A Computational Approach to Statistical Arguments in Ecology and Evolution.:
Teaches powerful methods to test hypotheses using statistical arguments without the constraints and sophisticated mathematics of classical statistics.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge :
Cambridge University Press,
2011.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Teaches powerful methods to test hypotheses using statistical arguments without the constraints and sophisticated mathematics of classical statistics. |
| Beschreibung: | Testing PERMUTE. |
| Beschreibung: | 1 online resource (268 pages) |
| Bibliographie: | Includes bibliographical references (pages 253-255) and index. |
| ISBN: | 9781139117562 1139117564 9781139128223 1139128221 9781139115391 1139115391 9780511783708 0511783701 9780521179232 0521179238 1107220963 9781107220966 1139124803 9781139124805 1283314967 9781283314961 1139123319 9781139123310 9786613314963 661331496X 1139113208 9781139113205 |
Internformat
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| 100 | 1 | |a Estabrook, George F. |0 http://id.loc.gov/authorities/names/nb2011027708 | |
| 245 | 1 | 2 | |a A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| 260 | |a Cambridge : |b Cambridge University Press, |c 2011. | ||
| 300 | |a 1 online resource (268 pages) | ||
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| 505 | 0 | |a Cover; Title; Copyright; Contents; Acknowledgments; 1 Introduction; 1.1 About the book; Purpose; Intended readers; Why use computation; Prerequisites; How to use this book; Brief overview; 1.2 Basic principles; Applicability; Argument style; Structure and variation; Example of a probability distribution; Other argument styles; 1.3 Scientific argument; Ingredients of statistical argument; Intellectual foundation; Structure; Test statistic; What is a statistical hypothesis?; Summary; 2 Programming and statistical concepts; 2.1 Computer programming; History; The two parts of a computer program. | |
| 505 | 8 | |a PlacesService berry example; Instructions; Leading spaces; Spreadsheet I/O; Procedures; Errors; 2.2 You start programming; Experienced programmers; Getting started with EXCEL macro programming; How to read and write a spreadsheet from your macro; 2.3 Completing the service berry example; Fruit-ripening phenology; Mechanisms of variation in fruit-ripening date; The data; Hypothesis and statistic; A macro to calculate the predicted probability distribution; Calculate the test statistic; Remember the four ingredients; Name vs content; 2.4 Sub CARPEL; 2.5 You practice. | |
| 505 | 8 | |a More about the EXCEL macro editorA real exercise problem; How to solve it; Remember lawyers; 3 Choosing a test statistic; 3.1 Significance of what; Data from fossil marine organisms; The controversy; Relevance of precision; Two irrelevant statistics; Relevant statistics; Freedom to choose any statistic; 3.2 Implement the program; Hypotheses of non-periodicity; Computational overview; Sample the chosen hypothesis with computation; Calculate a relevant statistic; Discover inter-peak intervals; Testing the macro; Estimate realized significance; Using significance to argue; 3.3 Sub PERIOD. | |
| 505 | 8 | |a 4 Random variables and distributions4.1 Random variables; At random; Random process; Continuous distributions; Random variable; 4.2 Distributions; Computation eliminates calculus; Bar graph; Practice writing a macro; Interpret the bar graph; Randomize; Accuracy vs precision; Pseudo-random; 4.3 Arithmetic with random variables; Hypotheses make statistics into random variables; Arithmetic with a random variable and numbers; A macro to convert u to another continuous uniform distribution; Sum of independent samples of the same binary random variable; Pascal's triangle; A macro to estimate s3. | |
| 505 | 8 | |a Macros to estimate other density distributions4.4 Expected value and variance; The middle of a distribution; Theoretical properties of expected value; Variance; Variance of the sum, f + g; The variance of u; 5 More programming and statistical concepts; 5.1 Re-sampling data; A question; Choose a test statistic; Design the macro; Not different mean same random process; Re-sampling data; Overview; Style; Efron; 5.2 Procedures; Why write procedures?; How to write a procedure; Access to places; Sub SORT; BIGDIF3; 5.3 Testing procedures; Testing SORT; Test data; Infinite loop; The watch window. | |
| 500 | |a Testing PERMUTE. | ||
| 520 | |a Teaches powerful methods to test hypotheses using statistical arguments without the constraints and sophisticated mathematics of classical statistics. | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references (pages 253-255) and index. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Ecology |x Statistical methods |x Data processing. | |
| 650 | 0 | |a Ecology |x Statistical methods. | |
| 650 | 0 | |a Ecology. |0 http://id.loc.gov/authorities/subjects/sh85040752 | |
| 650 | 0 | |a Evolution (Biology) |x Statistical methods |x Data processing. | |
| 650 | 0 | |a Evolution (Biology) |x Statistical methods. | |
| 650 | 0 | |a Evolution. |0 http://id.loc.gov/authorities/subjects/sh85046029 | |
| 650 | 6 | |a Évolution. | |
| 650 | 7 | |a NATURE |x Reference. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x Biology. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x General. |2 bisacsh | |
| 650 | 7 | |a Ecology |2 fast | |
| 650 | 7 | |a Ecology |x Statistical methods |2 fast | |
| 650 | 7 | |a Evolution |2 fast | |
| 650 | 7 | |a Evolution (Biology) |x Statistical methods |2 fast | |
| 758 | |i has work: |a A computational approach to statistical arguments in ecology and evolution (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH3rHKcFGgbjpwkRWCWCcP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Estabrook, George. |t A Computational Approach to Statistical Arguments in Ecology and Evolution. |d Cambridge : Cambridge University Press, ©2011 |z 9781107004306 |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocn763158001 |
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| adam_text | |
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| author | Estabrook, George F. |
| author_GND | http://id.loc.gov/authorities/names/nb2011027708 |
| author_facet | Estabrook, George F. |
| author_role | |
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| building | Verbundindex |
| bvnumber | localFWS |
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| callnumber-label | QH371 |
| callnumber-raw | QH371.3.S72 E88 2011 |
| callnumber-search | QH371.3.S72 E88 2011 |
| callnumber-sort | QH 3371.3 S72 E88 42011 |
| callnumber-subject | QH - Natural History and Biology |
| collection | ZDB-4-EBA |
| contents | Cover; Title; Copyright; Contents; Acknowledgments; 1 Introduction; 1.1 About the book; Purpose; Intended readers; Why use computation; Prerequisites; How to use this book; Brief overview; 1.2 Basic principles; Applicability; Argument style; Structure and variation; Example of a probability distribution; Other argument styles; 1.3 Scientific argument; Ingredients of statistical argument; Intellectual foundation; Structure; Test statistic; What is a statistical hypothesis?; Summary; 2 Programming and statistical concepts; 2.1 Computer programming; History; The two parts of a computer program. PlacesService berry example; Instructions; Leading spaces; Spreadsheet I/O; Procedures; Errors; 2.2 You start programming; Experienced programmers; Getting started with EXCEL macro programming; How to read and write a spreadsheet from your macro; 2.3 Completing the service berry example; Fruit-ripening phenology; Mechanisms of variation in fruit-ripening date; The data; Hypothesis and statistic; A macro to calculate the predicted probability distribution; Calculate the test statistic; Remember the four ingredients; Name vs content; 2.4 Sub CARPEL; 2.5 You practice. More about the EXCEL macro editorA real exercise problem; How to solve it; Remember lawyers; 3 Choosing a test statistic; 3.1 Significance of what; Data from fossil marine organisms; The controversy; Relevance of precision; Two irrelevant statistics; Relevant statistics; Freedom to choose any statistic; 3.2 Implement the program; Hypotheses of non-periodicity; Computational overview; Sample the chosen hypothesis with computation; Calculate a relevant statistic; Discover inter-peak intervals; Testing the macro; Estimate realized significance; Using significance to argue; 3.3 Sub PERIOD. 4 Random variables and distributions4.1 Random variables; At random; Random process; Continuous distributions; Random variable; 4.2 Distributions; Computation eliminates calculus; Bar graph; Practice writing a macro; Interpret the bar graph; Randomize; Accuracy vs precision; Pseudo-random; 4.3 Arithmetic with random variables; Hypotheses make statistics into random variables; Arithmetic with a random variable and numbers; A macro to convert u to another continuous uniform distribution; Sum of independent samples of the same binary random variable; Pascal's triangle; A macro to estimate s3. Macros to estimate other density distributions4.4 Expected value and variance; The middle of a distribution; Theoretical properties of expected value; Variance; Variance of the sum, f + g; The variance of u; 5 More programming and statistical concepts; 5.1 Re-sampling data; A question; Choose a test statistic; Design the macro; Not different mean same random process; Re-sampling data; Overview; Style; Efron; 5.2 Procedures; Why write procedures?; How to write a procedure; Access to places; Sub SORT; BIGDIF3; 5.3 Testing procedures; Testing SORT; Test data; Infinite loop; The watch window. |
| ctrlnum | (OCoLC)763158001 |
| dewey-full | 576.8015195 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 576 - Genetics and evolution |
| dewey-raw | 576.8015195 |
| dewey-search | 576.8015195 |
| dewey-sort | 3576.8015195 |
| dewey-tens | 570 - Biology |
| discipline | Biologie |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:18:07Z |
| institution | BVB |
| isbn | 9781139117562 1139117564 9781139128223 1139128221 9781139115391 1139115391 9780511783708 0511783701 9780521179232 0521179238 1107220963 9781107220966 1139124803 9781139124805 1283314967 9781283314961 1139123319 9781139123310 9786613314963 661331496X 1139113208 9781139113205 |
| language | English |
| oclc_num | 763158001 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (268 pages) |
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| publishDate | 2011 |
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| spelling | Estabrook, George F. http://id.loc.gov/authorities/names/nb2011027708 A Computational Approach to Statistical Arguments in Ecology and Evolution. Cambridge : Cambridge University Press, 2011. 1 online resource (268 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Cover; Title; Copyright; Contents; Acknowledgments; 1 Introduction; 1.1 About the book; Purpose; Intended readers; Why use computation; Prerequisites; How to use this book; Brief overview; 1.2 Basic principles; Applicability; Argument style; Structure and variation; Example of a probability distribution; Other argument styles; 1.3 Scientific argument; Ingredients of statistical argument; Intellectual foundation; Structure; Test statistic; What is a statistical hypothesis?; Summary; 2 Programming and statistical concepts; 2.1 Computer programming; History; The two parts of a computer program. PlacesService berry example; Instructions; Leading spaces; Spreadsheet I/O; Procedures; Errors; 2.2 You start programming; Experienced programmers; Getting started with EXCEL macro programming; How to read and write a spreadsheet from your macro; 2.3 Completing the service berry example; Fruit-ripening phenology; Mechanisms of variation in fruit-ripening date; The data; Hypothesis and statistic; A macro to calculate the predicted probability distribution; Calculate the test statistic; Remember the four ingredients; Name vs content; 2.4 Sub CARPEL; 2.5 You practice. More about the EXCEL macro editorA real exercise problem; How to solve it; Remember lawyers; 3 Choosing a test statistic; 3.1 Significance of what; Data from fossil marine organisms; The controversy; Relevance of precision; Two irrelevant statistics; Relevant statistics; Freedom to choose any statistic; 3.2 Implement the program; Hypotheses of non-periodicity; Computational overview; Sample the chosen hypothesis with computation; Calculate a relevant statistic; Discover inter-peak intervals; Testing the macro; Estimate realized significance; Using significance to argue; 3.3 Sub PERIOD. 4 Random variables and distributions4.1 Random variables; At random; Random process; Continuous distributions; Random variable; 4.2 Distributions; Computation eliminates calculus; Bar graph; Practice writing a macro; Interpret the bar graph; Randomize; Accuracy vs precision; Pseudo-random; 4.3 Arithmetic with random variables; Hypotheses make statistics into random variables; Arithmetic with a random variable and numbers; A macro to convert u to another continuous uniform distribution; Sum of independent samples of the same binary random variable; Pascal's triangle; A macro to estimate s3. Macros to estimate other density distributions4.4 Expected value and variance; The middle of a distribution; Theoretical properties of expected value; Variance; Variance of the sum, f + g; The variance of u; 5 More programming and statistical concepts; 5.1 Re-sampling data; A question; Choose a test statistic; Design the macro; Not different mean same random process; Re-sampling data; Overview; Style; Efron; 5.2 Procedures; Why write procedures?; How to write a procedure; Access to places; Sub SORT; BIGDIF3; 5.3 Testing procedures; Testing SORT; Test data; Infinite loop; The watch window. Testing PERMUTE. Teaches powerful methods to test hypotheses using statistical arguments without the constraints and sophisticated mathematics of classical statistics. Print version record. Includes bibliographical references (pages 253-255) and index. English. Ecology Statistical methods Data processing. Ecology Statistical methods. Ecology. http://id.loc.gov/authorities/subjects/sh85040752 Evolution (Biology) Statistical methods Data processing. Evolution (Biology) Statistical methods. Evolution. http://id.loc.gov/authorities/subjects/sh85046029 Évolution. NATURE Reference. bisacsh SCIENCE Life Sciences Biology. bisacsh SCIENCE Life Sciences General. bisacsh Ecology fast Ecology Statistical methods fast Evolution fast Evolution (Biology) Statistical methods fast has work: A computational approach to statistical arguments in ecology and evolution (Text) https://id.oclc.org/worldcat/entity/E39PCH3rHKcFGgbjpwkRWCWCcP https://id.oclc.org/worldcat/ontology/hasWork Print version: Estabrook, George. A Computational Approach to Statistical Arguments in Ecology and Evolution. Cambridge : Cambridge University Press, ©2011 9781107004306 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=400628 Volltext |
| spellingShingle | Estabrook, George F. A Computational Approach to Statistical Arguments in Ecology and Evolution. Cover; Title; Copyright; Contents; Acknowledgments; 1 Introduction; 1.1 About the book; Purpose; Intended readers; Why use computation; Prerequisites; How to use this book; Brief overview; 1.2 Basic principles; Applicability; Argument style; Structure and variation; Example of a probability distribution; Other argument styles; 1.3 Scientific argument; Ingredients of statistical argument; Intellectual foundation; Structure; Test statistic; What is a statistical hypothesis?; Summary; 2 Programming and statistical concepts; 2.1 Computer programming; History; The two parts of a computer program. PlacesService berry example; Instructions; Leading spaces; Spreadsheet I/O; Procedures; Errors; 2.2 You start programming; Experienced programmers; Getting started with EXCEL macro programming; How to read and write a spreadsheet from your macro; 2.3 Completing the service berry example; Fruit-ripening phenology; Mechanisms of variation in fruit-ripening date; The data; Hypothesis and statistic; A macro to calculate the predicted probability distribution; Calculate the test statistic; Remember the four ingredients; Name vs content; 2.4 Sub CARPEL; 2.5 You practice. More about the EXCEL macro editorA real exercise problem; How to solve it; Remember lawyers; 3 Choosing a test statistic; 3.1 Significance of what; Data from fossil marine organisms; The controversy; Relevance of precision; Two irrelevant statistics; Relevant statistics; Freedom to choose any statistic; 3.2 Implement the program; Hypotheses of non-periodicity; Computational overview; Sample the chosen hypothesis with computation; Calculate a relevant statistic; Discover inter-peak intervals; Testing the macro; Estimate realized significance; Using significance to argue; 3.3 Sub PERIOD. 4 Random variables and distributions4.1 Random variables; At random; Random process; Continuous distributions; Random variable; 4.2 Distributions; Computation eliminates calculus; Bar graph; Practice writing a macro; Interpret the bar graph; Randomize; Accuracy vs precision; Pseudo-random; 4.3 Arithmetic with random variables; Hypotheses make statistics into random variables; Arithmetic with a random variable and numbers; A macro to convert u to another continuous uniform distribution; Sum of independent samples of the same binary random variable; Pascal's triangle; A macro to estimate s3. Macros to estimate other density distributions4.4 Expected value and variance; The middle of a distribution; Theoretical properties of expected value; Variance; Variance of the sum, f + g; The variance of u; 5 More programming and statistical concepts; 5.1 Re-sampling data; A question; Choose a test statistic; Design the macro; Not different mean same random process; Re-sampling data; Overview; Style; Efron; 5.2 Procedures; Why write procedures?; How to write a procedure; Access to places; Sub SORT; BIGDIF3; 5.3 Testing procedures; Testing SORT; Test data; Infinite loop; The watch window. Ecology Statistical methods Data processing. Ecology Statistical methods. Ecology. http://id.loc.gov/authorities/subjects/sh85040752 Evolution (Biology) Statistical methods Data processing. Evolution (Biology) Statistical methods. Evolution. http://id.loc.gov/authorities/subjects/sh85046029 Évolution. NATURE Reference. bisacsh SCIENCE Life Sciences Biology. bisacsh SCIENCE Life Sciences General. bisacsh Ecology fast Ecology Statistical methods fast Evolution fast Evolution (Biology) Statistical methods fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85040752 http://id.loc.gov/authorities/subjects/sh85046029 |
| title | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_auth | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_exact_search | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_full | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_fullStr | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_full_unstemmed | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_short | A Computational Approach to Statistical Arguments in Ecology and Evolution. |
| title_sort | computational approach to statistical arguments in ecology and evolution |
| topic | Ecology Statistical methods Data processing. Ecology Statistical methods. Ecology. http://id.loc.gov/authorities/subjects/sh85040752 Evolution (Biology) Statistical methods Data processing. Evolution (Biology) Statistical methods. Evolution. http://id.loc.gov/authorities/subjects/sh85046029 Évolution. NATURE Reference. bisacsh SCIENCE Life Sciences Biology. bisacsh SCIENCE Life Sciences General. bisacsh Ecology fast Ecology Statistical methods fast Evolution fast Evolution (Biology) Statistical methods fast |
| topic_facet | Ecology Statistical methods Data processing. Ecology Statistical methods. Ecology. Evolution (Biology) Statistical methods Data processing. Evolution (Biology) Statistical methods. Evolution. Évolution. NATURE Reference. SCIENCE Life Sciences Biology. SCIENCE Life Sciences General. Ecology Ecology Statistical methods Evolution Evolution (Biology) Statistical methods |
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