dc.contributor.author |
Dalpiaz, David |
|
dc.date.accessioned |
2023-07-01T01:50:21Z |
|
dc.date.available |
2023-07-01T01:50:21Z |
|
dc.date.issued |
2021 |
|
dc.identifier.uri |
${sadil.baseUrl}/handle/123456789/3733 |
|
dc.description |
459 p. (PDF) |
sm |
dc.description.abstract |
This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.
It introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world's tricky problems faced by the "R Team" characters
The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions.
Complete an introductory course in statistics
Prepare for more advanced statistical courses
Gain the transferable analytical skills needed to interpret research from across the social sciences
Learn the technical skills needed to present data visually
Acquire a basic competence in the use of R. |
sm |
dc.language.iso |
en |
sm |
dc.publisher |
Github |
sm |
dc.subject |
Introduction to R |
sm |
dc.subject |
Data and programming |
sm |
dc.subject |
Summarizing data |
sm |
dc.subject |
Probability and statistics in R |
sm |
dc.subject |
Resources |
sm |
dc.subject |
Simple linear regression |
sm |
dc.subject |
Interferrence for simple linear regression |
sm |
dc.subject |
Multiple linear regression |
sm |
dc.subject |
Model building |
sm |
dc.subject |
Categorical predictors and interactions |
sm |
dc.subject |
Analysis of variance |
sm |
dc.subject |
Model diagnostics |
sm |
dc.subject |
Transformations |
sm |
dc.subject |
Collinearity |
sm |
dc.subject |
Variable selection and model building |
sm |
dc.title |
Applied statistics with R |
sm |
dc.type |
Book |
sm |