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E-grāmata: Basic Statistics with R: Reaching Decisions with Data

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(Analyst, Research & Development, Atlanta Braves Baseball Club)
  • Formāts: PDF+DRM
  • Izdošanas datums: 20-Feb-2021
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780128209264
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Basic Statistics with R: Reaching Decisions with DataPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 20-Feb-2021
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780128209264
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Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area.

In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines.

  • Features real-data to give students an engaging practice to connect with their areas of interest
  • Evolves from basic problems that can be worked by hand to the elementary use of opensource R software
  • Offers a direct, clear approach highlighted by useful visuals and examples

Recenzijas

"This monograph presents on a total of 283 pages an introduction into the basic concepts of the statistical analysis software R and addresses to readers with no previous knowledge. There are 20 chapters and two appendices in the book which are organized into five principal parts.In the first part of the book, the author introduces in Chapter 1 the basic framework of statistical thinking like the steps of scientific process (generation of hypotheses, data collection and description, statistical inference, theory/decision making). A general overview of the software R is provided in Chapter 2. Aspects concerning data collection are described in part two of the book covering the Chapters 3 to 5. Theoretical concepts on data collection are discussed in Chapter 3 and the implementation using R is provided in Chapter 4 (subsetting data, random numbers and random samples) and Chapter 5 (libraries and loading data into R). Part three of the book is devoted to explorative and descriptive statistics and covers the Chapters 6 and 7. Chapter 6 presents the methods of parameters and statistics for qualitative and quantitative variables and their implementation in R is provided in Chapter 7. Parts four and five (Chapters 8 to 20) focus on statistical inference. After an introduction into the framework of probability (Chapter 8), sample distributions (Chapter 9), hypothesis testing (Chapter 10), central limit theorem (Chapter 11), interval estimates (Chapter 12), hypothesis testing (Chapter 13) and confidence intervals for single parameter (Chapter 14) as well as for two parameters (hypothesis testing in Chapter 15 and confidence intervals in Chapter 16) the transfer of the theoretical concepts in R is described in Chapter 17. Chapter 18 deals with inference for two quantitative variables and simple linear regression is presented in Chapter 19. The fifth part ends with an overview of advanced statistical methods in Chapter 20. The volume ends with an appendix containing the solutions to all self-learning questions and an appendix listing all R example data sets. In summary, the book under review is recommended to interested students with no prior knowledge. Each chapter is enriched with a large number of supportive exercises and control questions supporting self-learning activities." --zbMath/European Mathematical Society and the Heidelberg Academy of Sciences and Humanities

"A useful introduction for non-specialists starting a career which involves analysing data. Such readers can refine their knowledge as they become more experienced" --Owen Toller, The Mathematical Gazette

Biography xv
Preface xvii
Acknowledgments xix
Part I An introduction to statistics and R
1 What is statistics and why is it important?
1.1 Introduction
3(1)
1.2 So what is statistics?
4(2)
1.2.1 The process of statistics
4(1)
1.2.2 Hypothesis/questions
4(1)
1.2.3 Data collection
5(1)
1.2.4 Data description
5(1)
1.2.5 Statistical inference
5(1)
1.2.6 Theories/decisions
6(1)
1.3 Computation and statistics
6(1)
2 An introduction to R
2.1 Installation
7(1)
2.2 Classes of data
7(1)
2.3 Mathematical operations in R
8(1)
2.4 Variables
9(2)
2.5 Vectors
11(1)
2.6 Data frames
12(1)
2.7 Practice problems
13(1)
2.8 Conclusion
14(3)
Part II Collecting data and loading it into R
3 Data collection: methods and concerns
3.1 Introduction
17(1)
3.2 Components of data collection
17(1)
3.3 Observational studies
18(3)
3.3.1 Biases in survey sampling
19(2)
3.3.2 Practice problems
21(1)
3.4 Designed experiments
21(2)
3.4.1 Practice problems
23(1)
3.5 Observational studies and experiments: which to use?
23(2)
3.5.1 Practice problems
24(1)
3.6 Conclusion
25(2)
4 R tutorial: subsetting data, random numbers, and selecting a random sample
4.1 Introduction
27(1)
4.2 Subsetting vectors
27(2)
4.3 Subsetting data frames
29(2)
4.4 Random numbers in R
31(1)
4.5 Select a random sample
32(1)
4.6 Getting help in R
33(1)
4.7 Practice problems
33(2)
4.8 Conclusion
35(2)
5 R tutorial: libraries and loading data into R
5.1 Introduction
37(1)
5.2 Libraries in R
37(5)
5.3 Loading datasets stored in libraries
42(1)
5.4 Loading csv files into R
42(1)
5.5 Practice problems
43(1)
5.6 Conclusion
43(4)
Part III Exploring and describing data
6 Exploratory data analyses: describing our data
6.1 Introduction
47(1)
6.2 Parameters and statistics
47(1)
6.3 Parameters, statistics, and EDA for categorical variables
48(3)
6.3.1 Practice problems
50(1)
6.4 Parameters, statistics, and EDA for a single quantitative variable
51(6)
6.4.1 Statistics for the center of a variable
51(2)
6.4.2 Practice problems
53(1)
6.4.3 Statistics for the spread of a variable
54(2)
6.4.4 Practice problems
56(1)
6.5 Visual summaries for a single quantitative variables
57(2)
6.6 Identifying outliers
59(2)
6.6.1 Practice problems
61(1)
6.7 Exploring relationships between variables
61(1)
6.8 Exploring association between categorical predictor and quantitative response
62(3)
6.8.1 Practice problems
65(1)
6.9 Exploring association between two quantitative variables
65(7)
6.9.1 Practice problems
71(1)
6.10 Conclusion
72(1)
7 R tutorial: EDA in R
7.1 Introduction
73(1)
7.2 Frequency and contingency tables in R
73(1)
7.3 Numerical exploratory analyses in R
74(3)
7.3.1 Summaries for the center of a variable
74(1)
7.3.2 Summaries for the spread of a variable
75(1)
7.3.3 Summaries for the association between two quantitative variables
76(1)
7.4 Missing data
77(1)
7.5 Practice problems
78(1)
7.6 Graphical exploratory analyses in R
78(4)
7.6.1 Scatterplots
78(2)
7.6.2 Histograms
80(2)
7.7 Boxplots
82(2)
7.8 Practice problems
84(1)
7.9 Conclusion
85(4)
Part IV Mechanisms of inference
8 An incredibly brief introduction to probability
8.1 Introduction
89(1)
8.2 Random phenomena, probability, and the Law of Large Numbers
90(1)
8.3 What is the role of probability in inference?
91(1)
8.4 Calculating probability and the axioms of probability
92(2)
8.5 Random variables and probability distributions
94(1)
8.6 The binomial distribution
95(1)
8.7 The normal distribution
96(2)
8.8 Practice problems
98(1)
8.9 Conclusion
99(2)
9 Sampling distributions, or why exploratory analyses are not enough
9.1 Introduction
101(1)
9.2 Sampling distributions
101(4)
9.3 Properties of sampling distributions and the central limit theorem
105(2)
9.4 Practice problems
107(1)
9.5 Conclusion
107(2)
10 The idea behind testing hypotheses
10.1 Introduction
109(1)
10.2 A lady tasting tea
109(1)
10.3 Hypothesis testing
110(5)
10.3.1 What are we testing?
110(2)
10.3.2 How rare is our data?
112(1)
10.3.3 What is our level of doubt?
113(2)
10.4 Practice problems
115(1)
10.5 Conclusion
115(2)
11 Making hypothesis testing work with the central limit theorem
11.1 Introduction
117(1)
11.2 Recap of the normal distribution
117(1)
11.3 Getting probabilities from the normal distributions
118(1)
11.3.1 Practice problems
119(1)
11.4 Connecting data to p-values
119(6)
11.4.1 Practice problems
124(1)
11.5 Conclusion
125(2)
12 The idea of interval estimates
12.1 Introduction
127(1)
12.2 Point and interval estimates
127(1)
12.3 When intervals are "right"
128(1)
12.4 Confidence intervals
128(1)
12.5 Creating confidence intervals
129(3)
12.6 Interpreting confidence intervals
132(1)
12.7 Practice problems
133(1)
12.8 Conclusion
133(4)
Part V Statistical inference
13 Hypothesis tests for a single parameter
13.1 Introduction
137(1)
13.2 One-sample test for proportions
138(5)
13.2.1 State hypotheses
138(1)
13.2.2 Set significance level
138(1)
13.2.3 Collect and summarize data
139(1)
13.2.4 Calculate test statistic
139(1)
13.2.5 Calculate p-values
140(1)
13.2.6 Conclude
141(1)
13.2.7 Practice problems
142(1)
13.3 One-sample r-test for means
143(7)
13.3.1 State hypotheses
143(1)
13.3.2 Set significance level
144(1)
13.3.3 Collect and summarize data
144(1)
13.3.4 Calculate test statistic
144(1)
13.3.5 Calculate p-values
145(1)
13.3.6 A brief interlude: the t distribution
146(2)
13.3.7 Conclude
148(2)
13.3.8 Practice problems
150(1)
13.4 Conclusion
150(1)
14 Confidence intervals for a single parameter
14.1 Introduction
151(1)
14.2 Confidence interval for p
151(2)
14.2.1 Practice problems
153(1)
14.3 Confidence interval for jtt
153(3)
14.3.1 Practice problems
155(1)
14.4 Other uses of confidence intervals
156(6)
14.4.1 Confidence intervals for p and sample size calculations
156(3)
14.4.2 Practice problems
159(1)
14.4.3 Confidence intervals for \i and hypothesis testing
159(2)
14.4.4 Practice problems
161(1)
14.5 Conclusion
162(1)
15 Hypothesis tests for two parameters
15.1 Introduction
163(1)
15.2 Two-sample test for proportions
164(7)
15.2.1 State hypotheses
164(1)
15.2.2 Set significance level
165(1)
15.2.3 Collect and summarize data
165(1)
15.2.4 Calculate the test statistic
166(2)
15.2.5 Calculate p-values
168(1)
15.2.6 Conclude
169(1)
15.2.7 Practice problems
170(1)
15.3 Two-sample f-test for means
171(8)
15.3.1 State hypotheses
171(1)
15.3.2 Set significance level
172(1)
15.3.3 Collect and summarize data
172(1)
15.3.4 Calculate the test statistic
173(2)
15.3.5 Calculate p-values
175(2)
15.3.6 Conclusion
177(1)
15.3.7 Practice problems
178(1)
15.4 Paired t-test for means
179(6)
15.4.1 State hypotheses
180(1)
15.4.2 Set significance level
181(1)
15.4.3 Collect and summarize data
181(1)
15.4.4 Calculate the test statistic
182(1)
15.4.5 Calculating p-values
182(1)
15.4.6 Conclude
183(1)
15.4.7 Practice problems
184(1)
15.5 Conclusion
185(2)
16 Confidence intervals for two parameters
16.1 Introduction
187(1)
16.2 Confidence interval for p1 -- p2
187(4)
16.2.1 Practice problems
191(1)
16.3 Confidence interval for μ1 - μ2
191(6)
16.3.1 Equal variances
192(1)
16.3.2 Unequal variances
193(2)
16.3.3 Interpretation and example
195(1)
16.3.4 Practice problems
196(1)
16.4 Confidence intervals for μD
197(2)
16.4.1 Practice problems
198(1)
16.5 Confidence intervals for μ1-μ2 and hypothesis testing
199(2)
16.5.1 Practice problems
201(1)
16.6 Conclusion
201(2)
17 R tutorial: statistical inference in R
17.1 Introduction
203(1)
17.2 Choosing the right test
203(1)
17.3 Inference for proportions
204(5)
17.3.1 Inference for a single proportion
205(2)
17.3.2 Inference for two proportions
207(1)
17.3.3 Practice problems
208(1)
17.4 Inference for means
209(4)
17.4.1 Inference for a single mean
209(1)
17.4.2 Inference for two means
210(2)
17.4.3 Paired inference for means
212(1)
17.4.4 Practice problems
213(1)
17.5 Conclusion
213(2)
18 Inference for two quantitative variables
18.1 Introduction
215(1)
18.2 Test for correlations
216(6)
18.2.1 State hypotheses
216(1)
18.2.2 Set significance level
217(1)
18.2.3 Collect and summarize data
217(1)
18.2.4 Calculate the test statistic
217(1)
18.2.5 Calculate p-values
218(1)
18.2.6 Conclusion
219(2)
18.2.7 Practice problems
221(1)
18.3 Confidence intervals for correlations
222(1)
18.4 Test for correlations in R
223(1)
18.5 Confidence intervals for correlations
224(1)
18.6 Practice problems
224(1)
18.7 Conclusion
225(2)
19 Simple linear regression
19.1 Introduction
227(1)
19.2 Basic of lines
228(1)
19.3 The simple linear regression model
229(1)
19.4 Estimating the regression model
230(2)
19.5 Regression in R
232(2)
19.6 Practice problems
234(1)
19.7 Using regression to create predictions
234(1)
19.8 Practice problems
235(1)
19.9 The assumptions of regression
236(4)
19.9.1 Assumption 1: linearity
236(3)
19.9.2 Assumption 2: independence
239(1)
19.9.3 Assumption 3: zero mean
239(1)
19.9.4 Assumption 4: homoskedasticity
239(1)
19.10 Inference for regression
240(4)
19.10.1 State hypotheses
240(1)
19.10.2 Set significance level
241(1)
19.10.3 Collect and summarize data
241(1)
19.10.4 Calculate our test statistic
241(1)
19.10.5 Calculate p-values
242(1)
19.10.6 Conclusion
243(1)
19.10.7 Inference for regression in R
243(1)
19.11 How good is our regression?
244(3)
19.12 Practice problems
247(1)
19.13 Conclusion
247(2)
20 Statistics: the world beyond this book
20.1 Questions beyond the techniques of this book
249(3)
20.2 The answers statistics gives
252(2)
20.3 Where does this leave us?
254(17)
A Solutions to practice problems
B List of R datasets
References 271(6)
Index 277
Dr. Stephen Loftus is an Analyst in Research & Development for the Atlanta Braves. Prior to this, he held academic positions at Randolph-Macon College and Sweet Briar College. In his experience in academia and industry, Dr. Loftus has spent a great deal of time studying and developing Bayesian models for a variety of projects. These highly collaborative projects range from analysis in baseball to studies in numerical ecology. In developing these models, he found himself, on many occasions, needing to explain not only the decisions made in making these models, but also the rationale behind the Bayesian philosophy of statistics to individuals with diverse mathematical backgrounds.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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