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E-raamat: Intuitive Introductory Statistics

  • Formaat: PDF+DRM
  • Sari: Springer Texts in Statistics
  • Ilmumisaeg: 09-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319560724
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  • Formaat: PDF+DRM
  • Sari: Springer Texts in Statistics
  • Ilmumisaeg: 09-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319560724
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This textbook is designed to give an engaging introduction to statistics and the art of data analysis. The unique scope includes, but also goes beyond, classical methodology associated with the normal distribution. What if the normal model is not valid for a particular data set? This cutting-edge approach provides the alternatives. It is an introduction to the world and possibilities of statistics that uses exercises, computer analyses, and simulations throughout the core lessons. These elementary statistical methods are intuitive. Counting and ranking features prominently in the text. Nonparametric methods, for instance, are often based on counts and ranks and are very easy to integrate into an introductory course.? The ease of computation with advanced calculators and statistical software, both of which factor into this text, allows important techniques to be introduced earlier in the study of statistics. This book's novel scope also includes measuring symmetry with Walsh averages, finding a nonparametric regression line, jackknifing, and bootstrapping . Concepts and techniques are explored through practical problems. Quantitative reasoning is at the core of so many professions and academic disciplines, and this book opens the door to the most modern possibilities.
Chapter 1 Exploratory Data Analysis: Observing Patterns and Departures from Patterns 1(142)
1.1 Interpreting Graphical Displays of Data Collections
2(46)
1.1.1 Construction of a Histogram
17(31)
1.2 Numerically Summarizing One-Variable Data Collections
48(47)
1.2.1 Effects of a Linear Transformation
78(17)
1.3 Comparing One-Variable Data Collections
95(29)
Comprehensive Exercises
124(19)
1.A Conceptual
124(4)
1.B Data Analysis/Computational
128(10)
1.C Activities
138(2)
1.D Internet Archives
140(3)
Chapter 2 Exploring Bivariate and Categorical Data 143(56)
2.1 Exploring the Relationship Between Two Quantitative Variables
143(26)
2.1.1 Common Types of Relationships - No Association, Positive Association, Negative Association
144(8)
2.1.2 Scatterplot Smoothing
152(9)
2.1.3 Including a Third Variable on Scatterplots
161(8)
2.2 Measuring the Strength of Association
169(13)
2.2.1 Properties of r
175(3)
2.2.2 An Alternative Measure of Association
178(4)
2.3 Exploring the Relationship between Two Categorical Variables (Frequency Tables)
182(8)
Comprehensive Exercises
190(9)
2.A Conceptual
190(2)
2.B Data Analysis/Computational
192(3)
2.C Activities
195(2)
2.D Internet Archives
197(2)
Chapter 3 Designing a Survey or Experiment: Deciding What and How to Measure 199(44)
3.1 Methods of Data Collection
201(17)
3.2 Planning and Conducting Surveys or Polls
218(9)
3.3 Planning and Conducting Experiments
227(10)
Comprehensive Exercises
237(6)
3.A Conceptual
237(1)
3.B Data Analysis/Computational
238(2)
3.C Activities
240(1)
3.D Internet Archives
241(2)
Chapter 4 Understanding Random Events: Producing Models Using Probability and Simulation 243(88)
4.1 Probability as Relative Frequency: Law of Large Numbers
245(8)
4.2 Some Basic Probability Rules
253(13)
4.2.1 Addition Rule
255(2)
4.2.2 Conditional Probability
257(1)
4.2.3 Multiplication Rule
258(8)
4.3 Discrete Random Variables and Their Probability Distributions
266(13)
4.3.1 Binomial Distribution
268(4)
4.3.2 Geometric Distribution
272(7)
4.4 Simulating Probability Distributions
279(6)
4.5 Expected Values and Standard Deviations for Random Variables
285(6)
4.6 Combining Random Variables
291(4)
4.7 Normal Distributions
295(20)
4.7.1 Probability Calculations for Normal Distributions
299(7)
4.7.2 Using Normal Distributions as Models for Measurements
306(9)
Comprehensive Exercises
315(16)
4.A Conceptual
315(2)
4.B Data Analysis/Computational
317(8)
4.C Activities
325(3)
4.D Internet Archives
328(3)
Chapter 5 Sampling Distributions and Approximations 331(86)
5.1 The Sampling Distribution for a Sample Average
333(13)
5.1.1 Comparing Two Averages
339(7)
5.2 Sampling Distributions for Proportions and Counts
346(20)
5.2.1 Comparing Two Proportions
350(2)
5.2.2 Comparing Several Proportions
352(2)
5.2.3 Using Ranks and Counts to Compare Two Samples
354(12)
5.3 Approximating Sampling Distributions
366(14)
5.4 Simulating Sampling Distributions
380(17)
Comprehensive Exercises
397(20)
5.A Conceptual
397(3)
5.B Data Analysis/Computational
400(13)
5.C Activities
413(1)
5.D Internet Archives
414(3)
6 Statistical Inference: Estimating Probabilities and Testing and Confirming Models 417(120)
6.1 Point Estimation
420(12)
6.2 Interval Estimation
432(34)
6.3 Hypothesis Testing
466(44)
Comprehensive Exercises
510(27)
6.A Conceptual
510(7)
6.B Data Analysis/Computational
517(13)
6.C Activities
530(3)
6.D Internet Archives
533(4)
7 Statistical Inference for the Center of a Population 537(96)
7.1 Exact Inference for the Center of a Population under a Minimal Assumption
539(15)
7.2 Exact Inference for the Center of a Continuous Population Under the Assumption of Population Symmetry
554(20)
7.3 Inference for the Center of a Normal Distribution-Procedures Associated with the Sample Mean and Sample Standard Deviation
574(19)
7.4 Discussion of Methods of Inference for the Center of a Population
593(11)
7.5 Approximate Inference for the Center of a Population when the Number of Sample Observations is Large
604(12)
7.6 Approximate Inference for the Median of an Arbitrary Distribution - Bootstrapping the Sample Median
616(5)
Comprehensive Exercises
621(12)
7.A Conceptual
621(2)
7.B Data Analysis/Computational
623(5)
7.C Activities
628(2)
7.D Internet Archives
630(3)
8 Statistical Inference for Matched Pairs or Paired Replicates Data 633(36)
8.1 Inference for Continuous Paired Replicates or Matched Pairs Data
636(13)
8.2 Inference for Qualitative Differences-Data from Paired Replicates or Matched Pairs Experiments
649(6)
Comprehensive Exercises
655(14)
8.A Conceptual
655(2)
8.B Data Analysis/Computational
657(9)
8.C Activities
666(1)
8.D Internet Archives
667(2)
9 Statistical Inference for Two Populations-Independent Samples 669(104)
9.1 Approximate Inference for the Difference in Proportions for Two Populations
671(16)
9.2 Inference for the Difference in Medians for Any Two Continuous Populations
687(21)
9.3 Approximate Inference for the Difference in Means for Two Populations-Procedures Based on the Two Sample Averages and Sample Standard Deviations
708(26)
9.4 Inference for the Difference in Means for Two Normal Populations with Equal Variances-Procedures Based on the Two Sample Averages and a Pooled Sample Standard Deviation
734(14)
9.5 Discussion of the Methods of Inference for the Difference Between the Centers of Two Populations with Independent Samples
748(1)
Comprehensive Exercises
749(24)
9.A Conceptual
749(1)
9.B Data Analysis/Computational
750(16)
9.C Activities
766(3)
9.D Internet Archives
769(4)
10 Statistical Inference for Two-Way Tables of Count Data 773(66)
10.1 General Test for Differences in Population Proportions
776(11)
10.2 Test for Association (Independence) between Two Categorical Attributes
787(14)
10.3 Exact Procedure for Testing Equality of Two Population Proportions
801(7)
10.4 Goodness-of-fit Test for Probabilities in a Multinomial Distribution with I > 2 Categories
808(9)
Comprehensive Exercises
817(22)
10.A Conceptual
817(3)
10.B Data Analysis/Computational
820(12)
10.C Activities
832(2)
10.D Internet Archives
834(5)
11 Statistical Inference for Bivariate Populations 839(68)
11.1 Correlation Procedures for Bivariate Normal Populations
840(11)
11.2 Rank-Based Correlation Procedures
851(9)
11.3 Fitting a Least Squares Line to Bivariate Data
860(7)
11.4 Linear Regression Inference for Normal Populations
867(7)
11.5 Rank-Based Linear Regression Inference
874(9)
Comprehensive Exercises
883(24)
11.A Conceptual
883(6)
11.B Data Analysis/Computational
889(12)
11.C Activities
901(1)
11.D Internet Archives
902(5)
12 Statistical Inference for More Than Two Populations 907(40)
12.1 One-way Rank-Based General Alternatives ANOVA for More Than Two Populations
909(7)
12.2 One-way General Alternatives ANOVA for More Than Two Normal Populations
916(10)
12.3 One-way Rank-Based Ordered Alternatives ANOVA for More Than Two Populations
926(9)
Comprehensive Exercises
935(12)
12.A Conceptual
935(1)
12.B Data Analysis/Computational
936(6)
12.C Activities
942(2)
12.D Internet Archives
944(3)
Appendix A: Listing of Datasets Usage Locations Throughout IIS 947(4)
Appendix B: Listing of R Functions Usage Locations Throughout IIS 951(4)
Bibliography 955(12)
Index 967
Douglas A. Wolfe is a Professor Emeritus in the Department of Statistics at The Ohio State University. Much of his current research is in ranked set sampling. He is also the author of a popular textbook on nonparametric statistics. Grant Schneider is a Data Scientist at Upstart Network in the San Francisco Bay area. Grant created the accompanying R package and is experienced with statistical programming for research and in the classroom. He received his PhD in Statistics from The Ohio State University.