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This book is designed to help you gain confidence in analysing and interpreting quantitative data and using appropriate statistical tests, by exploring, in plain language, a variety of data analysis methods. Highly practical, each chapter includes step-by-step instructions on how to run specific statistical tests using R, practical tips on how to interpret results correctly and exercises to put into practice what you have learned. It also includes guidance on how to use R and RStudio, how to visualise quantitative data, and the fundamentals of inferential statistics, estimations and hypothesis testing.…mehr
This book is designed to help you gain confidence in analysing and interpreting quantitative data and using appropriate statistical tests, by exploring, in plain language, a variety of data analysis methods.
Highly practical, each chapter includes step-by-step instructions on how to run specific statistical tests using R, practical tips on how to interpret results correctly and exercises to put into practice what you have learned.
It also includes guidance on how to use R and RStudio, how to visualise quantitative data, and the fundamentals of inferential statistics, estimations and hypothesis testing.
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Autorenporträt
Dr Nicoleta Gaciu is Senior Lecturer in Education at Oxford Brookes University, UK. Her academic and research specialisations in disciplines such as physics, statistics, computer sciences, research methods and business have given her the best opportunities to make connections across disciplines, to view real-life phenomena through different lenses and to take different perspectives, knowledge, logical and methodological approaches for interdisciplinary research.
Inhaltsangabe
Part 1: Understanding quantitative data and R 1. Introduction to information, knowledge and quantitative data 2. An introduction to R and RStudio Part 2: Data visualisation 3. Graphical representation of data Part 3: Providing information about data 4. Descriptive statistics 5. Measures of dispersion and distributions 6. Normal distribution and standardised scores Part 4: Making estimations and predictions from the data 7. Fundamentals of inferential statistics 8. Estimations and hypothesis testing Part 5: From sample to population 9. One-sample tests 10. Differences between the independent or dependent two samples 11. Difference between more than two independent samples 12. Difference between more than two dependent samples Part 6: Relationships and predictions 13. Relationship between variables 14. Predictions for independent and dependent variables
Part 1: Understanding quantitative data and R 1. Introduction to information, knowledge and quantitative data 2. An introduction to R and RStudio Part 2: Data visualisation 3. Graphical representation of data Part 3: Providing information about data 4. Descriptive statistics 5. Measures of dispersion and distributions 6. Normal distribution and standardised scores Part 4: Making estimations and predictions from the data 7. Fundamentals of inferential statistics 8. Estimations and hypothesis testing Part 5: From sample to population 9. One-sample tests 10. Differences between the independent or dependent two samples 11. Difference between more than two independent samples 12. Difference between more than two dependent samples Part 6: Relationships and predictions 13. Relationship between variables 14. Predictions for independent and dependent variables
Part 1: Understanding quantitative data and R 1. Introduction to information, knowledge and quantitative data 2. An introduction to R and RStudio Part 2: Data visualisation 3. Graphical representation of data Part 3: Providing information about data 4. Descriptive statistics 5. Measures of dispersion and distributions 6. Normal distribution and standardised scores Part 4: Making estimations and predictions from the data 7. Fundamentals of inferential statistics 8. Estimations and hypothesis testing Part 5: From sample to population 9. One-sample tests 10. Differences between the independent or dependent two samples 11. Difference between more than two independent samples 12. Difference between more than two dependent samples Part 6: Relationships and predictions 13. Relationship between variables 14. Predictions for independent and dependent variables
Part 1: Understanding quantitative data and R 1. Introduction to information, knowledge and quantitative data 2. An introduction to R and RStudio Part 2: Data visualisation 3. Graphical representation of data Part 3: Providing information about data 4. Descriptive statistics 5. Measures of dispersion and distributions 6. Normal distribution and standardised scores Part 4: Making estimations and predictions from the data 7. Fundamentals of inferential statistics 8. Estimations and hypothesis testing Part 5: From sample to population 9. One-sample tests 10. Differences between the independent or dependent two samples 11. Difference between more than two independent samples 12. Difference between more than two dependent samples Part 6: Relationships and predictions 13. Relationship between variables 14. Predictions for independent and dependent variables
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