I  Quantifying the World
1 Problem Solving By Asking Questions
 1.1 Why Data?
  1.1.1 Definitions and Formulas
  1.1.2 Worked Examples
  1.1.3 Exploration 1A: Assumptions get in the way
 1.2 Defining the Problem
  1.2.1 Definitions and Formulas
  1.2.2 Worked Examples
  1.2.3 Exploration 1B: Beef N’ Buns Service
 1.3 Homework
 1.4 Memo Problem: Carnivorous Cruise Lines
2 The Role of Data
 2.1 Extracting Data from the Problem Situation
  2.1.1 Definitions and Formulas
  2.1.2 Worked Examples
  2.1.3 Exploration 2A: Extracting Data at Beef n’ Buns
 2.2 Organizing data for Future Analysis
  2.2.1 Definitions and Formulas
  2.2.2 Worked Examples
  2.2.3 Exploration 2B: Entering Beef n’ Buns Data into a Spreadsheet
 2.3 Homework
 2.4 Memo Problem: Carnivorous Cruise Lines, Part 2
3 Using Models to Interpret Data
 3.1 The Mean As A Model
  3.1.1 Definitions and Formulas
  3.1.2 Worked Examples
  3.1.3 Exploration 3A: Wait Times at Beef n’ Buns
 3.2 Categorical Data and Means
  3.2.1 Definitions and Formulas
  3.2.2 Worked Examples
  3.2.3 Exploration 3B: Gender Discrimination Analysis with Pivot Tables
 3.3 Homework
 3.4 Memo Problem: Carnivorous Cruise Lines, Part 3
II  Analyzing Data Through Spatial Models
4 Box Plots
 4.1 What Does ”Typical” Mean?
  4.1.1 Definitions and Formulas
  4.1.2 Worked Examples
  4.1.3 Exploration 4A: Koduck Salary Increases
 4.2 Thinking inside the box
  4.2.1 Definitions and Formulas
  4.2.2 Worked Examples
  4.2.3 Exploration 4B: Relationships Among Data, Statistics, and Boxplots
 4.3 Homework
 4.4 Memo Problem: Matching Managers to a Company
5 Histograms
 5.1 Getting the Data to Fit a Common Ruler
  5.1.1 Definitions and Formulas
  5.1.2 Worked Examples
  5.1.3 Exploration 5A: Cool Toys for Tots
 5.2 Profiling Your Data
  5.2.1 Definitions and Formulas
  5.2.2 Worked Examples
  5.2.3 Exploration 5B: Beef n’ Buns Service Times
 5.3 Homework
 5.4 Memo Problem: Service at Beef n’ Buns
6 Interpreting Spatial Models
 6.1 Estimating Stats from Frequency Data
  6.1.1 Definitions and Formulas
  6.1.2 Worked Examples
  6.1.3 Exploration 6A: Data Summaries and Sensitivity
 6.2 Two Perspectives are Better than One
  6.2.1 Definitions and Formulas
  6.2.2 Worked Examples
  6.2.3 Exploration 6B: Stock Investment Decisions
 6.3 Homework
 6.4 Memo Problem: Portfolio Analysis
III  Analyzing Data Through Linear Models
7 Correlation
 7.1 Picturing Two Variable Relationships
  7.1.1 Definitions and Formulas
  7.1.2 Worked Examples
  7.1.3 Exploration 7A: Predicting the Price of a Home
 7.2 Fitting a Line to Data
  7.2.1 Definitions and Formulas
  7.2.2 Worked Examples
  7.2.3 Exploration 7B: Adding Trendlines
 7.3 Homework
 7.4 Memo Problem: Truck Maintenance Analysis
8 Simple Regression
 8.1 Modeling with Proportional Reasoning in Two Dimensions
  8.1.1 Definitions and Formulas
  8.1.2 Worked Examples
  8.1.3 Exploration 8A: Regression Modeling Practice
 8.2 Using and Comparing the Usefulness of a Proportional Model
  8.2.1 Definitions and Formulas
  8.2.2 Worked Examples
  8.2.3 Exploration 8B: How Outliers Influence Regression
 8.3 Homework
 8.4 Memo Problem: Commuter Rail Analysis
9 Multiple Regression Models
 9.1 Modeling with Proportional Reasoning in Many Dimensions
  9.1.1 Definitions and Formulas
  9.1.2 Worked Examples
  9.1.3 Exploration 9A: Production Line Data
 9.2 Modeling with Qualitative Variables
  9.2.1 Definitions and Formulas
  9.2.2 Worked Examples
  9.2.3 Exploration 9B: Maintenance Cost for Trucks
 9.3 Homework
 9.4 Memo Problem: Gender Discrimination
10 Is the Model Any Good
 10.1 Which coefficients are trustworthy?
  10.1.1 Definitions and Formulas
  10.1.2 Worked Examples
  10.1.3 Exploration 10A: Building a Trustworthy Model at EnPact
 10.2 More Complexity with Interaction Terms
  10.2.1 Definitions and Formulas
  10.2.2 Worked Examples
  10.2.3 Exploration 10B: Complex Gender Interactions at EnPact
 10.3 Homework
 10.4 Memo Problem: Truck Maintenance Expenses, Part 2
IV  Analyzing Data with Nonlinear Models
11 Nonlinear Models Through Graphs
 11.1 What if the Data is Not Proportional
  11.1.1 Definitions and Formulas
  11.1.2 Worked Examples
  11.1.3 Exploration 11A: Non-proportional data
 11.2 Transformations of Graphs
  11.2.1 Definitions and Formulas
  11.2.2 Worked Examples
  11.2.3 Exploration 11B: Shifting and Scaling the Basic Models
 11.3 Homework
 11.4 Memo Problem: DataCon Contract
12 Modeling with Nonlinear Data
 12.1 Non-proportional Regression Models
  12.1.1 Definitions and Formulas
  12.1.2 Worked Examples
  12.1.3 Exploration 12A: Learning and Production at Presario
 12.2 Interpreting a Non-proportional Model
  12.2.1 Definitions and Formulas
  12.2.2 Worked Examples
  12.2.3 Exploration 12B: What it means to be linear
 12.3 Homework
 12.4 Memo Problem: Insurance Costs
13 Multivariate Nonlinear Models
 13.1 Models with Numerical Interaction Terms
  13.1.1 Definitions and Formulas
  13.1.2 Worked Examples
  13.1.3 Exploration 13A: Revenue and Demand Functions
 13.2 Interpreting Quadratic Models in Several Variables
  13.2.1 Definitions and Formulas
  13.2.2 Worked Examples
  13.2.3 Exploration 13B: Exploring Quadratic Models
 13.3 Homework
 13.4 Memo Problem: Revenue Projections
V  Analyzing Data Using Calculus Models
14 Optimization
 14.1 Calculus with Powers and Polynomials
  14.1.1 Definitions and Formulas
  14.1.2 Worked Examples
  14.1.3 Exploration 14A: Finding the Derivative of a General Power Function
 14.2 Extreme Calculus!
  14.2.1 Definitions and Formulas
  14.2.2 Worked Examples
  14.2.3 Exploration 14B: Simple Regression Formulas
 14.3 Homework
 14.4 Memo Problem: Profit Analysis
15 Logarithmic and Exponential Models
 15.1 Logarithms and their derivatives
  15.1.1 Definitions and Formulas
  15.1.2 Worked Examples
  15.1.3 Exploration 15A: Logs and distributions of data
 15.2 Compound interest and derivatives of exponentials
  15.2.1 Definitions and Formulas
  15.2.2 Worked Examples
  15.2.3 Exploration 15B: Loan Amortization
 15.3 Homework
 15.4 Memo Problem: Loan Analysis
16 Optimization in Several Variables
 16.1 Constraints on Optimization
  16.1.1 Definitions and Formulas
  16.1.2 Worked Examples
  16.1.3 Exploration 16A: Setting up Optimization Problems
 16.2 Using Solver Table
  16.2.1 Definitions and Formulas
  16.2.2 Worked Examples
  16.2.3 Exploration 16B: Sensitivity Analysis
 16.3 Homework
 16.4 Memo Problem: Advertising Costs
17 Area Under a Curve
 17.1 Calculating the Area under a Curve
  17.1.1 Definitions and Formulas
  17.1.2 Worked Examples
  17.1.3 Exploration 17A: Numerical Integration
 17.2 Applications of the Definite Integral
  17.2.1 Definitions and Formulas
  17.2.2 Worked Examples
  17.2.3 Exploration 17B: Consumers’ and Producers’ Surplus at Market Equilibrium
 17.3 Homework
 17.4 Memo Problem: Pricing Dispute
VI  Appendices
A Professional Writing
B Sample Rubric for Evaluating Memo 7