# 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 | ||

Campaign Guidelines: Food Habits Campaign | Food Habits - Guidelines | ||||

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 | ||

Campaign Guidelines Time Use Campaign | Time Use – Guideline | 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 Personality Color | Statistical questions, comparing distributions | 146 | ||

Section 2: How Likely is it? | 148 | ||||

Lesson 8: How Likely is 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 | ||

Campaign Guidelines Stress/Chill Campaign | Stress/Chill – Guideline | 90 | |||

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 |