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: Campaigns and Community				313
		Lesson 1: Trash		Modeling to answer real world problems, official data sets	315
		Lesson 2: Drought		Exploratory data analysis, campaign creation	319
		Lesson 3: Community Connection	Team Campaign—data	Community topic research, campaign creation	321
		Lesson 4: Evaluate and Implement the Campaign	Team Campaign—data	Statistical questions, evaluate & mock implement campaign	323
		Lesson 5: Refine and Create the Campaign	Team Campaign—data	Revise and edit campaign, data collection	325
	Section 2: Predictions and Models				326
		Lesson 6: Statistical Predictions Using One Variable	Team Campaign—data	One-variable predictions using a rule	328
		Lesson 7: Statistical Predictions by Applying the Rule	Team Campaign—data	Predictions applying mean square deviation, mean absolute error	333
		Lesson 8: Statistical Predictions Using Two Variables	Team Campaign—data	Two-variable statistical predictions, scatterplots	335
		Lesson 9: The Spaghetti Line	Team Campaign—data	Estimate line of best fit, single linear regression	337
		LAB 4A: If the Line Fits…	Team Campaign—data	Estimate line of best fit	339
		Lesson 10: What’s the Best Line?	Team Campaign—data	Predictions based on linear models	343
		LAB 4B: What’s the Score?	Team Campaign—data	Comparing predictions to real data	345
		LAB 4C: Cross-Validation	Team Campaign—data	Use training and test data for predictions	346
		Lesson 11: What’s the Trend?	Team Campaign—data	Trend, associations, linear model	348
		Lesson 12: How Strong Is It?	Team Campaign—data	Correlation coefficient, strength of trend	351
		LAB 4D: Interpreting Correlations	Team Campaign—data	Use correlation coefficient to determine best model	353
		Lesson 13: Improving Your Model	Team Campaign—data	Non-linear regression	355
		LAB 4E: Some Models Have Curves	Team Campaign—data	Non-linear regression	357
	Section 3: Piecing it Together				358
		Lesson 14: More Variables to Make Better Predictions	Team Campaign—data	Multiple linear regression	359
		Lesson 15: Combination of Variables	Team Campaign—data	Multiple linear regression	361
		LAB 4F: This Model Is Big Enough for All of Us	Team Campaign—data	Multiple linear regression	364
		Practicum: Predictions	Team Campaign—data	Linear regression	365
	Section 4: Decisions, Decisions!				368
		Lesson 16: Footbal or Futbol?	Team Campaign—data	Multiple predictors, classifying into groups, decision trees	369
		Lesson 17: Grow Your Own Decision Tree	Team Campaign—data	Decision trees based on training and test data	371
		LAB 4G: Growing Trees	Team Campaign—data	Decision trees to classify observations	374
	Section 5: Ties That Bind				378
		Lesson 18: Where Do I Belong?	Team Campaign—data	Clustering, k-means	379
		LAB 4H: Finding Clusters	Team Campaign—data	Clustering, k-means	382
		Lesson 19: Our Class Network	Team Campaign—data	Clustering, networks	385
		End of Unit 4 Modeling Activity Project and Presentations: Community Issue	Team Campaign	Synthesis of above	388