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Teaching statistics : a bag of tricks

Teaching statistics : a bag of tricks (7회 대출)

자료유형
단행본
개인저자
Gelman, Andrew. Nolan, Deborah Ann.
서명 / 저자사항
Teaching statistics : a bag of tricks / Andrew Gelman, Deborah Nolan.
발행사항
Oxford ;   New York :   Oxford University Press ,   2002.  
형태사항
xv, 299 p. : ill. ; 22 cm.
ISBN
0198572255 (acid-free paper) 0198572247 (pbk. : acid-free paper) 9780198572244
서지주기
Includes bibliographical references and index.
일반주제명
Statistics -- Study and teaching.
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010 ▼a 2002727080
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020 ▼a 0198572255 (acid-free paper)
020 ▼a 0198572247 (pbk. : acid-free paper)
020 ▼a 9780198572244
035 ▼a (KERIS)REF000006987642
040 ▼a UKM ▼c UKM ▼d DLC ▼d 211009
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050 0 0 ▼a QA276.18 ▼b .G45 2002
082 0 4 ▼a 519.50711 ▼2 22
090 ▼a 519.50711 ▼b G319t
100 1 ▼a Gelman, Andrew.
245 1 0 ▼a Teaching statistics : ▼b a bag of tricks / ▼c Andrew Gelman, Deborah Nolan.
260 ▼a Oxford ; ▼a New York : ▼b Oxford University Press , ▼c 2002.
300 ▼a xv, 299 p. : ▼b ill. ; ▼c 22 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Statistics ▼x Study and teaching.
700 1 ▼a Nolan, Deborah Ann.
945 ▼a KINS

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 519.50711 G319t 등록번호 121147333 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차


CONTENTS
1 Introduction = 1
  1.1 The challenge of teaching introductory statistics = 1
  1.2 Fitting demonstrations, examples, and projects into a course = 1
  1.3 What makes a good example? = 3
  1.4 Why is statistics important? = 3
  1.5 The best of the best = 4
  1.6 Our motivation for writing this book = 4
PARTⅠ INTRODUCTORY PROBABILITY AND STATISTICS
 2 First week of class = 11
  2.1 Guessing ages = 11
  2.2 Where are the cancers? = 13
  2.3 Estimating a big number = 14
  2.4 What's in the news? = 15
  2.5 Collecting data from students = 17
 3 Descriptive statistics = 19
  3.1 Displaying graphs on the blackboard = 19
  3.2 Time series = 19
   3.2.1 World record times for the mile run = 20
  3.3 Numerical variables, distributions, and histograms = 20
   3.3.1 Categorical and continuous variables = 20
   3.3.2 Handedness = 21
   3.3.3 Soft drink consumption = 22
  3.4 Numerical summaries = 22
   3.4.1 Average soft drink consumption = 22
   3.4.2 The average student = 24
  3.5 Data in more than one dimension = 24
   3.5.1 Guessing exam scores = 25
   3.5.2 Who opposed the Vietnam War? = 27
  3.6 The normal distribution in one and two dimensions = 28
   3.6.1 Heights of men and women = 29
   3.6.2 Heights of conscripts = 30
   3.6.3 Scores on two exams = 30
  3.7 Linear transformations and linear combinations = 31
   3.7.1 College admissions = 31
   3.7.2 Social and economic indexes = 31
   3.7.3 Age adjustment = 32
  3.8 Logarithmic transformations = 32
   3.8.1 Simple examples : amoebas, squares, and cubes = 33
   3.8.2 Log-linear transformation : world population = 33
   3.8.3 Log-log transformation : metabolic rates = 35
 4 Linear regression and correlation = 38
  4.1 Fitting linear regressions = 38
   4.1.1 Simple examples of least squares = 38
   4.1.2 Tall people have higher incomes = 39
   4.1.3 Logarithm of world population = 41
  4.2 Correlation = 43
   4.2.1 Correlations of body measurements = 43
   4.2.2 Correlation and causation in observational data = 44
  4.3 Regression to the mean = 45
   4.3.1 Mini-quizzes = 45
   4.3.2 Exam scores, heights, and the general principle = 46
 5 Data collection = 48
  5.1 Sample surveys = 48
   5.1.1 Sampling from the telephone book = 48
   5.1.2 First digits and Benford's law = 52
   5.1.3 Wacky surveys = 54
   5.1.4 An election exit poll = 55
   5.1.5 Simple examples of bias = 56
   5.1.6 How large is your family? = 56
  5.2 Class projects in survey sampling = 57
   5.2.1 The steps of the project = 58
   5.2.2 Topics for student surveys = 63
  5.3 Experiments = 66
   5.3.1 An experiment that looks like a survey = 66
   5.3.2 Randomizing the order of exam questions = 68
   5.3.3 Taste tests = 69
  5.4 Observational studies = 72
   5.4.1 The Surgeon General's report on smoking = 73
   5.4.2 Large population studies = 73
   5.4.3 Coaching for the SAT = 75
 6 Statistical literacy and the news media = 76
  6.1 Introduction = 76
  6.2 Assignment based on instructional packets = 77
  6.3 Assignment where students find their own articles = 79
  6.4 Guidelines for finding and evaluating sources = 82
  6.5 Discussion and student reactions = 84
  6.6 Examples of course packets = 84
   6.6.1 A controlled experiment : IV fluids for trauma victims = 85
   6.6.2 A sample survey : 1 in 4 youths abused, survey finds = 90
   6.6.3 An observational study : Monster in the crib = 93
   6.6.4 A model-based analysis : Illegal aliens put uneven load = 98
 7 Probability = 103
  7.1 Constructing probability examples = 103
  7.2 Random numbers via dice or handouts = 103
   7.2.1 Random digits via dice = 103
   7.2.2 Random digits via handouts = 103
   7.2.3 Normal distribution = 104
   7.2.4 Poisson distribution = 104
  7.3 Probabilities of compound events = 104
   7.3.1 Babies = 104
   7.3.2 Real vs. fake coin flips = 105
   7.3.3 Lotteries = 107
  7.4 Probability modeling = 108
   7.4.1 Lengths of baseball World Series = 108
   7.4.2 Voting and coalitions = 110
   7.4.3 Space shuttle failure and other rare events = 110
  7.5 Conditional probability = 111
   7.5.1 What's the color on the other side of the card? = 111
   7.5.2 Lie detectors and false positives = 113
  7.6 You can load a die but you can't bias a coin flip = 114
   7.6.1 Demonstration using plastic checkers and wooden dice = 115
   7.6.2 Sporting events and quantitative literacy = 117
   7.6.3 Physical explanation = 118
 8 Statistical inference = 120
  8.1 Weighing a "random" sample = 120
  8.2 From probability to inference : distributions of totals and averages = 121
   8.2.1 Where are the missing girls? = 121
   8.2.2 Real-time gambler's ruin = 122
  8.3 Confidence intervals : examples = 123
   8.3.1 Biases in age guessing = 123
   8.3.2 Comparing two groups = 124
   8.3.3 Land or water? = 124
   8.3.4 Poll differentials : a discrete distribution = 125
   8.3.5 Golf : can you putt like the pros? = 126
  8.4 Confidence intervals : theory = 126
  8.4.1 Coverage of confidence intervals = 126
  8.4.2 Noncoverage of confidence intervals = 128
  8.5 Hypothesis testing : z, t, and χ²tests = 128
   8.5.1 Hypothesis tests from examples of confidence intervals = 129
   8.5.2 Binomial model : sampling from the phone book = 130
   8.5.3 Hypergeometric model : taste testing = 131
   8.5.4 Benford's law of first digits = 131
   8.5.5 Length of baseball World Series = 131
  8.6 Simple examples of applied inference = 132
   8.6.1 How good is your memory? = 132
   8.6.2 How common is your name? = 133
  8.7 Advanced concepts of inference = 134
   8.7.1 Shooting baskets and statistical power = 134
   8.7.2 Do-it-yourself data dredging = 134
   8.7.3 Praying for your health = 135
 9 Multiple regression and nonlinear models = 137
  9.1 Regression of income on height and sex = 137
   9.1.1 Inference for regression coefficients = 137
   9.1.2 Multiple regression = 137
   9.1.3 Regression with interactions = 139
   9.1.4 Transformations = 140
  9.2 Exam scores = 141
   9.2.1 Studying the fairness of random exams = 141
   9.2.2 Measuring the reliability of exam questions = 141
  9.3 A nonlinear model for golf putting = 142
   9.3.1 Looking at data = 143
   9.3.2 Constructing a probability model = 143
   9.3.3 Checking the fit of the model to the data = 144
  9.4 Pythagoras goes linear = 145
 10 Lying with statistics = 147
  10.1 Examples of misleading presentations of numbers = 147
   10.1.1 Fabricated or meaningless numbers = 147
   10.1.2 Misinformation = 147
   10.1.3 Ignoring the baseline = 149
   10.1.4 Arbitrary comparisons or data dredging = 149
   10.1.5 Misleading comparisons = 151
  10.2 Selection bias = 153
   10.2.1 Distinguishing from other sorts of bias = 153
   10.2.2 Some examples presented as puzzles = 154
   10.2.3 Avoiding over-skepticism = 155
  10.3 Reviewing the semester's material = 155
   10.3.1 Classroom discussion = 155
   10.3.2 Assignments : find the lie or create the lie = 156
  10.4 1 in 2 marriages end in divorce? = 156
  10.5 Ethics and statistics = 158
   10.5.1 Cutting corners in a medical study = 158
   10.5.2 Searching for statistical significance = 159
   10.5.3 Controversies about randomized experiments = 159
   10.5.4 How important is blindness? = 160
   10.5.5 Use of information in statistical inferences = 161
PARTⅡ PUTTING IT ALL TOGETHER
 11 How to do it = 167
  11.1 Getting started = 167
   11.1.1 Multitasking = 167
   11.1.2 Advance planning = 167
   11.1.3 Fitting an activity to your class = 168
   11.1.4 Common mistakes = 168
  11.2 In-class activities = 171
   11.2.1 Setting up effective demonstrations = 171
   11.2.2 Promoting discussion = 172
   11.2.3 Getting to know the students = 173
   11.2.4 Fostering group work = 173
  11.3 Using exams to teach statistical concepts = 175
  11.4 Projects = 175
   11.4.1 Monitoring progress = 177
   11.4.2 Organizing independent projects = 178
   11.4.3 Topics for projects = 181
   11.4.4 Statistical design and analysis = 183
  11.5 Resources = 185
   11.5.1 What's in a spaghetti box? Cooking up activities from scratch = 185
   11.5.2 Books = 186
   11.5.3 Periodicals = 187
   11.5.4 Web sites = 187
   11.5.5 People = 188
 12 Structuring an introductory statistics course = 189
  12.1 Before the semester begins = 189
  12.2 Finding time for student activities in class = 190
  12.3 A detailed schedule for a semester-long course = 190
  12.4 Outline for an alternative schedule of activities = 198
PARTⅢ MORE ADVANCED COURSES
 13 Decision theory and Bayesian statistics = 203
  13.1 Decision analysis = 204
   13.1.1 How many quarters are in the jar? = 204
   13.1.2 Utility of money = 207
   13.1.3 Risk aversion = 209
   13.1.4 What is the value of a life? = 210
   13.1.5 Probabilistic answers to true-false questions = 211
   13.1.6 Homework project : evaluating real-life forecasts = 212
   13.1.7 Real decision problems = 213
  13.2 Bayesian statistics = 215
   13.2.1 Where are the cancers? = 215
   13.2.2 Subjective probability intervals and calibration = 216
   13.2.3 Drawing parameters out of a hat = 219
   13.2.4 Where are the cancers? A simulation = 219
   13.2.5 Hierarchical modeling and shrinkage = 220
 14 Student activities in survey sampling = 222
  14.1 First week of class = 222
   14.1.1 News clippings = 222
   14.1.2 Class survey = 223
  14.2 Random number generation = 224
   14.2.1 What do random numbers look like? = 224
   14.2.2 Random numbers from coin flips = 224
  14.3 Estimation and confidence intervals = 225
  14.4 A visit to Clusterville = 226
  14.5 Statistical literacy and discussion topics = 228
  14.6 Projects = 230
   14.6.1 Research papers on complex surveys = 231
   14.6.2 Sampling and inference in StatCity = 232
   14.6.3 A special topic in sampling = 236
 15 Problems and projects in probability = 237
  15.1 Setting up a probability course as a seminar = 237
  15.2 Introductory problems = 238
   15.2.1 Probabilities of compound events = 239
   15.2.2 Introducing the concept of expectation = 240
  15.3 Challenging problems = 241
  15.4 Does the Poisson distribution fit real data? = 243
  15.5 Organizing student projects = 244
  15.6 Examples of structured projects = 244
   15.6.1 Fluctuations in coin tossing-arcsine laws = 245
   15.6.2 Recurrence and transience in Markov chains = 247
  15.7 Examples of unstructured projects = 249
   15.7.1 Martingales = 249
   15.7.2 Generating functions and branching processes = 250
   15.7.3 Limit distributions of Markov chains = 250
   15.7.4 Permutations = 251
  15.8 Research papers as projects = 252
 16 Directed projects in a mathematical statistics course = 254
  16.1 Organization of a case study = 255
  16.2 Fitting the cases into a course = 255
   16.2.1 Covering the cases in lectures = 256
   16.2.2 Group work in class = 256
   16.2.3 Cases as reports = 257
   16.2.4 Independent projects in a seminar course = 257
  16.3 A case study : quality control = 258
  16.4 A directed project : helicopter design = 259
   16.4.1 General instructions = 259
   16.4.2 Designing the study and fitting a response surface = 261
Notes = 265
References = 277
Author Index = 288
Subject Index = 292


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