Table of Contents

Table of Contents
Content					Page
Overview and Philosophy					8
Scope and Sequence					14
UNIT 1			Campaign	Topics
Daily Overview					19
Essential Concepts					20
	Section 1: Data are all Around				22
		Lesson 1: Data Trails		Defining data, consumer privacy	24
		Lesson 2: Stick Figures		Organizing & collecting data	26
		Lesson 3: Data Structures		Organizing data, rows & columns, variables	28
		Lesson 4: The Data Cycle		Data cycle, statistical investigative questions	30
		Lesson 5: So Many Questions		Statistical investigative questions, variability	35
		Lesson 6: What Do I Eat?	Food Habits	Data cycle, collecting data	39
		Lesson 7: Setting the Stage	Food Habits – data	Participatory Sensing	42
	Section 2: Visualizing Data				47
		Lesson 8: Tangible Plots	Food Habits – data	Dotplots, minimum/maximum, frequency	48
		Lesson 9: What Is Typical?	Food Habits – data	Typical value, center	52
		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	79
		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	89
		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	99
		Lesson 17: Interpreting Two-Way Tables	Time Use – data	Marginal & conditional relative frequencies	101
		Lab 1G: What’s the FREQ?	Time Use – data	2-way tables, tally	106
		Practicum: Teen Depression	Time Use	Statistical investigative questions, interpreting plots	109
		Lab 1H: Our Time		Data cycle, synthesis	111
		End of Unit Project: Analyzing Data to Evaluate Claims		Data cycle	112
UNIT 2			Campaign	Topics
Daily Overview					114
Essential Concepts					115
	Section 1: What is Your True Color?				117
		Lesson 1: What Is Your True Color?	Personality Color - data	Subsets, relative frequency	119
		Lesson 2: What Does Mean Mean?	Personality Color	Measures of center – mean	122
		Lesson 3: Median In the Middle	Personality Color	Measures of center – median	126
		Lesson 4: How Far Is It from Typical?	Personality Color	Measures of spread – MAD	129
		Lab 2A: All About Distributions	Personality Color	Measures of center & spread	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	Numerical summaries, custom functions	143
		Practicum: The Summaries	Food Habits or Time Use	Data cycle, comparing distributions	146
	Section 2: How Likely is it?				148
		Lesson 8: How Likely is It?		Probability, simulations	149
		Lesson 9: Bias Detective		Simulations to detect unfairness	152
		Lesson 10: Marbles, Marbles		Probability, with replacement	156
		Lab 2C: Which Song Plays Next?		Probability of simple events, do loops, set.seed()	158
		Lesson 11: This AND/OR That		Compound probabilities	161
		Lab 2D: Queue It Up!		Probability with/without replacement, sample()	165
		Practicum: Win, Win, Win		Probability estimation through repeated simulations	168
	Section 3: Are You Stressing or Chilling?				169
		Lesson 12: Don’t Take My Stress Away	Stress/Chill – data	Introduction to campaign	171
		Lesson 13: The Horror Movie Shuffle	Stress/Chill – data	Chance differences – categorical	175
		Lab 2E: The Horror Movie Shuffle	Stress/Chill – data	Inference for categorical variables	179
		Lesson 14: The Titanic Shuffle	Stress/Chill – data	Chance differences - numerical	182
		Lab 2F: The Titanic Shuffle	Stress/Chill – data	Inference for numerical variables	186
		Lesson 15: Tangible Data Merging	Stress/Chill – data	Merging datasets	188
		Lab 2G: Getting It Together	Stress/Chill & Personality Color	Stacking vs. joining datasets	190
		Practicum:What Stresses Us?	Stress/Chill & Personality Color	Analyzing merged data	192
	Section 4: What’s Normal?				193
		Lesson 16: What Is Normal?		Introduction to normal curve	194
		Lesson 17: A Normal Measure of Spread		Measures of spread - SD	197
		Lesson 18: What’s Your Z-Score?		z-scores, shuffling	200
		Lab 2H: Eyeballing Normal		Normal curves overlaid on distributions & simulated data	204
		Lab 2I: R’s Normal Distribution Alphabet		Normal probability, rnorm(), pnorm(), qnorm()	206
		End of Unit Project & Presentation: Asking and Answering Statistical Investigative Questions of Our Data	Stress/Chill, Personality Color, FoodHabits, or Time Use	Synthesis of Unit 2	208
UNIT 3			Campaign	Topics
Daily Overview					210
Essential Concepts					211
	Section 1: Testing, Testing…1, 2, 3…				213
		Lesson 1: Anecdotes vs. Data		Reading articles critically, data	215
		Lesson 2: What is an Experiment?		Experiments, causation	218
		Lesson 3: Let’s Try an Experiment!		Random assignments, confounding factors	221
		Lesson 4: Predictions, Predictions		Visualizations, predictions	223
		Lesson 5: Time Perception Experiment		Elements of an experiment	225
		Lab 3A: The Results Are In!		Analyzing experiment data	226
		Practicum: Music to my Ears		Design an experiment	227
	Section 2: Would You Look at That?				228
		Lesson 6: Observational Studies		Observational study	230
		Lesson 7: Observational Studies vs. Experiments		Observational study, experiment	232
		Lesson 8: Monsters that Hide in Observational Studies		Observational study, confounding factors	234
		Lab 3B: Confound it all!		Confounding factors	238
	Section 3: Are You Asking Me?				240
		Lesson 9: Survey Says…		Survey	241
		Lesson 10: We’re So Random		Data collection, random samples	244
		Lesson 11: The Gettysburg Address		Sampling bias	248
		Lab 3C: Random Sampling		Random sampling	253
		Lesson 12: Bias in Survey Sampling		Bias in survey sampling	255
		Lesson 13: The Confidence Game		Confidence intervals	258
		Lesson 14: How Confident Are You?		Confidence intervals, margin of error	261
		Lab 3D: Are You Sure about That?		Bootstrapping	263
		Practicum: Let’s Build a Survey!		Survey design with non-leading questions	266
	Section 4: What’s the Trigger?				267
		Lesson 15 Ready, Sense, Go!		Sensors, data collection	268
		Lesson 16: Does it have a Trigger?		Survey questions, sensor questions	271
		Lesson 17: Creating Our Own PS Campaign		Participatory Sensing campaign creation	273
		Lesson 18: Evaluating Our Own PS Campaign		Statistical investigative questions, evaluate campaign	276
		Lesson 19: Implementing Our Own PS Campaign	Class Campaign—data	Mock-implement & create campaign	278
	Section 5: Webpages				280
		Lesson 20: Online Data-ing	Class Campaign—data	Data on the internet	281
		Lab 3E: Scraping Web Data	Class Campaign—data	Scraping data from the Internet	284
		Lab 3F: Maps	Class Campaign—data	Making maps with data from the Internet	286
		Lesson 21: Learning to Love XML	Class Campaign—data	Data storage, XML	288
		Lesson 22: Changing Format	Class Campaign—data	Converting XML files	293
		Practicum: What Does Our Campaign Data Say?	Class Campaign	Statistical investigative questions, our data	296
		End of Unit Project: TB or Not TB	Class Campaign	Simulation using experiment data	297
UNIT 4			Campaign	Topics
Daily Overview					300
Essential Concepts					301
	Section 1: Campaigns and Community				303
		Lesson 1: Trash		Modeling to answer real world problems, official datasets	304
		Lesson 2: Drought		Exploratory data analysis, campaign creation	307
		Lesson 3: Community Connection	Team Campaign—data	Community topic research, campaign creation	309
		Lesson 4: Evaluate and Implement the Campaign	Team Campaign—data	Evaluate & mock-implement campaign	312
		Lesson 5: Refine and Create the Campaign	Team Campaign—data	Revise and edit campaign, data collection	314
	Section 2: Predictions and Models				315
		Lesson 6: Statistical Predictions Using One Variable	Team Campaign—data	One variable predictions using a rule	317
		Lesson 7: Statistical Predictions Applying the Rule	Team Campaign—data	Predictions applying MSE, MAE	319
		Lesson 8: Statistical Predictions Using Two Variables	Team Campaign—data	Two-variable statistical predictions	323
		Lesson 9: The Spaghetti Line	Team Campaign—data	Estimate line of best fit, linear regression	326
		LAB 4A: If the Line Fits…	Team Campaign—data	Estimate line of best fit	328
		Lesson 10: What’s the Best Line?	Team Campaign—data	Predictions based on linear models	330
		LAB 4B: What’s the Score?	Team Campaign—data	Comparing predictions to real data	333
		LAB 4C: Cross-Validation	Team Campaign—data	Use training and test data for predictions	335
		Lesson 11: What’s the Trend?	Team Campaign—data	Trend, associations, linear model	338
		Lesson 12: How Strong Is It?	Team Campaign—data	Correlation coefficient, strength of trend	342
		LAB 4D: Interpreting Correlations	Team Campaign—data	Correlation coefficient, best model	345
		Lesson 13: Improving Your Model	Team Campaign—data	Non-linear regression	348
		LAB 4E: Some Models Have Curves	Team Campaign—data	Non-linear regression	350
		Practicum: Predictions	Team Campaign—data	Linear regression	352
	Section 3: Piecing it Together				353
		Lesson 14: More Variables to Make Better Predictions	Team Campaign—data	Multiple linear regression	354
		Lesson 15: Combination of Variables	Team Campaign—data	Multiple linear regression	357
		LAB 4F: This Model Is Big Enough for All of Us	Team Campaign—data	Multiple linear regression	360
	Section 4: Decisions, Decisions!				361
		Lesson 16: Footbal or Futbol?	Team Campaign—data	Multiple predictors, classifying into groups	362
		Lesson 17: Grow Your Own Decision Tree	Team Campaign—data	Decision trees based on training/test data	368
		LAB 4G: Growing Trees	Team Campaign—data	Decision trees to classify observations	372
	Section 5: Ties That Bind				375
		Lesson 18: Where Do I Belong?	Team Campaign—data	Clustering, k-means	376
		LAB 4H: Finding Clusters	Team Campaign—data	Clustering, k-means	382
		Lesson 19: Our Class Network	Team Campaign—data	Clustering, networks	384
		End of Unit Modeling Activity Project	Team Campaign	Synthesis of Unit 4	387