Parametri
- 472pagine
- 17 ore di lettura
Maggiori informazioni sul libro
This text serves as a companion to a set of self-paced learning labs designed to help students apply statistical concepts using the R computing language. It emphasizes understanding key ideas like confidence intervals rather than the technicalities of data generation. This approach allows students focused on statistical concepts to avoid distractions from specific software details. Many students, often entering research with only one statistics course, benefit from a practical introduction to data analysis that includes a statistical computing language. In classroom settings, it’s effective for students to engage with labs after learning corresponding material, whether through self-study or instructor-led presentations. Each lab aligns with specific sections of the text, and traditional exercises at the end of each chapter do not require computing. Chapters 1-5 include such exercises, while more complex methods like multiple regression necessitate computing for practical experience. The lab exercises in later chapters are crucial for mastering the material. Accompanying each chapter are "Lab Notes," which serve as a detailed reference for R functions used in the labs, tailored for first-time users. These notes provide more comprehensive explanations than standard R documentation, covering topics like histograms, loops, and regression models.
Acquisto del libro
Introductory Statistics for the Life and Biomedical Sciences, David Harrington, Julie Vu
- Lingua
- Pubblicato
- 2020
- Rilegatura
- (In brossura)
Metodi di pagamento
Ancora nessuna valutazione.
- Titolo
- Introductory Statistics for the Life and Biomedical Sciences
- Lingua
- Inglese
- Autori
- David Harrington, Julie Vu
- Editore
- OpenIntro, Inc.
- Pubblicato
- 2020
- Formato
- In brossura
- Pagine
- 472
- ISBN10
- 1943450129
- ISBN13
- 9781943450121
- Serie
- Tag
- Descrizione
- This text serves as a companion to a set of self-paced learning labs designed to help students apply statistical concepts using the R computing language. It emphasizes understanding key ideas like confidence intervals rather than the technicalities of data generation. This approach allows students focused on statistical concepts to avoid distractions from specific software details. Many students, often entering research with only one statistics course, benefit from a practical introduction to data analysis that includes a statistical computing language. In classroom settings, it’s effective for students to engage with labs after learning corresponding material, whether through self-study or instructor-led presentations. Each lab aligns with specific sections of the text, and traditional exercises at the end of each chapter do not require computing. Chapters 1-5 include such exercises, while more complex methods like multiple regression necessitate computing for practical experience. The lab exercises in later chapters are crucial for mastering the material. Accompanying each chapter are "Lab Notes," which serve as a detailed reference for R functions used in the labs, tailored for first-time users. These notes provide more comprehensive explanations than standard R documentation, covering topics like histograms, loops, and regression models.