Teaching Statistics: A Bag of Tricks

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OUP Oxford, Aug 8, 2002 - Mathematics - 320 pages
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Students in the sciences, economics, psychology, social sciences, and medicine take introductory statistics. Statistics is increasingly offered at the high school level as well. However, statistics can be notoriously difficult to teach as it is seen by many students as difficult and boring, if not irrelevant to their subject of choice. To help dispel these misconceptions, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, examples and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and combines chapters such as, 'First week of class', with exercises to break the ice and get students talking; then 'Descriptive statistics' , collecting and displaying data; then follows the traditional topics - linear regression, data collection, probability and inference. Part II gives tips on what does and what doesn't work in class: how to set up effective demonstrations and examples, how to encourage students to participate in class and work effectively in group projects. A sample course plan is provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics and sampling.

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12 Fitting demonstrations examples and projects into a course
13 What makes a good example?
14 Why is statistics important?
15 The best of the best
Part IIntroductory probability andstatistics
2First week of class
22 Where are the cancers?
93 A nonlinear model for golf putting
94 Pythagoras goes linear
10Lying with statistics
102 Selection bias
103 Reviewing the semesters material
104 1 in 2 marriages end in divorce?
105 Ethics and statistics
Part IIPutting it all together

23 Estimating a big number
24 Whats in the news?
25 Collecting data from students
3Descriptive statistics
32 Time series
33 Numerical variables distributions and histograms
34 Numerical summaries
35 Data in more than one dimension
36 The normal distribution in one and two dimensions
37 Linear transformations and linear combinations
38 Logarithmic transformations
4Linear regression and correlation
42 Correlation
43 Regression to the mean
5Data collection
52 Class projects in survey sampling
53 Experiments
54 Observational studies
6Statistical literacy and thenews media
61 Introduction
63 Assignment where students find their own articles
64 Guidelines for finding and evaluating sources
65 Discussion and student reactions
66 Examples of course packets
73 Probabilities of compound events
74 Probability modeling
75 Conditional probability
76 You can load a die but you cant bias a coin flip
8Statistical inference
distributions of totals and averages
z t and χ2 tests
86 Simple examples of applied inference
87 Advanced concepts of inference
9Multiple regression andnonlinear models
92 Exam scores
11How to do it
112 Inclass activities
113 Using exams to teach statistical concepts
114 Projects
115 Resources
12Structuring an introductory statistics course
122 Finding time for student activities in class
123 A detailed schedule for a semesterlong course
124 Outline for an alternative schedule of activities
Part IIIMore advanced courses
13Decision theory and Bayesianstatistics
131 Decision analysis
132 Bayesian statistics
14 Student activities in surveysampling
142 Random number generation
143 Estimation and confidence intervals
144 A visit to Clusterville
145 Statistical literacy and discussion topics
146 Projects
15Problems and projects inprobability
152 Introductory problems
153 Challenging problems
154 Does the Poisson distribution fit real data?
155 Organizing student projects
156 Examples of structured projects
157 Examples of unstructured projects
158 Research papers as projects
16Directed projects in amathematical statistics course
161 Organization of a case study
162 Fitting the cases into a course
quality control
helicopter design
1642 Designing the study and fitting a response surface
Notes and further reading
Author Index
Subject Index

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