Interactive Statistics
Published on 9. February 1999
Book
Paperback/Softback
676 pages
978-0-13-231036-9 (ISBN)
Description
This worktext encourages hands-on exploration of statistical concepts so that students take an active part in the learning process. With its strong emphasis on data analysis, this book seeks to make students better consumers of statistics and to give them the skills to design and execute experiments in an undergraduate research class. Statistical concepts are presented economically and immediately reinforced with activities-to be done in small groups or individually-that make the concepts clear and vivid. The TI graphing calculator, although not required, is integrated as an easy-to-use, portable tool that helps students to see statistical methods and models in action. A comprehensive Instructor's Resource Manual, developed by the authors, gives extensive support and guidance for teaching interactively.
More details
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 277 mm
Width: 240 mm
Thickness: 30 mm
Weight
1353 gr
ISBN-13
978-0-13-231036-9 (9780132310369)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Brenda Gunderson
Student Solutions Manual
Book
04/2004
2nd Edition
Pearson
€18.56
Article exhausted; check for reprint
Martha Aliaga | Brenda Gunderson
Interactive Statistics
Book
06/2002
2nd Edition
Pearson
€69.32
Article exhausted; check for reprint
Content
(NOTE: Each chapter begins with an Introduction and ends with a Chapter Summary.)
1. How to Make a Decision with Statistics.
Introduction-Statistics and the Scientific Method. Decisions, Decisions. The Language of Statistical Decision-Making. What's in the Bag? Significant versus Important. Appendix 1.A - Selecting Two Vouchers.
2. Producing Data.
Why Sample? The Language of Sampling. Good Data? Simple Random Sample. Stratified Random Sampling. Systematic Sampling. Cluster Sampling. Multistage Sampling.
3. Observational Studies and Experiments.
Why Study Studies? The Language of Studies. Understanding Observational Studies. Understanding Experiments. Reading with a Critical Eye. What about Ethics?
4. Summarizing Data Graphically.
What Are We Summarizing? Displaying Distributions-Qualitative Variables. Displaying Distributions-Quantitative Variables. Guidelines for Plots, Graphs, and Pictures.
5. Summarizing Data Numerically.
Measuring Center. Measuring Variation or Spread. Linear Transformations and Standardization.
6. Using Models to Make Decisions.
Why Do We Need to Know Models? Modeling Continuous Variables. Modeling Discrete Variables.
7. Is There A Relationship?
Two Quantitative Variables. When Scatterplots Don't Work: Two Qualitative Variables.
8. How to Measure Uncertainty with Probability.
What Is Probability? Simulating Probabilities. The Language of Probability. Random Variables. Appendix 8.A - The Binomial Distribution.
9. Sampling Distributions: Measuring the Accuracy of Sample Results.
Sampling Distribution of a Sample Proportion. Bias and Variability. Sampling Distribution of a Sample Mean.
10. Making Decisions with Confidence.
Making Decisions about a Population Proportion. Making Decisions about a Population Mean. Confidence Interval Estimation: For a Proportion. Confidence Interval Estimation: For a Mean. Confidence Intervals and Hypothesis Testing.
11. Comparing Two Treatments.
Paired Samples versus Independent Samples. Paired Samples. Independent Samples: Comparing Means. Independent Samples: Comparing Proportions.
12. Comparing Many Treatments.
Analysis of Variance. The F-Test Statistic and the F-distribution. ANOVA: Letting the Computer Do the Work! How Do We Get the Mean Squares? What Does Reject H0 in ANOVA Mean? (And What Doesn't It Mean?).
13. Analysis of Count Data.
The Chi-Square Statistic. Test of Goodness of Fit. Test of Homogeneity. Test of Independence.
Appendix.
Answers to Odd-Numbered Exercises.
Index.
1. How to Make a Decision with Statistics.
Introduction-Statistics and the Scientific Method. Decisions, Decisions. The Language of Statistical Decision-Making. What's in the Bag? Significant versus Important. Appendix 1.A - Selecting Two Vouchers.
2. Producing Data.
Why Sample? The Language of Sampling. Good Data? Simple Random Sample. Stratified Random Sampling. Systematic Sampling. Cluster Sampling. Multistage Sampling.
3. Observational Studies and Experiments.
Why Study Studies? The Language of Studies. Understanding Observational Studies. Understanding Experiments. Reading with a Critical Eye. What about Ethics?
4. Summarizing Data Graphically.
What Are We Summarizing? Displaying Distributions-Qualitative Variables. Displaying Distributions-Quantitative Variables. Guidelines for Plots, Graphs, and Pictures.
5. Summarizing Data Numerically.
Measuring Center. Measuring Variation or Spread. Linear Transformations and Standardization.
6. Using Models to Make Decisions.
Why Do We Need to Know Models? Modeling Continuous Variables. Modeling Discrete Variables.
7. Is There A Relationship?
Two Quantitative Variables. When Scatterplots Don't Work: Two Qualitative Variables.
8. How to Measure Uncertainty with Probability.
What Is Probability? Simulating Probabilities. The Language of Probability. Random Variables. Appendix 8.A - The Binomial Distribution.
9. Sampling Distributions: Measuring the Accuracy of Sample Results.
Sampling Distribution of a Sample Proportion. Bias and Variability. Sampling Distribution of a Sample Mean.
10. Making Decisions with Confidence.
Making Decisions about a Population Proportion. Making Decisions about a Population Mean. Confidence Interval Estimation: For a Proportion. Confidence Interval Estimation: For a Mean. Confidence Intervals and Hypothesis Testing.
11. Comparing Two Treatments.
Paired Samples versus Independent Samples. Paired Samples. Independent Samples: Comparing Means. Independent Samples: Comparing Proportions.
12. Comparing Many Treatments.
Analysis of Variance. The F-Test Statistic and the F-distribution. ANOVA: Letting the Computer Do the Work! How Do We Get the Mean Squares? What Does Reject H0 in ANOVA Mean? (And What Doesn't It Mean?).
13. Analysis of Count Data.
The Chi-Square Statistic. Test of Goodness of Fit. Test of Homogeneity. Test of Independence.
Appendix.
Answers to Odd-Numbered Exercises.
Index.