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Introductory Statistics for the Life and Biomedical Sciences

Parametre

  • 472 stránok
  • 17 hodin čítania

Viac o knihe

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.

Nákup knihy

Introductory Statistics for the Life and Biomedical Sciences, David Harrington, Julie Vu

Jazyk
Rok vydania
2020
Väzba
(mäkká)
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Titul
Introductory Statistics for the Life and Biomedical Sciences
Jazyk
anglicky
Vydavateľ
OpenIntro, Inc.
Rok vydania
2020
Väzba
mäkká
Počet strán
472
ISBN10
1943450129
ISBN13
9781943450121
Série
Štítky
Anotácia
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.