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A Guide to Teaching Statistics

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280 pages
A Guide to Teaching Statistics: Innovations and Best Practices addresses the critical aspects of teaching statistics to undergraduate students, acting as an invaluable tool for both novice and seasoned teachers of statistics.
• Guidance on textbook selection, syllabus construction, and course outline
• Classroom exercises, computer applications, and Internet resources designed to promote active learning
• Tips for incorporating real data into course content
• Recommendations on integrating ethics and diversity topics into statistics education
• Strategies to assess student's statistical literacy, thinking, and reasoning skills
• Additional material online at www.teachstats.org
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Series Editors’ Preface Preface
Part I
1
Contents
Course Preparation
Teaching Statistics: A Beginning So Why Teach Statistics? Historical Pedagogical Controversies Who should teach statistics? Statistics labs and related technology Content of statistics courses Statistics in Relation to the Discipline Sequence of the Class and Topics Introducing Research Methods within the Context of Statistics Student Populations Mathematical ability Cognitive ability and learning styles Self-efﬁcacy and motivation Gender Helping Your Students Survive Statistics Conclusion
xiii xvii
1
3 5 7 7 8 10 11 12
16 17 17 19 20 22 23 25
viii
2
Contents
Nuts and Bolts of Teaching Statistics Syllabus Construction Textbook Selection Conceptual orientation Level of difﬁculty Chapter topics and organization Core formulas and vocabulary Type of data sets/quality of the exercises Traditional Versus Electronic Textbooks Supplemental Materials Study guides Companion Web sites Computer tutorials Electronic Discussion Boards Multimedia Tools Presentation technology Interactive applications: Java applets, Flash, Shockwave, and HTML Multimedia simulation programs Conclusion
Part II
3
Theoretical and Pedagogical Concerns
Educational Reform in Statistics Educational Reform Statistically Educated Students Statistical Literacy Knowledge elements Dispositional elements Statistical Thinking Statistical Reasoning Misconceptions Impacting the Development of Literacy, Thinking, and Reasoning Final Thoughts on Statistical Literacy, Thinking, and Reasoning Assessment What is the role of assessment? What is the role of authentic assessment? Assessment and learning outcomes or goals Conclusion
27 28 30 31 33 34 35 36 37 38 39 39 40 42 44 45
46 48 49
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53 54 56 59 60 62 63 66
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72 73 73 74 75 77
6
5
129
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4
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Contents
ix
Teaching Speciﬁc Statistical Concepts
In the Classroom Conceptual Learning, Active Learning, and Real Data Conceptual learning versus rote memorization Active learning Real data Instructional Techniques Lecture The use of questions Practice problems and examples Journal assignments Activities and demonstrations Writing assignments Concept maps Cooperative learning Projects Assessment Principles of effective assessment Mastery learning Confronting Fear and Anxiety Conclusion
Part III
79 80 80 82 83 84 85 86 87 88 89 90 93 94 95 97 97 98 99 101
105 106 107 108 109 110 111 112 114 117 119 122 125 127
Descriptive Statistics and Bivariate Distributions Graphing Data The use of graphs in science Elements of good design Human graphical perception Available graphing methods Software design Normal Distribution Measures of Central Tendency Measures of Variability Correlation Simple Linear Regression Computer Applications Conclusion
Teaching Hypothesis Testing Samples, Sampling Distributions, and the Central Limit Theorem
x Contents
Conﬁdence Intervals Introduction to Null Hypothesis Testing Additional Introduction to Hypothesis Testing Concepts Power Effect sizes Type I and Type II errors Analysis of Variance Introduction to ANOVA Violating ANOVA assumptions Factorial ANOVA General linear model The Debate Surrounding Null Hypothesis Signiﬁcance Testing Nonparametric Statistics Computer Applications Conclusion
Part IV
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8
Data Analysis in Statistical Education Teaching with Statistical Software Tools Data Analysis Packages SPSS Microsoft Excel Other commercial data analysis programs Comparing data analysis programs Data Analysis Software Textbooks Using Data Sets in the Classroom Artiﬁcial data sets for the classroom Reality-based data sets Finding appropriate reality-based data sets Drawbacks to using real data sets Conclusion
Endings and Beginnings Multivariate Statistics Multiple regression Logistic regression Additional multivariate techniques
133 135
138 138 140 141 142 142 143 144 145
146 146 149 151
153
155 156 158 158 160 162 163 165 166 167 168 169 174 176
179 180 182 184 185
Special Topics Ethics Diversity Online Statistical Education Finishing up Any Statistics Course Final Thoughts
References Index
Contents
xi
186 187 190 193 195 198
201 248