000			03505nam a2200397 4500
001			OTLid0000549
003			MnU
005			20201105133339.0
006			m o d s
008			180907s2014 mnu o 0 0 eng d
040			_aMnU _beng _cMnU
050		4	_aQA1
050		4	_aQA37.3
100	1		_aDiez, David _eauthor
245	0	0	_aIntroductory Statistics with Randomization and Simulation _cDavid Diez
250			_aFirst Edition
264		2	_bOpen Textbook Library
264		1	_bOpenIntro
300			_a1 online resource
490	0		_aOpen textbook library.
505	0		_a1. Introduction to data. -- 2. Foundations for inference. -- 3. Inference for categorical data. -- 4. Inference for numerical data. -- 5. Introduction to linear regression. -- 6. Multiple and logistic regression. -- Appendix A. Probability.
520	0		_aWe hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from interesting, real data. (3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world. Textbook overview The chapters of this book are as follows: 1. Introduction to data. Data structures, variables, summaries, graphics, and basic data collection techniques. 2. Foundations for inference. Case studies are used to introduce the ideas of statistical inference with randomization and simulations. The content leads into the standard parametric framework, with techniques reinforced in the subsequent chapters.1 It is also possible to begin with this chapter and introduce tools from Chapter 1 as they are needed. 3. Inference for categorical data. Inference for proportions using the normal and chi-square distributions, as well as simulation and randomization techniques. 4. Inference for numerical data. Inference for one or two sample means using the t distribution, and also comparisons of many means using ANOVA. A special section for bootstrapping is provided at the end of the chapter. 5. Introduction to linear regression. An introduction to regression with two variables. Most of this chapter could be covered immediately after Chapter 1. 6. Multiple and logistic regression. An introduction to multiple regression and logistic regression for an accelerated course. Appendix A. Probability. An introduction to probability is provided as an optional reference. Exercises and additional probability content may be found in Chapter 2 of OpenIntro Statistics at openintro.org. Instructor feedback suggests that probability, if discussed, is best introduced at the very start or very end of the course.
542	1		_fAttribution-NonCommercial-ShareAlike
546			_aIn English.
588	0		_aDescription based on online resource
650		0	_aMathematics _vTextbooks
650		0	_aApplied mathematics _vTextbooks
700	1		_aBarr, Christopher _eauthor
700	1		_aÇetinkaya-Rundel, Mine _eauthor
710	2		_aOpen Textbook Library _edistributor
856	4	0	_uhttps://open.umn.edu/opentextbooks/textbooks/549 _zAccess online version
999			_c19929 _d19929