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Statistical Data Analysis Using R
Statistical Data Analysis Using R
Knygos.lt klubas Knygos.lt nariams
97,43 €
-30%
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139,19 €
  • Planuojame turėti už 175 d.
This book introduces statistical data analysis using R programming, covering tools like descriptive statistics, regression, ANOVA, and non-parametric tests. It covers essential statistical tools, including descriptive statistics, probability distributions, and hypothesis testing, with practical examples and solved exercises. It introduces both built-in library packages and manual coding solutions, offering flexibility and clarity for learners. Featuring numerous tables, diagrams, and hands-on p…

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This book introduces statistical data analysis using R programming, covering tools like descriptive statistics, regression, ANOVA, and non-parametric tests. It covers essential statistical tools, including descriptive statistics, probability distributions, and hypothesis testing, with practical examples and solved exercises. It introduces both built-in library packages and manual coding solutions, offering flexibility and clarity for learners. Featuring numerous tables, diagrams, and hands-on programming exercises, this book ensures ease of understanding and practical mastery of R for statistical analysis.

  • Comprehensive coverage of statistical tools, including descriptive statistics, regression, ANOVA, and non-parametric tests.
  • Includes dual programming approach, in-built library packages and manual coding solutions.
  • Focus on graphics and data visualisation for effective interpretation of results.
  • Practical R code examples and solved exercises for hands-on learning.

This book is for undergraduate and postgraduate students, researchers, and professionals in fields such as statistics, computer science, business analytics, public health, psychology, economics, and environmental science.

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This book introduces statistical data analysis using R programming, covering tools like descriptive statistics, regression, ANOVA, and non-parametric tests. It covers essential statistical tools, including descriptive statistics, probability distributions, and hypothesis testing, with practical examples and solved exercises. It introduces both built-in library packages and manual coding solutions, offering flexibility and clarity for learners. Featuring numerous tables, diagrams, and hands-on programming exercises, this book ensures ease of understanding and practical mastery of R for statistical analysis.

  • Comprehensive coverage of statistical tools, including descriptive statistics, regression, ANOVA, and non-parametric tests.
  • Includes dual programming approach, in-built library packages and manual coding solutions.
  • Focus on graphics and data visualisation for effective interpretation of results.
  • Practical R code examples and solved exercises for hands-on learning.

This book is for undergraduate and postgraduate students, researchers, and professionals in fields such as statistics, computer science, business analytics, public health, psychology, economics, and environmental science.

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