Category: s17-busad360

Final Review

Thu, Apr 27

Review:

  • Stock Market Simulation Presentations
  • Exam 3 Scores with Grade Summary
  • Course Evaluations
  • Final Exam Schedule
    • 1pm Section on Wed @ 1pm
    • 2:30pm Section on Fri @ 1pm
    • All welcome to test early Mon 1-4pm in HSB 117

Presentation:

  • Final Exam Review Topics
    • Calculations by Hand
      • Sum of Squares
      • Pearson Correlation Coefficient
      • Linear Regression
      • R-Squared
    • Scatterplots
    • Regression Output Interpretation
      • R-Squared
      • Coefficients
      • t-stats and p-values
      • F-stat and Significance of F
    • Multicollinearity
    • Binary Variables
    • Measuring Forecast Error with MAD, MSE
    • Polynomial Regression
    • Big Data Analytics
    • Monte Carlo Simulation
    • Random Sampling
    • Python Programming
  • Format to Expect
    • Correlation and Regression calculations by hand with small data set
    • Regression (simple and multiple) output interpretation questions
    • Forecast and forecast error calculations
    • Python code interpretation – simple Monte Carlo (like dice sim) w nested looping
    • Maybe an essay question or two
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Lesson 19: Variables, Lists, Functions, Conditionals and Loops in Python

Thu, Apr 13

Review:

Presentation:

  • Variables
  • Lists
  • Functions
  • Conditionals
    • If statements
    • If Else statements
  • Iteration
    • while loops
    • nested while loops

Here are the Python files I created during lecture:

Assignment:

  • Work on previous assignments and Exam 3 project

 

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Exam 3 Instructions

Exam 3 Instructions:

  • Due Date: On or before Tue April 25
  • Software: Python or Sheets/Excel
  • Data: S&P 500 Historical Returns
  • Simulation: Stock Market Investment Returns
    • Randomly sample the S&P 500 Historical Returns to simulate stock market returns. Assume the historical return data are normally distributed.
    • Use a 40-year investment time horizon.
    • Start with a $10,000 balance.
    • Run a Monte Carlo simulation to generate 500 ending investment balances.
    • Find the mean and standard deviation of your ending balances.
    • Generate a histogram displaying the distribution of your ending balances.
  • Print 1 Page with your (1) name, (2) section, (3) mean and standard deviation of ending balances, and (4) the histogram displaying the distribution of your ending balances. Bring your 1-page print out to the demo.
  • Demo: You must demonstrate your Python program or Sheets/Excel worksheet to me in person. Be prepared to explain your simulation, answer questions and make (or allow me to make) modifications to your program or worksheet to test alternative parameters (e.g., different investment time horizon, different starting balance, different number of simulations, etc).
  • Due Date: Yesterday. Just kidding. But, in business, clients always want everything yesterday and prefer to pay tomorrow. So get used to it. Demo with 1-page printout due on or before Apr 25. Demo as soon as you’re ready. Earlier is better. I will leave time for demos (on your laptop or on my PC in my office) at the end of class next week. You may also demo during regular office hours, MW 12:30-2:30.
  • Grading: I will present Exam 3 results and your grade summary (for Exam 3 and the term) in class on Thu, Apr 27
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Lesson 17: Monte Carlo Simulation

Thu, Apr 6

Review:

  • Linear Regression in Python
  • Tutoring Available for Python in HSB 121
    • Mon 11-5
    • Tue 12-1, 2:30-5
    • Thu 2:30-5

Presentation:

  • Monte Carlo Simulation

Assignment:

  • Create a simple Monte Carlo Simulation with Python
  • Simulate a simple Dice Game:
    • Roll 2-7, lose
    • Roll 8-12, win
    • Start with $100
      • bet $1 per roll (win pays $1, loss takes $1 bet)
      • roll 100 times and find ending balance (could be negative)
      • simulate 100 outcomes (10,000 total rolls of the dice)
    • Report the mean, median, and standard deviation of your final balances
    • Repeat using 1,000 rolls per outcome (100,000 total rolls of the dice) and report the mean, median and standard deviation of final outcomes

 

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Lesson 16: Linear Regression in Python

Tue, Apr 4

Review:

  • Sum of Squares Calculations in Python
  • Think Python (PDF)

Presentation:

Assignment:

  • Rewrite the 3 simple regression programs (lesson03a, 3b and 3c) with the following data set
    • Data set 2: {(50,12), (60,15), (70,20), (80,30)}
  • Write 2 programs (one for each of the two data worksheets in this Sheets file) to run multiple regression
    • Use “lesson04_multiregress.py” as example
  • Read Chapter 1 (pp. 1-9) in Think Python
  • Write, from scratch, a simple program to calculate the answer to Exercise 1.4 on p. 9  in Think Python
  • Nothing to submit yet. Instead, just save these programs in your PythonAnywhere “Files”.
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