東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Introductory applied statistics = wi...

Blaine, Bruce.

FindBook

Google Book

Amazon

博客來

Introductory applied statistics = with resampling methods & R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introductory applied statistics/ by Bruce Blaine.
其他題名:	with resampling methods & R /
作者:	Blaine, Bruce.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	1 online resource (xiv, 190 p.) :ill., digital ;24 cm.
內容註:	1. Foundations I: Introductory Data Analysis with R -- 2. Data Analysis in Bivariate Data: Foundations -- 3. Statistics and Data Analysis in an ANOVA Model -- 4. Statistics and Data Analysis in a Proportions Model -- 5. Statistics and Data Analysis in a Regression Model -- 6. Statistics and Data Analysis in a Logistic Model -- 7. Statistical Inference I: Randomization Methods for Hypothesis Testing -- 8. Statistical Inference II: Bootstrapping Methods for Parameter Estimation -- 9. Using Resampling Methods for Statistical Inference: Four Examples -- 10. Statistics and Data Analysis in a Pre-Post Design.
Contained By:	Springer Nature eBook
標題:	Statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-27741-2
ISBN:	9783031277412

Introductory applied statistics = with resampling methods & R /
Blaine, Bruce.

Introductory applied statisticswith resampling methods & R /[electronic resource] :by Bruce Blaine. - Cham :Springer International Publishing :2023. - 1 online resource (xiv, 190 p.) :ill., digital ;24 cm.

1. Foundations I: Introductory Data Analysis with R -- 2. Data Analysis in Bivariate Data: Foundations -- 3. Statistics and Data Analysis in an ANOVA Model -- 4. Statistics and Data Analysis in a Proportions Model -- 5. Statistics and Data Analysis in a Regression Model -- 6. Statistics and Data Analysis in a Logistic Model -- 7. Statistical Inference I: Randomization Methods for Hypothesis Testing -- 8. Statistical Inference II: Bootstrapping Methods for Parameter Estimation -- 9. Using Resampling Methods for Statistical Inference: Four Examples -- 10. Statistics and Data Analysis in a Pre-Post Design.

This book offers an introduction to applied statistics through data analysis, integrating statistical computing methods. It covers robust and non-robust descriptive statistics used in each of four bivariate statistical models that are commonly used in research: ANOVA, proportions, regression, and logistic. The text teaches statistical inference principles using resampling methods (such as randomization and bootstrapping), covering methods for hypothesis testing and parameter estimation. These methods are applied to each statistical model introduced in preceding chapters. Data analytic examples are used to teach statistical concepts throughout, and students are introduced to the R packages and functions required for basic data analysis in each of the four models. The text also includes introductory guidance to the fundamentals of data wrangling, as well as examples of write-ups so that students can learn how to communicate findings. Each chapter includes problems for practice or assessment. Supplemental instructional videos are also available as an additional aid to instructors, or as a general resource to students. This book is intended for an introductory or basic statistics course with an applied focus, or an introductory analytics course, at the undergraduate level in a two-year or four-year institution. This can be used for students with a variety of disciplinary backgrounds, from business, to the social sciences, to medicine. No sophisticated mathematical background is required.

ISBN: 9783031277412

Standard No.: 10.1007/978-3-031-27741-2doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Introductory applied statistics = with resampling methods & R /
LDR:03109nmm a2200325 a 4500 001 2318962
003 DE-He213
005 20230505182902.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031277412 $q (electronic bk.)
020 $a 9783031277405 $q (paper)
024 7 $a 10.1007/978-3-031-27741-2 $2 doi
035 $a 978-3-031-27741-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a UN $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .B634 2023
100 1 $a Blaine, Bruce. $3 3634466
245 1 0 $a Introductory applied statistics $h [electronic resource] : $b with resampling methods & R / $c by Bruce Blaine.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a 1 online resource (xiv, 190 p.) : $b ill., digital ; $c 24 cm.
505 0 $a 1. Foundations I: Introductory Data Analysis with R -- 2. Data Analysis in Bivariate Data: Foundations -- 3. Statistics and Data Analysis in an ANOVA Model -- 4. Statistics and Data Analysis in a Proportions Model -- 5. Statistics and Data Analysis in a Regression Model -- 6. Statistics and Data Analysis in a Logistic Model -- 7. Statistical Inference I: Randomization Methods for Hypothesis Testing -- 8. Statistical Inference II: Bootstrapping Methods for Parameter Estimation -- 9. Using Resampling Methods for Statistical Inference: Four Examples -- 10. Statistics and Data Analysis in a Pre-Post Design.
520 $a This book offers an introduction to applied statistics through data analysis, integrating statistical computing methods. It covers robust and non-robust descriptive statistics used in each of four bivariate statistical models that are commonly used in research: ANOVA, proportions, regression, and logistic. The text teaches statistical inference principles using resampling methods (such as randomization and bootstrapping), covering methods for hypothesis testing and parameter estimation. These methods are applied to each statistical model introduced in preceding chapters. Data analytic examples are used to teach statistical concepts throughout, and students are introduced to the R packages and functions required for basic data analysis in each of the four models. The text also includes introductory guidance to the fundamentals of data wrangling, as well as examples of write-ups so that students can learn how to communicate findings. Each chapter includes problems for practice or assessment. Supplemental instructional videos are also available as an additional aid to instructors, or as a general resource to students. This book is intended for an introductory or basic statistics course with an applied focus, or an introductory analytics course, at the undergraduate level in a two-year or four-year institution. This can be used for students with a variety of disciplinary backgrounds, from business, to the social sciences, to medicine. No sophisticated mathematical background is required.
650 0 $a Statistics. $3 517247
650 0 $a Statistics $x Data processing. $3 535534
650 0 $a Resampling (Statistics) $3 647644
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Applied Statistics. $3 3300946
650 2 4 $a Statistics and Computing. $3 3594429
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-27741-2
950 $a Mathematics and Statistics (SpringerNature-11649)