Table of Contents

Table of Contents
Content Page
Overview and Philosophy 9
Scope and Sequence 16
UNIT 1
Campaign Topics
Daily Overview 22
Essential Concepts 23
Section 1: Data are all Around 25
Lesson 1: Data Trails Defining data, consumer privacy 27
Lesson 2: Stick Figures Organizing & collecting data 29
Lesson 3: Data Structures Organizing data, rows & columns, variables 31
Lesson 4: The Data Cycle Data cycle, statistical questions 34
Lesson 5: So Many Questions Statistical questions, variability 38
Lesson 6: What Do I Eat? Food Habits Collecting data, statistical questions 40
Lesson 7: Setting the Stage Food Habits – data Participatory sensing 43
Section 2: Visualizing Data 47
Lesson 8: Tangible Plots Food Habits – data Dotplots, minimum/maximum, frequency 49
Lesson 9: What Is Typical? Food Habits – data Typical value, center 53
Lesson 10: Making Histograms Food Habits – data Histograms, bin widths 55
Lesson 11: What Shape Are You In? Food Habits – data Shape, center, spread 58
Lesson 12: Exploring Food Habits Food Habits – data Single & multi-variable plots 60
Lesson 13: RStudio Basics Food Habits – data Intro to RStudio 62
Lab 1A: Data, Code & RStudio Food Habits – data RStudio basics 65
Lab 1B: Get the Picture? Food Habits – data Variable types, bar graphs, histograms 68
Lab 1C: Export, Upload, Import Importing data 71
Lesson 14: Variables, Variables, Variables Multi-variable plots 75
Lab 1D: Zooming Through Data Subsetting 80
Lab 1E: What’s the Relationship? Multi-variable plots 83
Practicum: The Data Cycle & My Food Habits Food Habits Data cycle, variability 86
Section 3: Would You Look at the Time 88
Lesson 15: Americans’ Time on Task Time Use – data Evaluating claims 90
Lab 1F: A Diamond In the Rough Time Use – data Cleaning names, categories, and strings 94
Lesson 16: Categorical Associations Time Use – data Joint relative frequencies in 2- way tables 98
Lesson 17: Interpreting Two-Way Tables Time Use – data Marginal & conditional relative frequencies 100
Lab 1G: What’s the FREQ? Time Use – data 2-way tables, tally 105
Practicum: Teen Depression Time Use Statistical questions, interpreting plots 107
Lab 1H: Our Time Data cycle, synthesis 109
End of Unit Project and Oral Presentation: Analyzing Data to Evaluate Claims Data cycle 110
UNIT 2 Campaign Topics
Daily Overview 112
Essential Concepts 113
Section 1: What is Your True Color? 115
Lesson 1: What Is Your True Color? Personality Color - data Subsets, relative frequency 117
Lesson 2: What Does Mean Mean? Personality Color Measures of center – mean 120
Lesson 3: Median In the Middle Personality Color Measures of center – median 124
Lesson 4: How Far Is It from Typical? Personality Color Measures of spread – MAD 128
Lab 2A: All About Distributions Personality Color Measures of center & spread – mean, median, MAD 132
Lesson 5: Human Boxplots Boxplots, IQR 134
Lesson 6: Face Off Comparing distributions 137
Lesson 7: Plot Match Comparing distributions 140
Lab 2B: Oh, the Summaries… Personality Color Boxplots, IQR, numerical summaries, custom functions 143
Practicum: The Summaries Food Habits or Time Use Statistical questions, comparing distributions 146
Section 2: How Likely is it? 148
Lesson 8: How LikelyIis It? Probability, simulations 150
Lesson 9: Bias Detective Simulations to detect bias 153
Lesson 10: Marbles, Marbles Probability, with replacement 157
Lab 2C: Which Song Plays Next? Probability of simple events, do loops, set.seed() 159
Lesson 11: This AND/OR That Compound probabilities 162
Lab 2D: Queue It Up! Probability with & without replacement, sample() 166
Practicum: Win, Win, Win Probability estimation through repeated simulations 169
Section 3: Are You Stressing or Chilling? 170
Lesson 12: Don’t Take My Stress Away Stress/Chill – data Introduction to campaign 172
Lesson 13: The Horror Movie Shuffle Stress/Chill – data Chance differences – cat var 176
Lab 2E: The Horror Movie Shuffle Stress/Chill – data Inference for categorical variable, do loops, shuffle() 180
Lesson 14: The Titanic Shuffle Stress/Chill – data Chance differences – num var 183
Lab 2F: The Titanic Shuffle Stress/Chill – data Inference for numerical variable, do loops, shuffle() 187
Lesson 15: Tangible Data Merging Stress/Chill – data Merging data sets 189
Lab 2G: Getting It Together Stress/Chill & Personality Color Merging data sets, stacking vs. joining 192
Practicum:What Stresses Us? Stress/Chill & Personality Color Answering statistical questions of merged data 194
Section 4: What’s Normal? 195
Lesson 16: What Is Normal? Introduction to normal curve 197
Lesson 17: Normal Measure of Spread Measures of spread - SD 201
Lesson 18: What’s Your Z-Score? z-scores, shuffling 204
Lab 2H: Eyeballing Normal Normal curves overlaid on distributions & simulated data 220
Lab 2I: R’s Normal Distribution Alphabet 212
End of Unit Project: Asking and Answering Statistical Questions of Our Own Data Stress/Chill, Personality Color, Habits, or Time Use Synthesis of above 214
UNIT 3 Campaign Topics
Daily Overview 216
Essential Concepts 217
Section 1: Testing, Testing…1, 2, 3… 219
Lesson 1: Anecdotes vs. Data Reading articles critically, data 221
Lesson 2: What is an Experiment? Experiments, causation 224
Lesson 3: Let’s Try an Experiment! Random assignments, confounding factors 227
Lesson 4: Predictions, Predictions Visualizations, predictions 229
Lesson 5: Time Perception Experiment Elements of an experiment 231
Lab 3A: The results are in! Analyzing experiment data 233
Practicum: Music to my Ears Design an experiment 234
Section 2: Would You Look at That? 235
Lesson 6: Observational Studies Observational study 237
Lesson 7: Observational Studies vs. Experiments Observational study, experiment 239
Lesson 8: Monsters that Hide in Observational Studies Observational study, confounding factors 241
Lab 3B: Confound it all! Confounding factors 245
Section 3: Are You Asking Me? 247
Lesson 9: Survey Says… Survey 249
Lesson 10: We’re So Random Data collection, random samples 252
Lesson 11: The Gettysburg Address Sampling bias 256
Lab 3C: Random Sampling Random sampling 261
Lesson 12: Bias in Survey Sampling Bias, sampling methods 263
Lesson 13: The Confidence Game Confidence intervals 266
Lesson 14: How Confident Are You? Confidence intervals, margin of error 269
Lab 3D: Are You Sure about That? Bootstrapping 271
Practicum: Let’s Build a Survey! Non-biased survey design 274
Section 4: What’s the Trigger? 275
Lesson 15 Ready, Sense, Go! Sensors, data collection 277
Lesson 16: Does it have a Trigger? Survey questions, sensor questions 280
Lesson 17: Creating Our Own Participatory Sensing Campaign Participatory sensing campaign creation 283
Lesson 18: Evaluating Our Own Participatory Sensing Campaign Statistical questions, evaluate campaign 286
Lesson 19: Implementing Our Own Participatory Sensing Campaign Class Campaign—data Mock-implement campaign, campaign creation, data collection 288
Section 5: Webpages 290
Lesson 20: Online Data-ing Class Campaign—data Data on the internet 292
Lab 3E: Scraping web data Class Campaign—data Scraping data from the internet 296
Lab 3F: Maps Class Campaign—data Making maps with data from the internet 299
Lesson 21: Learning to Love XML Class Campaign—data Data storage, XML 301
Lesson 22: Changing Orientation Class Campaign—data Converting XML files 303
Practicum: What Does Our Campaign Data Say? Class Campaign Statistical questions, visualizations, numerical summaries 305
End of Unit Project: TB or Not TB Class Campaign Simulation using experiment data 306
UNIT 4 Campaign Topics
Daily Overview 309
Essential Concepts 311
Section 1: Testing, Testing…1, 2, 3… 313
Lesson 1: Water Usage Data cycle, official data sets 315
Lesson 2: Exploring Water Usage Exploratory data analysis, campaign creation 319
Lesson 3: Evaluating and Implementing a Water Campaign Water Campaign—data Statistical questions, evaluate & mock implement campaign 321
Lesson 4: Refining the Water Campaign Water Campaign—data Revise and edit campaign, data collection 323
Lesson 5: Statistical Predictions Using One Variable Water Campaign—data One-variable predictions using a rule 325
Lesson 6: Statistical Predictions by Applying the Rule Water Campaign—data Predictions applying mean square deviation, mean absolute error 328
Lesson 7: Statistical Predictions Using Two Variables Water Campaign—data Two-variable statistical predictions, scatterplots 333
LAB 4A: If the Line Fits… Water Campaign—data Estimate line of best fit 335
LAB 4B: What’s the Score? Water Campaign—data Comparing predictions to real data 337
Lesson 8: What’s the Trend? Water Campaign—data Trend, associations, linear model 339
Lesson 9: Spaghetti Line Water Campaign—data Estimate line of best fit, single linear regression 343
LAB 4C: Cross-Validation Water Campaign—data Use training and testing data for predictions 346
Lesson 10: Predicting Values Water Campaign—data Predictions based on linear models 348
Lesson 11: How Strong Is It? Water Campaign—data Correlation coefficient, strength of trend 351
LAB 4D: Interpreting Correlations Water Campaign—data Use correlation coefficient to determine best model 353
Section 2: Piecing It Together 356
Lesson 12: More Variables to Make Better Predictions Water Campaign—data Multiple linear regression 358
Lesson 13: Combination of Variables Water Campaign—data Multiple linear regression 361
LAB 4E: This Model Is Big Enough for All of Us Water Campaign—data Multiple linear regression 364
Practicum: Predictions Water Campaign—data Linear regression 365
Lesson 14: Improving Your Model Water Campaign—data Non-linear regression 366
LAB 4F: Some Models Have Curves Water Campaign—data Non-linear regression 368
Section 3: The Growth of Landfills 370
Lesson 15: The Growth of Landfills Water Campaign—data Modeling to answer realworld problems 372
Lesson 16: Exploring Trash via the Dashboard Water Campaign—data Analyze data to improve models 376
Lesson 17: Exploring Trash via RStudio Water Campaign—data Analyze data to improve models 377
Prepare Team Presentations Water Campaign—data Modeling with statistics -
Present Team Recommendations Water Campaign—data Modeling with statistics -
Section 4: Decisions, Decisions! 378
Lesson 18: Grow Your Own Decision Tree Water Campaign—data Multiple predictors, classifying into groups, decision trees 380
Lesson 19: Data Scientists or Doctors? Water Campaign—data Decision trees based on training and testing data 385
LAB 4G: Growing Trees Water Campaign—data Decision trees to classify observations 388
Section 5: Ties That Bind 390
Lesson 20: Where Do I Belong? Water Campaign—data Clustering, k-means 392
LAB 4H: Finding Clusters Water Campaign—data Clustering, k-means 397
Lesson 21: Our Class Network Water Campaign—data Clustering, networks 399
End of Unit 3 and 4 Design Project and Oral Presentations: Water Usage
Water Campaign Synthesis of above 403