Table of Contents Title and Description Overview p. xv - - Before the Semester Begins: Suggestions for Prep & Syllabus p. xxxi - - Complex Numerical Summaries; Graphical Displays 1.A - 1.B - Data for Life Collect data that will be referred to throughout the semester; supplemental spreadsheet provided Our Learning Community Student success focus Establish a sense of shared responsibility; provide key information about course content and policies p. 1 - p. 1 p. 9 - p. 5 1.C 1.C Instant Runoff Voting schemes 1.D - Borda Count Voting schemes p. 19 1.C p. 7 p. 27 1.D p. 11 2.A 2.A 2.B 2.B 2.C - Graphical Displays Analysis and communication; dotplots, histograms, boxplots; mean; median Forming Effective Study Groups Student success focus Taking responsibility for own learning and supporting learning of others; setting norms Mini-Project: Graphical Displays Write formal, contextual analysis on compared data; research-related data; sample rubric provided p. 35 2.A p. 13 p. 43 - p. 15 p. 51 - p. 17 page vii
Title and Description 3.A 3.A Who Is in the Population? Populations; sampling 3.B 3.B How Much Water Do I Drink? Analyzing class data; Central Limit Theorem p. 57 3.A p. 19 p. 67 3.B p. 21 3.C 3.C 4.A 4.A 4.B 4.B 5.A 5.A 5.B 5.B How Much Water Does Our Class Drink? (Optional) Sample standard deviation What Are the Risks? Theoretical probability of two or more independent events Calculating Risk Conditional probability of two or more dependent events Cost of Living Comparisons Conversion to create equivalent units; supplemental spreadsheet Index Numbers Using indices such as Consumer Price Index; supplemental spreadsheet p. 73 3.C p. 23 p. 83 4.A p. 27 p. 91 4.B p. 31 p. 101 5.A p. 35 p. 109 5.B p. 37 5.C 5.C Polls, Polls, Polls! Weighted averages p. 117 5.C p. 39 5.D 5.D 6.A 6.A 6.B Average Income Weighted averages and expected value; supplemental spreadsheet How Can We Smooth the Data? (Optional) Simple and weighted moving averages; supplemental spreadsheet Mini-Project: Income Disparities (Optional) Written analysis of graphical display of weighted moving average p. 125 5.D p. 43 p. 131 6.A p. 45 p. 139 p. 47 7.A 7.A U.S. Budget Priorities Part-part vs. part-whole ratios p. 149 7.A p. 49 page viii
Title and Description 7.B 7.B Understanding U.S. Budget Priorities Decimals, percentages, and part-whole ratios p. 157 7.B p. 53 7.C 7.C Changes to U.S. Budget Priorities Absolute and relative change p. 167 7.C p. 57 7.D 7.D Percent of Total U.S. Budget Dotplots used to introduce symmetry and skewness p. 175 7.D p. 59 7.E 7.E What s My Credit Score? Application of ratios; assignment can be miniproject. Collect data for 8, Part D; schedule lab for 8.D and 10.A. p. 183 7.E p. 61 7.F 7.F U.S. Incarceration Rates Applications of ratios; comparison p. 193 7.F p. 65 Mathematical Modeling 8.A 8.A More Water, Please! Introduction to mathematical modeling 8.B 8.B What s My Car Worth? Distinguishing proportionality and linearity 8.C 8.C How Money Makes Money Non-linear models p. 203 8.A p. 67 p. 215 8.B p. 71 p. 223 8.C p. 73 8.D 8.D 8.E 8.E 8.F 8.F Have My Choices Affected My Learning? Regression using student data. Computer lab day, if possible. Mini-Project: Progressive and Flat Income Tax Systems (Optional) Informal piecewise linear function Mini-Project: Estimating the Number of People in a Crowd (Optional) Using proportionality to estimate p. 235 8.D p. 77 p. 245 p. 81 p. 259 p. 87 9.A 9.A Depreciation Modeling, interpolation, and extrapolation p. 271 9.A p. 91 page ix
Title and Description 9.B 9.B Appreciating Depreciation Linear interpolation via similar triangles 9.C 9.C How Much Should I Be Paid? Correlation 9.D 9.D Why Are You Wearing the Same Old Socks? Correlation vs. causation; strength p. 283 9.B p. 97 p. 293 9.C p. 101 p. 305 9.D p. 107 10.A 10.A Fibonacci s Rabbits Exponential growth; limitations. Computer lab day, if possible. p. 315 10.A p. 111 10.B 10.B Is It Getting Crowded? Exponential growth; limitations p. 323 p. 113 You may wish to consider various configurations with the upcoming modeling lessons. For example, you may wish to consider having different groups complete and present the various logistic lessons or having some groups do logistic models while other groups do the periodic models. You may also choose to omit either logistic or periodic models. 11.A 11.A Oh, Deer! (Optional) Logistic models 11.B 11.B Population Growth (Optional) Time series model of logistic growth p. 331 11.A p. 115 p. 341 11.B p. 119 11.C 11.C Can You Hear Me Now? (Optional) Logistic models. Spreadsheet demonstration or computer lab day, if possible. p. 351 11.C p. 121 11.D 11.D Hares and Lynxes (Optional) Predator-prey 11.E 11.E Reindeer and Lichens (Optional) Effects of parameter choices on model predictions 12.A 12.A How Long Is the Longest Day? (Optional) Cyclical data 12.B 12.B What s My Sine? (Optional) Periodic functions p. 359 11.D p. 125 p. 369 11.E p. 129 p. 377 12.A p. 131 p. 389 12.B p. 135 page x
Title and Description 12.C 12.C SIR Disease (Optional) Effect of parameters on a model (epidemics) p. 397 12.C p. 139 12.D SIR (Continued) (Optional) Create a time-series model using a spreadsheet; assignment could be a mini-project. p. 407 p. 143 Statistical Studies 13.A 13.A Mind the Gap in Income Inequality Introductory vocabulary for statistical studies p. 415 13.A p. 145 13.B 13.B When in Rome... Observational and experimental studies and their conclusions p. 427 13.B p. 149 13.C 13.C A Worth Weighting For Sampling processes 13.D 13.D Weight... There s More! Evaluate and design sampling processes 14.A 14.A Blood Pressure and Bias Sampling and non-sampling error 14.B 14.B Taking Aim at Bias Types of bias 14.C 14.C Conclusions in Observational Studies Minimizing bias; appropriate conclusions 15.A 15.A The Video Game Diet Designing experimental studies; cause and effect 15.B 15.B All Things in Moderation Confounding variables 15.C 15.C The Power of the Pill Blinding; placebo effect; placebos 15.D 15.D Designing an Experiment Double blinding; blocking p. 437 13.C p. 151 p. 451 13.D p. 155 p. 463 14.A p. 159 p. 471 14.B p. 163 p. 479 14.C p. 167 p. 489 15.A p. 169 p. 497 15.B p. 171 p. 507 15.C p. 175 p. 515 15.D p. 179 page xi
15.E 15.E In Conclusion Culminating lesson on conclusions from statistical studies p. 527 15.E p. 183 Complex Quantitative Information and Graphical Displays You may wish to consider various configurations with the upcoming lessons on analyzing and writing about graphical displays. For example, you may wish to consider having different groups complete 16, Parts B, D, E, and F, and present to the class. 16.A 16.A Education Pays Analyzing stacked column graphs 16.B 16.B Looking for Links Analyzing comparative stacked columns graphs 16.C 16.C It s About Time! Building stacked columns graphs from class data 16.D 16.D Connecting the Dots Analyzing motion bubble charts 16.E 16.E Big Data (GIS) Analysis problems associated with large, volatile data 16.F 16.F Big Brother They re Watching Conclusions from heat maps 16.A 16.B 16.C 16.D 16.E 16.F 17.A 17.A Decisions, Decisions Decision making based on multiple pieces of quantitative information 17.A 17.B 17.B The Write Approach to Data Improving written analyses of graphical displays 17.C 17.C Numbers Never Lie Misleading and erroneous graphical displays 17.D 17.D Can You Feel the Heat? Using data to understand complex issues 17.C 17.D 18.A Mini-Project: Tornado Climatology Choosing appropriate ways to represent data 18.B 18.B What s Your Top Ten? Various ways to present mathematical models 18.C 18.C What a Wonderful World Using multiple representations to choose a model 18.B 18.C page xii
18.D 18.D Mathematical Models Limitations of models 18.D More from Probability and Statistics 19.A 19.A How Does Amazon Know What You Want? Probability and the area under a curve 19.B 19.B Applications of Probability Probability and histograms 19.C 19.C Heads I Win, Tails You Lose Random variables 19.D 19.D A Little Math is a Dangerous Thing Probability distribution functions 20.A 20.A Six Sigma (Optional) Using statistics for quality control 19.A 19.B 19.C 19.D 20.A 20.B 20.B That s Normal How changes in mean or standard deviation affect the normal curve 20.B 20.C 20.C More Normal The Empirical Rule 20.C 20.D 20.D Technology and the Normal Curve (Optional) Using technology to find probabilities of events that are normally distributed 20.D 21.A 21.A Poincare s Bread Using a sample mean to estimate a population mean 21.B 21.B Loads of Loaves Applying the Central Limit Theorem 21.C 21.C Expressing Confidence Introduction to confidence intervals 21.D 21.D Adjusting Confidences Margin of error 21.E 21.E Paths to Victory Poll results and levels of confidence 21.A 21.B 21.C 21.D 21.E page xiii
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