| 000 | 02619nam a22002177a 4500 | ||
|---|---|---|---|
| 003 | CUTN | ||
| 005 | 20231218122827.0 | ||
| 008 | 231218b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9781526413826 | ||
| 020 | _a9781526413819 | ||
| 041 | _aEnglish | ||
| 082 |
_223 _a519.5 _bGOR |
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| 100 | _aGorard, Stephen. | ||
| 245 |
_aHow to make sense of statistics / _cStephen Gorard. |
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| 260 |
_aLondon : _bSage ; _c2021. |
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| 300 |
_axxii, 289 p. ; _bill. pbk: _c24 cm. |
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| 505 | _a Part I: IntroductionChapter 1: Why we use numbers in researchChapter 2: What is a number?: Issues of measurementPart II: Basic analysesChapter 3: Working with one variableChapter 4: Working with tables of categorical variablesChapter 5: Examining differences between real numbersChapter 6: Significance tests: how to conduct them and what they do not meanChapter 7: Significance tests: why we should not report themPart III: Advanced issues for analysisChapter 8: The role of judgement in analysisChapter 9: Research designsChapter 10: Sampling and populationsChapter 11: What is randomness?Chapter 12: Handling missing data: The importance of what we don’t know chapter 13: Handling missing data: more complex issues Part IV: Modelling with Data Chapter 14: Errors in measurements 15: Correlating two real numbers chapter 16: Predicting measurements using simple linear regression chapter 17: Predicting measurements using multiple linear regression chapter 18: Assumptions and limitations in regression chapter 19: Predicting outcomes using logistic regression chapter 20: Data reduction techniques part V: ConclusionChapter 21: Presenting data for your audience | ||
| 520 | _aIn a new textbook designed for students new to statistics and social data, Stephen Gorard focuses on non-inferential statistics as a basis to ensure students have basic statistical literacy. Understanding why we have to learn statistics and seeing the links between the numbers and real life is a crucial starting point. Using engaging, friendly, approachable language this book will demystify numbers from the outset, explaining exactly how they can be used as tools to understand the relationships between variables. This text assumes no previous mathematical or statistical knowledge, taking the reader through each basic technique with step-by-step advice, worked examples, and exercises. Using non-inferential techniques, students learn the foundations that underpin all statistical analysis and will learn from the ground up how to produce theoretically and empirically informed statistical results | ||
| 650 | _aStatistics | ||
| 942 |
_2ddc _cBOOKS |
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| 999 |
_c41007 _d41007 |
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