; Describing the Population Distribution; The Normal and t Distributions; Applications: Descriptive Statistics in Action; Tarenflurbil Study; Hydroxychloroquine Study; RALP Study; Brachytherapy Study; Chapter 3: Testing a Hypothesis; The Test of Hypothesis; Let's Roll the Dice; Testing Whether Al's Die Is Loaded; Statement of Hypotheses; Testing the Null Hypothesis; Making a Decision; "Statistically Significant" Results; What About Your Sequence of Die Rolls?; LargeSample Test of Hypothesis About a Mean
 Assumptions for the TestStatement of Hypotheses; Before Going Further: The Sampling Distribution of a Sample Statistic; Simple Example of a Sampling Distribution; A More Elaborate Example; Sampling Distribution of the Mean for the LargeSample Test of Hypothesis; The Central Limit Theorem; Test Statistic and P Value; Summary; Chapter 4: Additional Inferential Procedures; Confidence Intervals and the T Test; Confidence Intervals; Testing the Difference Between Two Means: The T Test; Statement of Hypotheses; Sample Information and the Sampling Distribution; Assumptions for the T Test
 Computation of the Test StatisticFinding the P Value; OneTailed vs. TwoTailed Tests; Summary: Hypothesis Testing; Decision Errors and the Power of the Test; Power of the T Test in the Diet Example; T Tests for the GSS Data; Comments About Statistical Tests; P Values, Revisited; Sampling from "The Population"; Application: T Tests and Statistical Power in Action; Gender Difference in Physician Salaries; Power Considerations in Hydroxychloroquine Study; Power in the Arterial Inflammation Study; Chapter 5: Bivariate Statistical Techniques; A Nonparametric Test for the SteakDiet Example
 Computing the WRSTBivariate Statistics; Bivariate Analysis: Other Scenarios; Qualitative Treatment with More Than Two Levels: ANOVA; Qualitative Treatment and Qualitative Response: χ 2; Calculating the χ 2 Value; Minimum and Maximum Values of χ 2; Measuring the Strength of Association; Quantitative Treatment and Response: The Correlation Coefficient; Testing the Significance of R; The Paired t Test: How Correlation Affects the Standard Error; Summary of Bivariate Statistics; Application: Bivariate Statistics in Action; ANOVA: GGT and Alcohol Consumption
 χ 2 : SecondtoFourth Digit Ratio Study
 Isbn
 9781461477921
 Label
 Converting Data into Evidence : A Statistics Primer for the Medical Practitioner
 Title
 Converting Data into Evidence
 Title remainder
 A Statistics Primer for the Medical Practitioner
 Statement of responsibility
 by Alfred DeMaris, Steven H. Selman
 Language

 eng
 eng
 Summary
 Converting Data into Evidence: A Statistics Primer for the Medical Practitioner provides a thorough introduction to the key statistical techniques that medical practitioners encounter throughout their professional careers. These techniques play an important part in evidencebased medicine or EBM. Adherence to EBM requires medical practitioners to keep abreast of the results of medical research as reported in their general and specialty journals. At the heart of this research is the science of statistics. It is through statistical techniques that researchers are able to discern the patterns in the data that tell a clinical story worth reporting. The authors begin by discussing samples and populations, issues involved in causality and causal inference, and ways of describing data. They then proceed through the major inferential techniques of hypothesis testing and estimation, providing examples of univariate and bivariate tests. The coverage then moves to statistical modeling, including linear and logistic regression and survival analysis. In a final chapter, a userfriendly introduction to some newer, cuttingedge, regression techniques will be included, such as fixedeffects regression and growthcurve modeling. A unique feature of the work is the extensive presentation of statistical applications from recent medical literature. Over 30 different articles are explicated herein, taken from such journals. With the aid of this primer, the medical researcher will also find it easier to communicate with the statisticians on his or her research team. The book includes a glossary of statistical terms for easy access. This is an important reference work for the shelves of physicians, nurses, nurse practitioners, physician’s assistants, medical students, and residents.
 http://library.link/vocab/creatorName
 DeMaris, Alfred
 Dewey number
 610.727
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut

 1oHt0Kdu6Ao
 WxRbiQN1twk
 Language note
 English
 LC call number
 QA276280
 Literary form
 non fiction
 Nature of contents
 dictionaries
 http://library.link/vocab/relatedWorkOrContributorName
 Selman, Steven H.
 http://library.link/vocab/subjectName

 Statistics
 Statistics for Life Sciences, Medicine, Health Sciences
 Statistics, general
 Label
 Converting Data into Evidence : A Statistics Primer for the Medical Practitioner, by Alfred DeMaris, Steven H. Selman, (electronic resource)
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Content category
 text
 Content type code
 txt
 Contents

 Preface; Contents; Chapter 1: Statistics and Causality; What Is Statistics?; What Statistics Is; An Example; Populations and Samples; Probability vs. Nonprobability Samples; Sampling "to" a Population; Statistics and Causal Inference; A Mathematical Definition of "Causal Effect"; How Do We Estimate the ACE?; Example of Latent SelfSelection; Internal vs. External Validity: A Conundrum; Chapter 2: Summarizing Data; Descriptive Statistical Techniques; Quantitative vs. Qualitative Data; Describing Data; Measuring Center and Spread of a Variable's Distribution; The Mean
 Percentiles and the MedianDispersion; Data from the General Social Survey; Describing the Population Distribution; The Normal and t Distributions; Applications: Descriptive Statistics in Action; Tarenflurbil Study; Hydroxychloroquine Study; RALP Study; Brachytherapy Study; Chapter 3: Testing a Hypothesis; The Test of Hypothesis; Let's Roll the Dice; Testing Whether Al's Die Is Loaded; Statement of Hypotheses; Testing the Null Hypothesis; Making a Decision; "Statistically Significant" Results; What About Your Sequence of Die Rolls?; LargeSample Test of Hypothesis About a Mean
 Assumptions for the TestStatement of Hypotheses; Before Going Further: The Sampling Distribution of a Sample Statistic; Simple Example of a Sampling Distribution; A More Elaborate Example; Sampling Distribution of the Mean for the LargeSample Test of Hypothesis; The Central Limit Theorem; Test Statistic and P Value; Summary; Chapter 4: Additional Inferential Procedures; Confidence Intervals and the T Test; Confidence Intervals; Testing the Difference Between Two Means: The T Test; Statement of Hypotheses; Sample Information and the Sampling Distribution; Assumptions for the T Test
 Computation of the Test StatisticFinding the P Value; OneTailed vs. TwoTailed Tests; Summary: Hypothesis Testing; Decision Errors and the Power of the Test; Power of the T Test in the Diet Example; T Tests for the GSS Data; Comments About Statistical Tests; P Values, Revisited; Sampling from "The Population"; Application: T Tests and Statistical Power in Action; Gender Difference in Physician Salaries; Power Considerations in Hydroxychloroquine Study; Power in the Arterial Inflammation Study; Chapter 5: Bivariate Statistical Techniques; A Nonparametric Test for the SteakDiet Example
 Computing the WRSTBivariate Statistics; Bivariate Analysis: Other Scenarios; Qualitative Treatment with More Than Two Levels: ANOVA; Qualitative Treatment and Qualitative Response: χ 2; Calculating the χ 2 Value; Minimum and Maximum Values of χ 2; Measuring the Strength of Association; Quantitative Treatment and Response: The Correlation Coefficient; Testing the Significance of R; The Paired t Test: How Correlation Affects the Standard Error; Summary of Bivariate Statistics; Application: Bivariate Statistics in Action; ANOVA: GGT and Alcohol Consumption
 χ 2 : SecondtoFourth Digit Ratio Study
 Dimensions
 unknown
 Edition
 1st ed. 2013.
 Extent
 1 online resource (231 p.)
 Form of item
 online
 Isbn
 9781461477921
 Media category
 computer
 Media type code
 c
 Other control number
 10.1007/9781461477921
 Specific material designation
 remote
 System control number

 (CKB)3710000000002544
 (EBL)1398476
 (SSID)ssj0000962612
 (PQKBManifestationID)11600206
 (PQKBTitleCode)TC0000962612
 (PQKBWorkID)10976075
 (PQKB)11776693
 (DEHe213)9781461477921
 (MiAaPQ)EBC1398476
 (EXLCZ)993710000000002544
 Label
 Converting Data into Evidence : A Statistics Primer for the Medical Practitioner, by Alfred DeMaris, Steven H. Selman, (electronic resource)
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Content category
 text
 Content type code
 txt
 Contents

 Preface; Contents; Chapter 1: Statistics and Causality; What Is Statistics?; What Statistics Is; An Example; Populations and Samples; Probability vs. Nonprobability Samples; Sampling "to" a Population; Statistics and Causal Inference; A Mathematical Definition of "Causal Effect"; How Do We Estimate the ACE?; Example of Latent SelfSelection; Internal vs. External Validity: A Conundrum; Chapter 2: Summarizing Data; Descriptive Statistical Techniques; Quantitative vs. Qualitative Data; Describing Data; Measuring Center and Spread of a Variable's Distribution; The Mean
 Percentiles and the MedianDispersion; Data from the General Social Survey; Describing the Population Distribution; The Normal and t Distributions; Applications: Descriptive Statistics in Action; Tarenflurbil Study; Hydroxychloroquine Study; RALP Study; Brachytherapy Study; Chapter 3: Testing a Hypothesis; The Test of Hypothesis; Let's Roll the Dice; Testing Whether Al's Die Is Loaded; Statement of Hypotheses; Testing the Null Hypothesis; Making a Decision; "Statistically Significant" Results; What About Your Sequence of Die Rolls?; LargeSample Test of Hypothesis About a Mean
 Assumptions for the TestStatement of Hypotheses; Before Going Further: The Sampling Distribution of a Sample Statistic; Simple Example of a Sampling Distribution; A More Elaborate Example; Sampling Distribution of the Mean for the LargeSample Test of Hypothesis; The Central Limit Theorem; Test Statistic and P Value; Summary; Chapter 4: Additional Inferential Procedures; Confidence Intervals and the T Test; Confidence Intervals; Testing the Difference Between Two Means: The T Test; Statement of Hypotheses; Sample Information and the Sampling Distribution; Assumptions for the T Test
 Computation of the Test StatisticFinding the P Value; OneTailed vs. TwoTailed Tests; Summary: Hypothesis Testing; Decision Errors and the Power of the Test; Power of the T Test in the Diet Example; T Tests for the GSS Data; Comments About Statistical Tests; P Values, Revisited; Sampling from "The Population"; Application: T Tests and Statistical Power in Action; Gender Difference in Physician Salaries; Power Considerations in Hydroxychloroquine Study; Power in the Arterial Inflammation Study; Chapter 5: Bivariate Statistical Techniques; A Nonparametric Test for the SteakDiet Example
 Computing the WRSTBivariate Statistics; Bivariate Analysis: Other Scenarios; Qualitative Treatment with More Than Two Levels: ANOVA; Qualitative Treatment and Qualitative Response: χ 2; Calculating the χ 2 Value; Minimum and Maximum Values of χ 2; Measuring the Strength of Association; Quantitative Treatment and Response: The Correlation Coefficient; Testing the Significance of R; The Paired t Test: How Correlation Affects the Standard Error; Summary of Bivariate Statistics; Application: Bivariate Statistics in Action; ANOVA: GGT and Alcohol Consumption
 χ 2 : SecondtoFourth Digit Ratio Study
 Dimensions
 unknown
 Edition
 1st ed. 2013.
 Extent
 1 online resource (231 p.)
 Form of item
 online
 Isbn
 9781461477921
 Media category
 computer
 Media type code
 c
 Other control number
 10.1007/9781461477921
 Specific material designation
 remote
 System control number

 (CKB)3710000000002544
 (EBL)1398476
 (SSID)ssj0000962612
 (PQKBManifestationID)11600206
 (PQKBTitleCode)TC0000962612
 (PQKBWorkID)10976075
 (PQKB)11776693
 (DEHe213)9781461477921
 (MiAaPQ)EBC1398476
 (EXLCZ)993710000000002544
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