000			04330nam a2200409 4500
001			OTLid0000552
003			MnU
005			20201105133339.0
006			m o d s
008			180907s2019 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	_aAdvanced High School Statistics _cDavid Diez
250			_a2nd Edition
264		2	_bOpen Textbook Library
264		1	_bOpenIntro
300			_a1 online resource
490	0		_aOpen textbook library.
505	0		_a1 Data collection -- 1.1 Case study -- 1.2 Data basics -- 1.3 Overview of data collection principles -- 1.4 Observational studies and sampling strategies -- 1.5 Experiments -- 2 Summarizing data -- 2.1 Examining numerical data -- 2.2 Numerical summaries and box plots -- 2.3 Considering categorical data -- 2.4 Case study: malaria vaccine (special topic) -- 3 Probability -- 3.1 Defining probability -- 3.2 Conditional probability -- 3.3 The binomial formula -- 3.4 Simulations -- 3.5 Random variables -- 3.6 Continuous distributions -- 4 Distributions of random variables -- 4.1 Normal distribution -- 4.2 Sampling distribution of a sample mean -- 4.3 Geometric distribution -- 4.4 Binomial distribution -- 4.5 Sampling distribution of a sample proportion -- 5 Foundation for inference -- 5.1 Estimating unknown parameters -- 5.2 Confidence intervals -- 5.3 Introducing hypothesis testing -- 5.4 Does it make sense? -- 6 Inference for categorical data -- 6.1 Inference for a single proportion -- 6.2 Difference of two proportions -- 6.3 Testing for goodness of fit using chi-square -- 6.4 Homogeneity and independence in two-way tables -- 7 Inference for numerical data -- 7.1 Inference for a mean with the t-distribution -- 7.2 Inference for paired data -- 7.3 Inference for the difference of two means -- 8 Introduction to linear regression -- 8.1 Line fitting, residuals, and correlation -- 8.2 Fitting a line by least squares regression -- 8.3 Inference for the slope of a regression line -- 8.4 Transformations for skewed data -- A Exercise solutions -- B Distribution tables -- C Distribution Tables -- D Calculator reference, Formulas, and Inference guide
520	0		_aWe hope readers will take away three ideas from this book in addition to forming a foundationof 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 real, interesting data. (3) Data are messy, and statistical tools are imperfect. But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the real world. Textbook overviewThe chapters of this book are as follows: 1. Data collection. Data structures, variables, and basic data collection techniques. 2. Summarizing data. Data summaries and graphics. 3. Probability. The basic principles of probability. 4. Distributions of random variables. Introduction to key distributions, and how the normal model applies to the sample mean and sample proportion. 5. Foundation for inference. General ideas for statistical inference in the context of estimating the population proportion. 6. Inference for categorical data. Inference for proportions using the normal and chisquare distributions. 7. Inference for numerical data. Inference for one or two sample means using the t distribution, and comparisons of many means using ANOVA. 8. Introduction to linear regression. An introduction to regression with two variables. Instructions are also provided in several sections for using Casio and TI calculators.
542	1		_fAttribution-ShareAlike
546			_aIn English.
588	0		_aDescription based on print resource
650		0	_aMathematics _vTextbooks
650		0	_aApplied mathematics _vTextbooks
700	1		_aBarr, Christopher _eauthor
700	1		_aÇetinkaya-Rundel, Mine _eauthor
700	1		_aDorazio, Leah _eauthor
710	2		_aOpen Textbook Library _edistributor
856	4	0	_uhttps://open.umn.edu/opentextbooks/textbooks/552 _zAccess online version
999			_c19931 _d19931