{"product_id":"activity-based-statistics-2nd-edition-student-guide-isbn-9780470412091","title":"Activity-Based Statistics, 2nd Edition Student Guide","description":"\u003ci\u003eActivity-Based Statistics\u003c\/i\u003e helps build real statistical understanding through a set of innovative hands-on activities that can be used each day in conjunction with other texts.  The second edition continues to emphasize discovery by motivating students to apply the skills they have learned to discover the everyday relevance of statistics.  There are over 45 activities and five long-term projects that have been updated and extended to encourage students to experience statistics in context.  While the second edition includes updated technology extensions for Fathom, technology is used throughout the book to extend activities and is not required to complete them.   Preface.  \u003cp\u003eTo the Student.\u003c\/p\u003e \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI. EXPLORING DATA.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Living Histogram.\u003c\/p\u003e \u003cp\u003eHow to build a histogram out of people.\u003c\/p\u003e \u003cp\u003eGetting to Know the Class.\u003c\/p\u003e \u003cp\u003eSurveying the class. Exploratory data analysis. Displaying categorical and\u003cbr\u003e quantitative data.\u003c\/p\u003e \u003cp\u003eA Living Box Plot.\u003c\/p\u003e \u003cp\u003eHow to construct a box-and-whisker plot with people. Shape, center, and spread.\u003c\/p\u003e \u003cp\u003eV Is for Variation: How Far Are You from the Mean?\u003c\/p\u003e \u003cp\u003eMeasuring variability in data.\u003c\/p\u003e \u003cp\u003eMatching Plots to Variables.\u003c\/p\u003e \u003cp\u003eConnecting our knowledge of real-life distributions to their graphs.\u003c\/p\u003e \u003cp\u003eMatching Statistics to Plots.\u003c\/p\u003e \u003cp\u003eMatching summary statistics to graphs of distributions. How the mean can differ\u003cbr\u003e from the median.\u003c\/p\u003e \u003cp\u003eVariation in Measurement.\u003c\/p\u003e \u003cp\u003eCollecting measurement data and looking at its distribution.\u003c\/p\u003e \u003cp\u003eMeasurement Bias.\u003c\/p\u003e \u003cp\u003eExperiencing measurement bias.\u003c\/p\u003e \u003cp\u003eLet Us Count.\u003c\/p\u003e \u003cp\u003eVariation due to the process of measurement.\u003c\/p\u003e \u003cp\u003eMatching Descriptions to Scatter Plots.\u003c\/p\u003e \u003cp\u003eMaking the correspondence between scatter plots and statistics (regression line\u003cbr\u003e and r).\u003c\/p\u003e \u003cp\u003eThe Regression Effect.\u003c\/p\u003e \u003cp\u003eFind out about the regression effect.\u003c\/p\u003e \u003cp\u003eLeonardo's Model Bodies.\u003c\/p\u003e \u003cp\u003eLooking at correlation between the sizes of different body parts.\u003c\/p\u003e \u003cp\u003eRelating to Correlation.\u003c\/p\u003e \u003cp\u003eHow the correlation coefficient from a sample varies about the true population\u003cbr\u003e coefficient.\u003c\/p\u003e \u003cp\u003eModels, Models, Models . . . .\u003c\/p\u003e \u003cp\u003eModeling time series data with lines. Breaking the time series into two pieces.\u003cbr\u003e Predictable Pairs.\u003c\/p\u003e \u003cp\u003eRelationships between categorical variables. Association in two-way tables.\u003cbr\u003e Ratings and Ranks.\u003c\/p\u003e \u003cp\u003eThe relationship between ratings and ranks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. PLANNING A STUDY.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRandom Rectangles.\u003c\/p\u003e \u003cp\u003eSampling bias. The importance of random sampling.\u003c\/p\u003e \u003cp\u003eThe Rating Game.\u003c\/p\u003e \u003cp\u003eAnalyzing ratings that are results from a questionnaire you design.\u003c\/p\u003e \u003cp\u003eStringing Students Along.\u003c\/p\u003e \u003cp\u003eLearning about selection bias.\u003c\/p\u003e \u003cp\u003eGummy Bears in Space.\u003c\/p\u003e \u003cp\u003eFactorial designs and interaction. Controlling variables.\u003c\/p\u003e \u003cp\u003eFunnel Swirling.\u003c\/p\u003e \u003cp\u003eExperimental design, particularly factorial design.Variability in an experiment.\u003c\/p\u003e \u003cp\u003eJumping Frogs.\u003c\/p\u003e \u003cp\u003eExperiments in a factorial situation. Estimating the effects of each factor and of\u003cbr\u003e the interaction.\u003c\/p\u003e \u003cp\u003eHow to Ask Questions: Designing a Survey\u003c\/p\u003e \u003cp\u003eHow the way a question is worded can affect the outcome of a survey.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII. ANTICIPATING PATTERNS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Random Behavior?\u003c\/p\u003e \u003cp\u003eGambler's fallacy. It’s hard to predict short-term random behavior.\u003c\/p\u003e \u003cp\u003eThe Law of Averages.\u003c\/p\u003e \u003cp\u003eWhat is the law of averages? How probability helps us predict in the long term.\u003c\/p\u003e \u003cp\u003eStreaky Behavior.\u003c\/p\u003e \u003cp\u003eRuns in Bernoulli trials. Randomness is streakier than we think.\u003c\/p\u003e \u003cp\u003eCounting Successes.\u003c\/p\u003e \u003cp\u003eHow to create simulations to study problems about the number of successes in\u003cbr\u003e repetitions of an event with a known probability.\u003c\/p\u003e \u003cp\u003eWaiting for Sammy Sosa.\u003c\/p\u003e \u003cp\u003eThe geometric, or waiting-time, distribution.\u003c\/p\u003e \u003cp\u003eThe Lazy Student.\u003c\/p\u003e \u003cp\u003eWhat happens to the spread when you add random variables.\u003c\/p\u003e \u003cp\u003eWhat's the Chance?\u003c\/p\u003e \u003cp\u003eDependent and independent trials.\u003c\/p\u003e \u003cp\u003eSpinning Pennies.\u003c\/p\u003e \u003cp\u003eSampling distributions. Distribution of sample proportions where p 0.5.\u003c\/p\u003e \u003cp\u003eCents and the Central Limit Theorem.\u003c\/p\u003e \u003cp\u003eHow the sampling distribution of the mean of a nonnormal distribution looks\u003cbr\u003e normal.\u003cbr\u003e Sampling Error and Estimation.\u003c\/p\u003e \u003cp\u003eHow an estimate (for example, of a mean) based on a sample differs from the\u003cbr\u003e population value.\u003c\/p\u003e \u003cp\u003eHow Accurate Are the Polls?\u003c\/p\u003e \u003cp\u003eHow an estimate of a proportion differs from the population value. How the\u003cbr\u003e spread of sampling distributions defines a margin of sampling error.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV. STATISTICAL INFERENCE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow Many Tanks?\u003c\/p\u003e \u003cp\u003eEstimating a population from serial numbers. Unbiased estimators.\u003c\/p\u003e \u003cp\u003eEstimating the Total of a Restaurant Bill.\u003c\/p\u003e \u003cp\u003eSources of bias in estimation. Compensating for bias.\u003c\/p\u003e \u003cp\u003eWhat Is a Confidence Interval Anyway?\u003c\/p\u003e \u003cp\u003eExplaining the confidence interval as the range of plausible population values.\u003c\/p\u003e \u003cp\u003eConfidence Intervals for the Proportion of Even Digits.\u003c\/p\u003e \u003cp\u003eThe meaning of the confidence level.What affects whether 95% of the confidence\u003c\/p\u003e \u003cp\u003eintervals contain the true value.\u003c\/p\u003e \u003cp\u003eCapture\/Recapture.\u003c\/p\u003e \u003cp\u003eEstimating population size using a capture\/recapture technique.\u003c\/p\u003e \u003cp\u003eHow to Ask Sensitive Questions.\u003c\/p\u003e \u003cp\u003eRandomized response sampling. Using probability techniques to disguise survey\u003cbr\u003e answers and preserve confidentiality.\u003c\/p\u003e \u003cp\u003eEstimating a Total.\u003c\/p\u003e \u003cp\u003eIt is not always clear how to sample to get the best estimate.\u003c\/p\u003e \u003cp\u003eThe Bootstrap.\u003c\/p\u003e \u003cp\u003eCreating an interval estimate for statistics when the traditional confidence\u003cbr\u003e interval may be inappropriate.\u003c\/p\u003e \u003cp\u003eStatistical Evidence of Discrimination.\u003c\/p\u003e \u003cp\u003eUsing the randomization test to show that variables are associated.\u003c\/p\u003e \u003cp\u003eHow Typical Are Our Households’ Ages? The Chi-Square Test\u003c\/p\u003e \u003cp\u003eUsing chi-square to show that (binned) age distributions are different.\u003c\/p\u003e \u003cp\u003eCoins on Edge.\u003c\/p\u003e \u003cp\u003eThe power of a hypothesis test increases as the sample size increases.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV. PROJECTS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTheme: Exploration of Data and Improvement in Quality.\u003c\/p\u003e \u003cp\u003eApplication: Improved Payment Processing in a Utilities Firm.\u003c\/p\u003e \u003cp\u003eTheme: Sample Survey.\u003c\/p\u003e \u003cp\u003eApplication 1: A Typical Election Poll.\u003c\/p\u003e \u003cp\u003eApplication 2: The Nielsens.\u003c\/p\u003e \u003cp\u003eTheme: Experiment.\u003c\/p\u003e \u003cp\u003eApplication: Does Aspirin Help Prevent Heart Attacks? The Physicians' Health Study.\u003c\/p\u003e \u003cp\u003eTheme: Modeling.\u003c\/p\u003e \u003cp\u003eApplication: Body Composition.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDr. Richard L Sceaffer\u003c\/b\u003e's research and scholarly interests are in statistical education and sampling theory\/practice. He is currently a professor in the department of statistics at the University of Florida.\u003cbr\u003eB.A. 1962 Lycoming college; M.A. 1964 Bucknell University; Ph.D. 1968 Florida State University.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988658176229,"sku":"NP9780470412091","price":44.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470412091.jpg?v=1761781149","url":"https:\/\/k12savings.com\/es\/products\/activity-based-statistics-2nd-edition-student-guide-isbn-9780470412091","provider":"K12savings","version":"1.0","type":"link"}