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+
+
+FILE: ./notebooks/01-publish/00-Python-Primer.ipynb
+   #  Python Primer
+   ##  Why Python?
+    WARNING: https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2019 is not reachable, status_code: 404
+   ##  Chapter Learning Objectives
+   ##  Activities
+
+
+FILE: ./notebooks/01-publish/01-Jupyter-Notebooks.ipynb
+   #  Welcome to Jupyter Notebooks and Vocareum
+   ##  Quick Python Introduction
+   ###  Variables
+   ###  Execution Order
+   ##  Vocareum Autograder
+   ###  Our First Autograded Submission
+
+
+FILE: ./notebooks/01-publish/02-Python-Basics-I-Variables-Strings-Bugs.ipynb
+   #  Python Basics I: Variables, Strings, and Bugs
+   ##  Learning Objectives
+   ##  Hello World and Interactive Sessions
+   ###  Command Line Mode
+   ##  Comments
+   ##  Errors
+   ###  "Good" Bugs
+   ###  "Bad" Bugs
+   ##  Indentation
+   ##  Input
+   ##  Variables
+   ###  Integers
+   ###  Floating Point Numbers
+   ###  Built-in Mathematical Functions
+   ##  Strings and Overloading
+
+
+FILE: ./notebooks/01-publish/03-Flow-control.ipynb
+   #  Python Basics II: Loopy Logic
+   ##  Learning Objectives
+   ##  If Statements
+   ##  Iteration with While Loops
+   ##  For Loops
+
+
+FILE: ./notebooks/01-publish/03b-Pseudocode.ipynb
+   #  Pseudocode
+   ##  Learning Objectives
+   ##  Pseudocode
+   ##  Estimate $\pi$ by throwing darts
+   ###  Governing Formula
+   ###  Pseudocode
+   ####  Python Implementation
+   ####  Python
+   ###  Using Help to Learn More About a Function
+   ###  Another Solution Approach - With Plots
+   ##  TODO: Add another pseudocode example
+
+
+FILE: ./notebooks/01-publish/04-Functions-and-Scope.ipynb
+   #  Functions and Scope
+   ##  Learning Objectives
+   ##  Functions
+   ###  Calling functions
+   ###  Return Values
+   ###  Docstrings and Help
+   ##  Scope
+
+
+FILE: ./notebooks/01-publish/04b-Recursion.ipynb
+   #  Recursion
+   ##  Learning Objectives
+   ##  Recursion
+    ERROR: http://justinwhite.com/big-calc/1000.html HTTPConnectionPool(host='justinwhite.com', port=80): Max retries exceeded with url: /big-calc/1000.html (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7ff3b924e310>, 'Connection to justinwhite.com timed out. (connect timeout=10)'))
+
+
+FILE: ./notebooks/01-publish/04c-Modules-and-Files.ipynb
+   #  Functions, Scoping, and Other Fun Stuff
+   ##  Learning Objectives
+   ##  Modules
+   ##  Files
+
+
+FILE: ./notebooks/01-publish/04d-Example-High-Low-Game.ipynb
+   #  High/Low Guess My Number Game
+   ##  Your Function
+   ##  Test 1
+   ##  Test 2
+
+
+FILE: ./notebooks/01-publish/05-Python-Basics-III-Lists-Dictionaries-Enumeration.ipynb
+   #  Python Basics III: Lists, Dictionaries, and Enumeration
+   ##  Learning Objectives
+   ##  Lists
+   ##  Dictionaries
+   ###  Example: Shopping List
+   ###  Another Example: Chemical Symbols and Element Names
+   ###  Dictionary of Dictionaries
+   ##  Enumerate
+   ###  Lists
+   ###  Dictionaries
+
+
+FILE: ./notebooks/01-publish/06-NumPy.ipynb
+   #  Linear Algebra with Numpy and Scipy
+   ##  Learning Objectives
+   ##  NumPy Arrays
+   ###  Getting Started
+   ###  Creating Arrays in Neat Ways
+   ###  Operations on Arrays
+   ###  Universal Functions
+   ###  Copying Arrays
+   ###  Indexing, Slicing, and Iterating
+   ###  Complex Numbers
+   ###  Integrating Everything Together
+
+
+FILE: ./notebooks/01-publish/07-Matplotlib.ipynb
+   #  Visualization with matplotlib
+   ##  Learning Objectives
+   ##  Matplotlib Basics
+   ###  Customizing Plots
+   ###  Plotting multiple lines
+   ##  Publication Quality Figures
+
+
+FILE: ./notebooks/01-publish/08-Pandas.ipynb
+   #  Manipulating Data with Pandas
+   ##  Learning Objectives
+   ##  Working with Data Using Pandas
+    WARNING: https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html is not reachable, status_code: 404
+   ###  Loading and Inspecting Data
+   ###  Computing Summary Statistics
+   ###  Combining Pandas and Matplotlib
+   ##  Investment Strategies
+   ###  Getting Started
+   ###  Portfolio Calculator
+
+
+FILE: ./notebooks/01-publish/09-Functions-as-Arguments.ipynb
+   #  Functions as Arguments
+   ##  Learning Objectives
+   ##  Functions passed to Functions
+   ##  Lambda Functions
+   ##  Tying it all together: Midpoint Integration
+   ###  Convergence Analysis
+   ###  A Fancier Integration Function
+   ###  Incorporating Lambda Functions with Midpoint Integration
+   ###  Extension to Two Dimensional Functions
+
+
+FILE: ./notebooks/01-publish/10-Testing-and-Debugging.ipynb
+   #  Testing and Debugging in Python
+   ###  Learning Objectives
+   ##  Testing Your Code
+   ###  Motivating Example
+   ###  Test 0: Takes Inputs as Advertised?
+   ###  Test 1: Initial Condition
+   ###  Test 2: Asymptotics
+   ###  Test 3: Check a Few Inputs
+   #  Debugging
+   ###  Debugging is a Mindset
+   ###  Test 2
+   ###  Test 3
+   #  Assertions
+   #  Error Handling
+
+
+FILE: ./notebooks/02-publish/01-Modeling-Systems-of-Linear-Equations.ipynb
+   #  Modeling Systems of Linear Equations
+   ##  Learning Objectives
+   ##  Introduction
+   ##  Environmental Controls for a Power Plant
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/wet_fgd3.png is not reachable, status_code: 404
+   ###  Determine Additional Data and Analysis Goal
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/wfgd-table.png is not reachable, status_code: 404
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/henrys_law2.png is not reachable, status_code: 404
+   ###  Draw a Picture
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/single_stage_model.png is not reachable, status_code: 404
+   ###  State Assumptions
+   ###  Mathematical Model
+   ###  Degree of Freedom Analysis
+   ###  Revised Model
+   ###  Calculations
+   ###  Final Result and Discussion
+   ##  FGD Revisited with Linear Algebra
+   ###  Rewrite Model as a Linear System
+   ###  Solve Linear System with Python
+   ###  Perform Sensitivity Analysis to Determine L
+
+
+FILE: ./notebooks/02-publish/02-Gauss-Elimination.ipynb
+   #  Gaussian Elimination
+   ##  Learning Objectives
+   ##  Linear Algebra: Invertible Matrix Theorem
+   ##  Motivating Example and Gaussian Elimination
+   ###  Gaussian Elimination: Take Care of First Column
+   ###  Gaussian Elimination: Take Care of Second Column
+   ##  Motivating Example and Back Substitution
+   ###  Verify Solution
+   ##  Generalized Gaussian Elimination
+   ###  Pseudocode
+   ###  Python Implementation
+   ###  Unit Test: Motivating Example
+   ##  Generalized Back Substitution
+   ###  Pseudocode
+   ###  Python Implementation
+   ##  Additional Testing: Random Linear Systems
+   ##  Round off and Pivoting
+   ###  Swapping Rows
+   ###  Selecting Pivot
+   ###  Put it All Together
+   ###  Unit Test: Motivating Example
+   ###  Unit Test: Troublesome System
+   ##  Timing
+   ##  Mass Balance Example (Adapted from an Old Exam Problem)
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/1b_fig.png is not reachable, status_code: 404
+   ###  Mathematical Model (Old Exam Question)
+   ###  Matrix Form (Old Exam Question)
+   ###  Solve with Gaussian Elimination
+   ###  Flip the order of the row
+   ###  Replace the 8th equation with an overall mole balance
+
+
+FILE: ./notebooks/02-publish/03-LU-Factorization.ipynb
+   #  LU Decomposition and Condition Number
+   ##  Learning Objectives
+   ##  Motivation: Solving the "Same" System Multiple Times
+   ##  LU Factorization
+   ###  Our First Function for LU Factorization
+   ###  A More Memory Efficient Version
+   ##  Solving Linear Systems
+   ###  Our First Function for Forward and Backward Solves
+   ###  Another Implementation (Optional)
+   ##  LU with Pivoting
+   ##  Errors in Linear Systems
+   ###  Rank of the Matrix
+   ####  Example 2
+   ####  Example 3
+   ###  Singular Value Decomposition
+   ###  Condition Number
+   ##  Take Home Messages
+
+
+FILE: ./notebooks/02-publish/04-Example-Atomic-Mass-Balances.ipynb
+   #  Example: Mass Balances and Linear Algebra
+   ##  Learning Objectives
+   ##  Atomic Species (Mass) Balance
+
+
+FILE: ./notebooks/03-publish/01-Taylor-Series.ipynb
+   #  Taylor Series Approximations
+   ##  Learning Objectives
+   ##  Motivation
+   ###  Class Roadmap
+   ##  Calculus Revisited: Derivatives and Taylor Series
+   ###  The definition of a derivative
+   ###  Taylor Series and Big-O notation
+   ###  Example: Approximating Cosine
+
+
+FILE: ./notebooks/03-publish/02-Finite-Difference.ipynb
+   #  Finite Difference Derivative Approximations
+   ##  Learning Objectives
+   ##  Forward Finite Difference
+   ##  Backward Finite Difference
+   ###  Example to Compare the Approximations
+   ##  Higher-order Approximations
+   ###  Central Difference Formula
+   ###  Comparison of the approximations
+   ##  Truncation and Random Errors
+   ###  Forward Difference
+   ###  Backward Difference
+   ###  Central Difference
+   ###  Second (and Higher) Derivatives
+
+
+FILE: ./notebooks/03-publish/03-Example-Heating-a-Metal-Slab.ipynb
+   #  Example: Heating a Metal Slab
+   ##  Learning Objectives
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/metal-slab.png is not reachable, status_code: 404
+   ###  Algorithm (Truncation) Error
+   ###  Random Error
+   ###  Optimal Location
+   ###  Visualize the Results
+
+
+FILE: ./notebooks/04-publish/01-Modeling-Systems-of-Nonlinear-Equations.ipynb
+   #  Modeling Systems of Nonlinear Equations
+   ##  Learning Objectives
+   ##  Flash Calculations
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/flash-system.png is not reachable, status_code: 404
+   ###  Full Mathematical Model
+   ###  Solve Nonlinear System with Python
+
+
+FILE: ./notebooks/04-publish/02-Newton-Method-Nonlinear-Equation-Solving.ipynb
+   #  Solving Systems of Nonlinear Equations with Newton's Method
+   ##  Learning Objectives
+   ##  Motivation
+   ##  Newton's Method (aka Newton-Raphson Method) with an Example
+   ###  Main Idea Through an Example
+   ###  Algorithm Steps and Iteration 1
+   ###  Convergence
+   ###  Iteration 2
+   ##  Newton's Method Generalized
+   ###  Generalization
+   ###  Algorithm and Implementation
+   ###  Unit Test and Example
+   ###  Looking Deeper into the Unit Test
+   ####  Iteration 1
+   ####  Iteration 2
+   ####  Iteration 3
+
+
+FILE: ./notebooks/04-publish/03-More-Newton-Type-Methods.ipynb
+   #  More Newton-Type Methods
+   ##  Learning Objectives
+   ##  Inexact Newton
+   ###  Unit Test
+   ##  Secant Method
+   ###  Iteration 1
+   ###  Iteration 2
+   ###  Secant Method Algorithm
+
+
+FILE: ./notebooks/04-publish/04-Convergence-Analysis-Newton-Method.ipynb
+   #  Convergence Analysis for Newton's Method
+   ##  Learning Objectives
+   ##  Examples of slow convergence
+   ###  Example 1: Multiple Roots, overlapping
+   ###  Example 2: Multiple roots, spaced far apart
+   ###  Example 3: Complex roots near the real root
+   ####  Iteration 1
+   ####  Iteration 2
+   ####  Iteration 3
+   ###  Iteration 4
+
+
+FILE: ./notebooks/04-publish/05-System-of-Equations-Newton-Method.ipynb
+   #  Newton's Method for Systems of Equations
+   ##  Learning Objectives
+   ###  Motivation: Flash Problem Revisited
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/flash-system.png is not reachable, status_code: 404
+   ###  Extending Newton's Method to Multiple Dimensions
+   ###  Two Phase, Single Feed Phase Problem
+   ####  Convert to Canonical Form
+   ####  Assemble Jacobian Matrix (Analytic)
+   ####  First Iteration
+   ###  Finite Difference to Approximate the Jacobian Matrix
+   ###  Putting it all together: Multivariate Newton's Method
+   ###  Return to the Two Phase Flash Calculation
+   ###  Now let's break it
+   ###  Using ``numpy`` instead
+
+
+FILE: ./notebooks/05-publish/01-Pyomo-Basics.ipynb
+   #  Pyomo Basics
+   ##  Learning Objectives
+   ##  Overview
+    WARNING: https://www.sciencedirect.com/science/article/pii/S0098135404001565?casa_token=vO761nMJ9CwAAAAA:rThzdy07Zgoi9Y5Bd7soAJnwbILWHQZ2zzN4rsvujeWjEyyfew_rXVgbAuuYdTGrDKbj9-JbLgQ is not reachable, status_code: 403
+    WARNING: https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/prs.12103?casa_token=t7j78jzYFyMAAAAA:MDnYJdgSrlNsniudvbMQEdo8gqBLOnYMK06yCgyMsVnX3Fm8BLo2G8b3RU_gZyFFg4bUrFVwRYKrkBg is not reachable, status_code: 503
+   ##  Constrained Nonlinear Optimization: Canonical Form
+   ##  Pyomo in an Example
+   ###  Declare Model
+   ###  Solve Numerically
+   ###  Visualize Solution
+   ##  Example: Planting Cherry Trees
+   ###  Step 1: Develop Mathematical Model, Convert to Canonical Form
+   ###  Step 2: Solve in Python
+   ###  Interpret Answer
+
+
+FILE: ./notebooks/05-publish/02-Electricity-Market-Optimization.ipynb
+   #  Electricity Market Optimization
+   ##  Learning Objectives
+   ##  Optimal Operation of Battery Energy Storage
+   ###  Visualize Price Data
+   ###  Create Mathematical Model
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/battery.png is not reachable, status_code: 404
+   ###  Define Pyomo Model
+   ###  Solve Optimization Model
+   ###  Extract Solution from Pyomo
+   ###  Plot Results
+   ###  How much money can a 4 MWh battery make in a year?
+
+
+FILE: ./notebooks/05-publish/03-Flash-Calculations-in-Pyomo.ipynb
+   #  Flash Calculations in Pyomo
+   ##  Learning Objectives
+   ##  Flash Example Revisited
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/flash-system.png is not reachable, status_code: 404
+
+
+FILE: ./notebooks/06-publish/01-Intro-and-Newton-Cotes.ipynb
+   #  Introduction and Newton-Cotes
+   ##  Learning Objectives
+   ##  Motivating Example and Main Idea
+   ###  Example: Integrate Normal Distribution
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/sat.jpg is not reachable, status_code: 404
+   ###  Example: Let's Revisit that Unbounded Assumption
+   ###  Main Idea
+   ##  Newton-Cotes Formulas
+   ###  1) Midpoint Rule
+   ###  2) Trapezoid Rule
+   ####  Trapezoid Rule with Multiple Pieces
+   ####  General Purpose Function and Test Case
+   ####  Error Analysis
+   ###  Simpson's Rule
+   ####  Quadratic Interpolation
+   ####  General Purpose Function and Test Case
+   ####  Error Analysis
+   ####  Another Example: Approximating $\pi$
+
+
+FILE: ./notebooks/06-publish/02-Gauss-Quadrature.ipynb
+   #  Gauss Quadrature
+   ##  Learning Objectives
+   ##  Gauss Quadrature Rules
+   ####  Integral Bounds Transformation
+   ####  Transformation Example
+   ###  Integrate a Linear Function
+   ###  Weights and Nodes
+   ####  Where did these points come from? (optional)
+   ###  Code for Gauss-Legendre Quadrature
+   ###  Visualize Quadrature Weights
+   ###  Polynomial Example
+   ###  Sine Example Revisited
+   ###  A More Complicated Example
+
+
+FILE: ./notebooks/06-publish/03-Adaptive-Methods-and-Scipy.ipynb
+   #  Adaptive Methods for Newton-Cotes and Gauss Quadrature, Scipy Library
+   ##  Learning Objectives
+   ##  Adaptive Methods Overcome Certain Limitations
+   ###  Scipy Documentation
+
+
+FILE: ./notebooks/06-publish/04-Application-Inertial-Navigation-Systems.ipynb
+   #  Application: Inertial Navigation Systems
+   ##  Learning Objectives
+   ##  Appliction: Inertial Navigation Systems
+   ###  Preliminary Analysis
+   ###  Free Body Diagram & Acceleration
+   ###  Velocity
+   ###  Position
+
+
+FILE: ./notebooks/06-publish/05-Forward-and-Backward-Euler.ipynb
+   #  Forward and Backward Euler Methods
+   ##  Learning Objectives
+   ##  Main Idea
+   ##  Forward (Explicit) Euler
+   ###  Main Idea
+   ###  Generalization and Python Implementation
+   ###  Test Problem
+   ###  Impact of Step Size on Integration Error
+   ##  Backward (Implicit) Euler
+   ###  Main Idea
+   ###  Python Implementation
+   ###  Impact of Step Size on Integration Error
+
+
+FILE: ./notebooks/06-publish/06-Trapezoid-Rule.ipynb
+   #  Crank-Nicolson (Trapezoid Rule)
+   ##  Learning Objectives
+   ##  Main Idea
+   #  Crank-Nicolson (aka Trapezoid Rule)
+   ###  Main Idea
+   ###  Python Implementation
+   ###  Impact of Step Size on Integration Error
+   ###  Stability and Oscillations
+
+
+FILE: ./notebooks/06-publish/07-Stability-Analysis.ipynb
+   #  Stability Analysis of Forward/Backward Euler and Crank-Nicolson Methods
+   ##  Learning Objectives
+   ##  Comparison of Forward/Backward Euler and Crank-Nicolson on a more complicated problem
+   ##  Stability
+   ###  Forward (Explicit) Euler Stability Analysis
+   ####  Solving for the Growth Rate to Determine Stability
+   ###  Backward (Implicit) Euler Stability Analysis
+   ####  Solving for the Growth Rate to Deterimine Stability
+   ###  Crank-Nicolson (Trapezoid) Method
+   ####  Solving for the Growth Rate to Determine Stability
+
+
+FILE: ./notebooks/06-publish/08-Explicit-Runge-Kutta.ipynb
+   #  Explicit Range Kutta Method
+   ##  Learning Objectives
+   ##  Fourth-order (Explicit) Runge-Kutta Method
+
+
+FILE: ./notebooks/06-publish/09-Systems-of-Differential-Equations-and-Scipy.ipynb
+   #  Systems of Differential Equations and Scipy
+   ##  Learning Objectives
+   ##  Systems of Differential Equations
+   ###  Forward (Explicit) Euler
+   ###  Backward (Explicit) Euler
+   ###  Crank-Nicolson
+   ###  Runge-Kutta
+   ##  Scipy
+
+
+FILE: ./notebooks/06-publish/10-Example-Reaction-Rates.ipynb
+   #  Example: Reaction Rates
+   ##  Learning Objectives
+   ###  Linear Differential Equation System
+   ###  Linear Differential Equation System
+   ###  Numerically Integrate using Class Implementation
+   ####  Forward Euler
+   ####  Backward Euler
+   ####  Crank-Nicolson
+   ####  Runge-Kutta
+   ###  Numerically Integrate using Scipy
+
+
+FILE: ./notebooks/07-publish/01-Sampling.ipynb
+   #  Sampling
+   ##  Learning Objectives
+   ##  Sampling
+   ###  Conceptual versus Tangible Populations
+   ###  Independence
+
+
+FILE: ./notebooks/07-publish/02-Summary-Statistics.ipynb
+   #  Summary Statistics
+   ##  Learning Objectives
+   ##  Summary Statistics
+   ###  Two Types of Data: Numerical and Categorical
+   ###  Sample Mean
+   ###  Sample Variance and Standard Deviation
+   ###  Sample Median
+   ###  Sample Mode and Range
+   ###  Quartiles and Percentiles
+   ###  Multivariate Data Example: Exam 1 Scores
+   ###  Sample Covariance
+   ###  Summary Statistics for Categorical Variables
+   ###  Summary Statistics and Population Parameters
+
+
+FILE: ./notebooks/07-publish/03-Visualizing-Data.ipynb
+   #  Visualizing Data
+   ##  Learning Objectives
+   ##  Visualizing Data
+   ###  Scatter Plots
+   ###  Example: Old Faithful
+   ###  Histogram
+   ###  Boxplot
+    WARNING: https://miro.medium.com/max/18000/1 is not reachable, status_code: 404
+   ###  Visualization Practice: Exam Scores
+
+
+FILE: ./notebooks/08-publish/01-Random-Variables.ipynb
+   #  Random Variables
+   ##  Data Analysis Intro
+   ##  Learning Objectives
+   ##  Statistics: Using Data to ...
+   ###  1. Describe the World
+   ###  2. Draw Conclusions (Inferences)
+   ###  3. Make Decisions Under Uncertainty
+   ##  Random Variables
+   ###  Example for Discrete Random Variables: Flipping Two Independent Coins
+   ###  Another Example: Continous Random Variables
+   ####  Uniform Distribution
+
+
+FILE: ./notebooks/08-publish/02-Jointly-Distributed-Random-Variables.ipynb
+   #   Jointly Distributed Random Variables
+   ##  Learning Objectives
+   ##  Jointly Distributed Random Variables
+   ###  Example: Independent Random Variables
+   ####  Simulating Two Dependent/Correlated Coins
+
+
+FILE: ./notebooks/08-publish/03-Practice-Problems.ipynb
+   #  Practice Problems
+   ##  Basic Ideas of Probability
+   ###  Practice Problem 2.1-A
+   ###  Practice Problem 2.1-B
+   ###  Practice Problem 2.1-C
+   ##  Counting Methods
+   ###  Practice Problem 2.2-A
+   ###  Practice Problem 2.2-B
+   ###  Practice Problem 2.2-C
+   ###  Practice Problem 2.2-D
+   ###  Practice Problem 2.2-E
+   ##  Conditional Probability and Independence
+   ###  Practice Problem 2.3-A
+   ###  Practice Problem 2.3-B
+   ###  Practice Problem 2.3-C
+   ###  Practice Problem 2.3-D
+   ###  Practice Problem 2.3-E
+   ###  Practice Problem 2.3-F
+   ##  Random Variables
+   ###  Practice Problem 2.4-A
+   ###  Practice Problem 2.4-B
+   ##  Jointly Distributed Random Variables
+   ###  Practice Problem 2.6-A
+   ###  Practice Problem 2.6-B
+
+
+FILE: ./notebooks/09-publish/01-Bernoulli-Probability-Distribution.ipynb
+   #  Bernoulli Probability Distribution
+   ##  Learning Objectives
+   ##  Bernoulli Distribution
+
+
+FILE: ./notebooks/09-publish/02-Binomial-Probability-Distribtuion.ipynb
+   #  Binomial Probability Distributions
+   ##  Learning Objectives
+   ##  Binomial Distribution
+   ###  Flipping Three Coins
+
+
+FILE: ./notebooks/09-publish/03-Poisson-Probability-Distribution.ipynb
+   #  Poisson Probability Distributions
+   ##  Learning Objectives
+   ##  Poisson Distribution
+   ###  Example: Failure Rates
+
+
+FILE: ./notebooks/09-publish/04-Normal-Probability-Distribution.ipynb
+   #  Normal Probability Distributions
+   ##  Learning Objectives
+   ##  Normal Distribution
+   ###  68-95-99.7 Rule
+    WARNING: https://miro.medium.com/max/24000/1 is not reachable, status_code: 404
+   ###  Standardization
+   ###  Example: Using the Z-Table
+   ####  What is the probability the next pizza coming off the manufacturing line has more than 10.4 grams of fat?  
+   ####  The FDA would like you to certify that 99% of frozen pizzas have fat content between $\underline{\qquad \qquad}$ and $\underline{\qquad \qquad}$ grams, where the interval in centered at the mean. Fill in the blanks.
+   ###  Example: Modeling Stocks
+   ####  Approach 1: Numerically integrate the pdf
+   ####  Approach 2: Standardize and use function for standard normal distribution
+
+
+FILE: ./notebooks/09-publish/05-Common-Probability-Distributions-Summary.ipynb
+   #  Common Probability Distributions Summary
+   ##  Learning Objectives
+
+
+FILE: ./notebooks/10-publish/01-Measurement-Error.ipynb
+   #  Measurement Error
+   ##  Learning Objectives
+   ##  Measurement Error
+
+
+FILE: ./notebooks/10-publish/02-Error-Propagation.ipynb
+   #  Error Propagation
+   ##  Learning Objectives
+   ##  Error Propagation
+   ###  Addition and Subtraction
+   ###  Multiplication and Division Rule
+   ####  Division Rule
+   ###  Differentiable Function
+
+
+FILE: ./notebooks/10-publish/03-Measuring-Flowrate-Example.ipynb
+   #  Measuring Flowrate Example
+   ##  Learning Objectives
+   ###  Example: Measuring Flowrate
+   ####  Approach 1: Division Rule
+   ####  Approach 2:  Differentiable Function Rule
+
+
+FILE: ./notebooks/10-publish/04-Practice-Problems.ipynb
+   #  Practice Problems
+   ##  Learning Objectives
+   ###  Practice Problem A
+   ###  Practice Problem B
+   ###  Practice Problem C
+
+
+FILE: ./notebooks/10-publish/05-Simulation.ipynb
+   #  Simulation
+   ##  Learning Objectives
+   ##  Simulation
+   ###  Using Simulation to Estimate a Probability
+   ###  Using Simulation to Estimate Means and Variances
+   ###  Using Simulation to Determine Whether a Population is Approximately Normal
+   ###  Practice Problem 4
+
+
+FILE: ./notebooks/11-publish/01-Central-Limit-Theorem.ipynb
+   #  Central Limit Theorem
+   ##  Learning Objectives
+   ##  Central Limit Theorem
+   ###  Numerical Illustrations of Central Limit Theorem
+    WARNING: https://www.stat.berkeley.edu/~mlugo/stat134-f11/clt-proof.pdf is not reachable, status_code: 404
+   ####  Samples from Uniform Distribution
+   ####  Samples from Bimodal Distribution
+   ###  CLT Illustrative Example: Membrane Defects
+   ####  Expected Value of Defects
+   ####  Variance of Defects
+   ####  Standard Deviation of Number of Defects
+   ####  Probability
+
+
+FILE: ./notebooks/11-publish/02-Standard-Normal-Distribution.ipynb
+   #  Standard Normal Distribution
+   ##  Learning Objectives
+   ##  CLT Illustrative Example: Membrane Defects
+   ####  Expected Value of Defects
+   ####  Variance of Defects
+   ##  Standardized Normal Distribution
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/zscore-plot.png is not reachable, status_code: 404
+
+
+FILE: ./notebooks/11-publish/03-Confidence-Intervals.ipynb
+   #  Confidence Intervals
+   ##  Learning Objectives
+   ##  Motivating Example: Catalyst Life
+   ##  Confidence Interval
+   ###  68-95-99.7 Rule
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/68-95-99.7.png is not reachable, status_code: 404
+   ###  Confidence Interval Formula
+   ###  Catalyst Life Example
+
+
+FILE: ./notebooks/11-publish/04-Students-t-Distribution.ipynb
+   #  Student's t-Distribution
+   ##  Learning Objectives
+   ##  Student's t-Distribution
+   ###  Main Idea
+   ###  Degrees of Freedom and Visualization
+    WARNING: https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Student_t_pdf.svg/960px-Student_t_pdf.svg.png is not reachable, status_code: 403
+    WARNING: https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Student_t_cdf.svg/960px-Student_t_cdf.svg.png is not reachable, status_code: 403
+   ###  Comparison of $z^*$ and $t^*$
+   ###  Catalyst Example Revisited
+   ###  Important Assumptions: Population is Normally Distributed, Samples are Random
+    ERROR: https://drive.google.com/uc?export=view&id=1izl_-WD7yZk40QA3F9wHeiRd9V6-SMyW HTTPSConnectionPool(host='doc-0c-4o-docs.googleusercontent.com', port=443): Read timed out. (read timeout=10)
+
+
+FILE: ./notebooks/11-publish/05-First-Hypothesis-Testing.ipynb
+   #  First Hypothesis Testing
+   ##  Learning Objectives
+   ##  Hypothesis Testing
+   ###  Motivating Example: Catalyst Lifetime
+   ###  Null and Alternative Hypotheses
+   ###  Hypothesis Testing Procedure
+   ####  Step 1. Define Hypotheses.
+   ####  Step 2. Assume $H_0$ is True.
+   ####  Step 3. Compute the Test Statistic.
+   ####  Step 4. Calculate P-Value.
+   ####  Step 5. State Conclusions
+   ###  Summary of Hypotheses and P-Values
+
+
+FILE: ./notebooks/11-publish/06-Hypothesis-Testing-Review-and-Type-I-Type-II-Errors.ipynb
+   #  Hypothesis Testing Review and Type I/Type II Errors
+   ##  Learning Objectives
+   ##  Review Hypothesis Testing Conclusions
+   ###  Test your Understading
+   ##  Type I and Type II Errors for Statistical Inference
+   ###  Example: Law and Order
+    WARNING: https://drive.google.com/uc?export=view&id=1vZyz1Dxf3SEJhxKvQGjuyP3l3Lgwg9_l is not reachable, status_code: 403
+
+
+FILE: ./notebooks/11-publish/07-Flavors-of-Hypothesis-Testing.ipynb
+   #  Flavors of Hypothesis Testing
+   ##  Introduction
+   ##  Learning Objectives
+   ##  Small-Sample Test for Population Mean
+   ###  Step 1. Define Hypotheses.
+   ###  Step 2. Assume $H_0$ is True.
+   ###  Step 3. Compute the Test Statistic.
+   ###  Step 4. Calculate P-Value.
+   ###  Step 5. State Conclusions.
+   ##  Small-Sample Difference Between Two Means
+   ###  Step 1. Define Hypotheses.
+   ###  Step 2. Assume $H_0$ is True.
+   ###  Step 3. Compute the Test Statistic.
+   ###  Step 4. Calculate the P-Value.
+   ###  Step 5. State Conclusions.
+   ##  Small-Sample Different Between Pairwise Means
+   ###  Step 1. Define Hypotheses.
+   ###  Step 2. Assume $H_0$ is True.
+   ###  Step 3. Compute the Test Statistic.
+   ###  Step 4. Calculate P-Value.
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/one-sided-left.png is not reachable, status_code: 404
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/two-sided.png is not reachable, status_code: 404
+    WARNING: https://ndcbe.github.io/data-and-computing/_images/one-sided-right.png is not reachable, status_code: 404
+   ###  Step 5. State Conclusions.
+   ##  Population Proportion
+   ###  Motivating Example
+   ###  Step 1. Define Hypotheses.
+   ###  Step 2. Assume $H_0$ is True.
+   ###  Step 3. Compute the Test Statistic.
+   ###  Step 4. Calculate P-Value.
+   ###  Step 5. State Conclusions.
+
+
+FILE: ./notebooks/11-publish/08-Statistical-Power-Basics.ipynb
+   #  Statistical Power Basics
+   ##  Learning Objectives
+   ##  Statistical Power
+   ###  Motivating Example
+   ###  Step 1. Fix the experiment design, make assumptions.
+   ###  Step 2. Compute the rejection region.
+    WARNING: https://drive.google.com/uc?export=view&id=1eiJt3uTmYLbOJVQOZ_P3vTo0ESa5tbpl is not reachable, status_code: 403
+   ###  Step 3. Compute the statistical power.
+   ###  Activity: Question 1
+   ###  Activity: Question 2
+
+
+FILE: ./notebooks/11-publish/09-Statistical-Power-in-Python.ipynb
+   #  Statistical Power in Python
+   ##  Learning Objectives
+   ##  Exploring Statistical Power in Python
+
+
+FILE: ./notebooks/11-publish/10-Statistical-Power-Practice-Problems.ipynb
+   #  Statistical Power Practice Problems
+   ##  Learning Objectives
+   ##  Practice Problems
+   ###  Practice Problem A
+   ###  Practice Problem B
+   ###  Practice Problem C
+
+
+FILE: ./notebooks/12-publish/01-Ordinary-Least-Squares-Linear-Regression.ipynb
+   #  Ordinary Least Squares Linear Regression
+   ##  Learning Objectives
+   ##  Correlation
+   ##  The Best Fit Line
+   ##  Motivating Example for Linear Regression
+   ###  Linear Regression Basics
+   ###  Optimization Formulation
+   ###  Analytic Solution
+   ###  Scipy Package
+   ###  Visualize
+   ##  Residual Analysis
+   ###  Calculate Residuals
+   ###  Histogram of Residuals
+   ###  Scatter Plot of Residuals versus Predicted Values
+    WARNING: https://drive.google.com/uc?export=view&id=18C0Lsiy5xXUWXGZ3shsKsv3CnsjufU-n is not reachable, status_code: 403
+   ###  Scatter Plot of Residuals versus Independent Variable
+    WARNING: https://drive.google.com/uc?export=view&id=1BobcdQP4SWK4kEeEcvWUAxTK9P_QDsic is not reachable, status_code: 403
+   ###  Scatter Plot of Residuals versus Data Collection Order
+   ###  Scatter Plot of Residuals Squared versus Independent Variable (and Collection Order)
+    WARNING: https://drive.google.com/uc?export=view&id=15--jigLRrNUq6TBzIraahIfzpvsJaZfN is not reachable, status_code: 403
+    WARNING: https://drive.google.com/uc?export=view&id=1Hu6PEXeZqkUJ2w6mcdXNk6oqXH6k1HL5 is not reachable, status_code: 403
+    WARNING: https://drive.google.com/uc?export=view&id=1SJzBRLdtv1oqmhLhfZsYsSlLH17_-5GI is not reachable, status_code: 403
+   ###  What if an assumption is violated?
+   ####  Plot and Best Fit
+   ####  Reisdual Analysis: Part 1
+   ####  Residual Analysis: Part 2
+
+
+FILE: ./notebooks/12-publish/02-Uncertainty-Analysis-and-Statistical-Inference.ipynb
+   #  Uncertainty Analysis and Statistical Inference
+   ##  Learning Objectives
+   ##  Uncertainty Analysis and Statistical Inference
+   ###  Estimating Variance of Residuals
+   ###  Simplified Uncertainty in Regression Parameters
+   ###  Confidence Intervals
+   ###  Uncertainty in Regression Parameters Revisited
+
+
+FILE: ./notebooks/12-publish/03-Linear-Regression-Practice-Problems.ipynb
+   #  Linear Regression Practice Problems
+   ##  Learning Objectives
+   ##  Supplemental Exercise 7.5 (Navidi 2015)
+   ##  Supplemental Exercise 7.8 (Navidi 2015)
+
+
+FILE: ./notebooks/12-publish/04-Multivariate-Linear-Regression.ipynb
+   #  Multivariate Linear Regression
+   ##  Learning Objectives
+   ##  Multivariate Regression
+   ###  Motivating Example: Predict Bluegills Age
+    ERROR: https://newonlinecourses.science.psu.edu/stat501/node/325/ HTTPSConnectionPool(host='newonlinecourses.science.psu.edu', port=443): Max retries exceeded with url: /stat501/node/325/ (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1129)')))
+   ###  Normal Equations
+   ###  Attempt 1. Linear Model
+   ###  Attempt 2. Quadratic Model
+   ###  How to select the best model?
+
+
+FILE: ./notebooks/13-publish/01-Transformations-and-Linear-Regression.ipynb
+   #  Transformations and Linear Regression
+   ##  Learning Objectives
+   ##  Introduction
+   ###  Motivating Example and Dataset
+   ##  Transformation and Linear Regression
+   ###  Step 1: Calculate Best Fit and Plot
+   ###  Step 2. Residual Analysis
+   ####  Calculate Residuals
+   ####  Histogram of Residuals
+   ####  Scatter Plots of Residuals
+   ###  Step 3. Uncertainty Analysis / Calculate Covariance Matrix
+   ###  Final Thoughts
+
+
+FILE: ./notebooks/13-publish/02-Nonlinear-Regression.ipynb
+   #  Nonlinear Regression
+   ##  Learning Objectives
+   ##  Nonlinear Regression
+   ###  Step 1: Calculate Best Fit and Plot
+   ###  Step 2: Residual Analysis
+   ###  Step 3. Uncertainty Analysis / Calculate Covariance Matrix
+
+
+FILE: ./notebooks/13-publish/03-Nonlinear-Regression-Practice-Problem.ipynb
+   #  Nonlinear Regression Practice Problem
+   ##  Learning Objectives
+   ##  Nonlinear Regression Practice Problem
+   ###  Transform Model Parameters
+   ###  Perform Nonlinear Regression
+   ###  Plot Residuals and Fitted Model
+   ###  Covariance Matrix
+   ###  Final Thoughts
+
+
+FILE: ./notebooks/13-publish/04-Weighted-Regression.ipynb
+   #  Weighted Linear Regression
+   ##  Learning Objectives
+   ##  Weighted Regression
+   ###  Michaelis-Menten Modeling Revisited
+   ###  Approach 1: Transformation + Linear Regression
+   ####  Step 1: Calculate Best Fit and Plot
+   ####  Step 2. Residual Analysis
+   ###  Approach 2: Nonlinear Regression
+   ###  Approach 3: Transformation + Weighted Linear Regression
+   ####  Step 1: Calculate Best Fit and Plot
+   ####  Step 2. Residual Analysis
+   ###  Uncertainty Analysis
+
+
+FILE: ./notebooks/13-publish/05-Monte-Carlo-Uncertainty-Analysis-for-Nonlinear-Regression.ipynb
+   #  Monte Carlo Uncertainty Analysis for Nonlinear Regression
+   ##  Simulation
+   ###  Using Simulation to Estimate a Probability
+   ###  Using Simulation to Estimate Means and Variances
+   ###  Using Simulation to Determine Whether a Population is Approximately Normal
+   ##  Monte Carlo Error Propagation
+   ####  Student 1
+   #####  Standard Error Propagation
+   #####  Monte Carlo Error Propagation
+   ####  Student 2
+   #####  Monte Carlo Error Propagation
+   ###  Uncertainty Analysis for Nonlinear Regression
+   ####  Nonlinear Regression + Linearized Uncertainty Analysis
+   #####  Step 1. Calculate Best Fit and Plot
+   #####  Step 2. Residual Analysis
+   #####  Step 3. Uncertainty Analysis / Calculate Covariance Matrix
+   ####  Nonlinear Regression + Monte Carlo Uncertainty Analysis
+   #####  Pseudocode
+   #####  Python Hints
+   #####  Implement in Python
+   #####  Visualize Distribution of Fitted Parameters
+   #####  Compute Covariance Matrix
+
+
+FILE: ./notebooks/13-publish/06-Bootstrap-Confidence-Intervals.ipynb
+   #  Bootstrap Confidence Intervals
+   ##  Bootstrap Confidence Intervals
+   ###  Motivating Example: Catalyst Lifetime 
+   ###  Traditional Confidence Intervals
+   ####  t-interval
+   ###  Basic Bootstrap with Replacement
+
+
+FILE: ./notebooks/13-publish/07-Nonlinear-Case-Study-Adsorptive-Membranes.ipynb
+   #  Nonlinear Regression Case Study: Adsorptive Nanoporous Membranes
+   ##  Background
+   ##  1. Regression Analysis
+   ###  A. Paramter estimation using a transformation and linear regression
+   ####  Step ii: Setup Best Fit Plot for Best Fit Model and Residuals
+   ####  Step iii: Compute Covariance Matricies
+   ###  B. Paramter estimation using an alternate transformation and linear regression
+   ###  C. Parameter estimation using nonlinear regression
+   ###  D. Comparison of Three Regression Approaches
+   ##  2. Estimate Batch Processing Time with Numeric Integration
+   ###  A. Model Manipulation and Setup
+   ###  B. Numeric Integration
+   ##  3. Uncertainty Propagation (Nonlinear Formula)
+   ###  A. Define Functions
+   ###  B. First Transformation + Linear Regression
+   ###  C. Second (Alternate) Transformation + Linear Regression
+   ###  D. Nonlinear Regression
+   ###  E. Discussion
+   ##  4. Monte Carlo Uncertainty Propogation
+   ###  A. Inspect Residuals
+   ###  B. Monte Carlo Python Code
+   ###  C. Visualize and Interpret MC Results: Fitted Parameters
+   ###  D. Visualize and Interpret MC Results: Batch Time
diff --git a/scripts/lint.py b/scripts/lint.py
index 4423fb41..7a8f4522 100644
--- a/scripts/lint.py
+++ b/scripts/lint.py
@@ -12,36 +12,41 @@
 import nbformat
 
 URL = r'(http|ftp|https):\/\/([\w\-_]+(?:(?:\.[\w\-_]+)+))([\w\-\.,@?^=%&:/~\+#]*[\w\-\@?^=%&/~\+#])?'
+HEADER = re.compile(r'(^|\n)(?P<level>#{1,6})(?P<header>.*?)#*(\n|$)')
+
+def find_and_test_urls(cell):
+    urls = re.findall(URL, cell.source)
+    for protocol, domain, target in urls:
+        url = "://".join([protocol, "".join([domain, target])])
+        try:
+            get = requests.get(url, timeout=10)
+            if not get.status_code == 200:
+                print(f"    WARNING: {url} is not reachable, status_code: {get.status_code}")
+        except requests.exceptions.RequestException as e:
+            print(f"    ERROR: {url} {e}")
+
+            
+def find_headers(cell):
+    headers = re.findall(HEADER, cell.source)
+    if headers:
+        print(f"   {' '.join(headers[0][1:3])}")
 
+        
 def lint_notebook(full_folder_name, file):
     path = f"{full_folder_name}/{file}"
-    print("\n", path)
+    print(f"\n\nFILE: {path}")
     with open(path, "r") as fp:
         nb = nbformat.read(fp, as_version=4)
-        
-    # check that urls are reachable
-    print("urls ...")
     for cell in nb.cells:
         if cell.cell_type == "markdown":
-            urls = re.findall(URL, cell.source)
-            for protocol, domain, target in urls:
-                url = "://".join([protocol, "".join([domain, target])])
-                try:
-                    get = requests.get(url, timeout=10)
-                    if get.status_code == 200:
-                        print(f"    OK: {url} is reachable")
-                    else:
-                        print(f"    WARNING: {url} is not reachable, status_code: {get.status_code}")
-                except requests.exceptions.RequestException as e:
-                    print(f"    ERROR: {url} {e}")
-
+            find_headers(cell)
+            find_and_test_urls(cell)
 
 
 folders = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13"]
 
 for fld in folders:
     full_folder_name = f"./notebooks/{fld}-publish"
-    print("\n", full_folder_name)
     for file in sorted(os.listdir(full_folder_name)):
         if re.match("(.*)\.ipynb$", file):
             lint_notebook(full_folder_name, file)