STAT206: Statistics for Software Engineering

Course that I took in 2A SE.

There is this Statistics course is recommended by Soham.

Link to course notes here.

Things I still haven’t mastered

I think in general, this just comes down to lots of practice and muscle memory. These are concepts that I really need more practice in:

Chi-Squared Table comes in 2 cases:

I need more practice with Chi squared.

Review a lot my solutions for Quiz 3, because I really didn’t do well on this one.

Use sum? CLT (38 minutes in) But for mean, use Standard Error for

Personal Notes:

  • When you are trying to figure out the value for the z-table, always draw a Normal Distribution. It doesn’t hurt.
    • If it’s just one-tailed, you can just use the direct value
    • If it’s two tailed (because it’s within two inequalities), you can’t use the z-value directly, see Confidence Interval for sample calculation

Notes on things that I found hard: Week 9 Tutorial

  • 1f: In a class of 120 Software Engineering students, what is the probability that the class average will be 76 or more?

  • Example 3: How did they get ??

  • Correlation#todo #gap-in-knowledge I don’t understand this, check with the professor for the final file:///Users/stevengong/My%20Drive/Waterloo/2A/STAT206/Notes/Week%209.1%20-%20Discrete%20Joint%20Distributions%20-%20class%20notes.pdf.

  • Practice Confidence Interval, know the steps really by heart

  • Hypothesis Testing -> really do exercises and understand, unlike the first time you learned stats for Hypothesis Testing -> really do exercises and understand, unlike the first time you learned stats for Biology

  • Linear Regression model, the methods


This is also called a Chi-Squared test.

Motivation: Maybe there is a different proportion of left-handed and right-handed smokers. Testing the proportion of L and R handed smokers, is the same as testing for the independence and attributes.

How do you calculate ei

Miscellaneous Ideas

Probability and Statistics is very powerful, but oftentimes you can be easily mistaken and misled by your intuition.

For example, if you throw two dices, you would think that the probability of getting 7 is the same as getting a 12, since everything is uniformly distributed, so thus it’s random, but no.

A big part of statistics/probability initially is to learn how to count. -> OH yes, I remember this, it wasn’t from MIT6.042 but rather on Permutations and Permutations and Combinations

One super cool thing that I also learned in MIT6042.J is the Baseline Fallacy


  • Random Experiment: An experiment whose outcomes are unknown.
  • Sample Space: The set of all possible outcomes in an experiment.
  • Event: Any subset of a sample space.
  • Probability
  • Relative Frequency:

Problem-Solving Insights

#todo write down the patterns that you seen while solving these.

Splitting a problem into two parts (using an OR).

Ex: Probability that the first card is a King, and second card is Red. First part is assuming K is not red, second parenthesis is assuming K is red.