Skip to main content

college - math2 stat2 gist

Math 2​

Ok, here's the thing. Math 2...

So, thing is, your syllabus is divided as follows:

Week 1-4: It's basically matrices. The very basic stuff all 11 th-12 th syllabi have.

What are matrices, addition, multiplication, elementary row transformation (here it is Gaussian elimination), adjoint matrices, determinants and stuff.

So, matrix manipulation is the end goal.

Week 5-8 becomes more of Linear Algebra.

So, how do you use matrices, solving irl questions with matrices, making questions into matrices.

So, trivial solutions, non-trivial solutions, binds, etc. Binds meaning uh... Those graphs na, where you say that in this range this set of equations has a solution, beyond it does not, that stuff basically. Unique solutions, infinite solutions, no solutions, that stuff.

Week 9-12 just goes into graph theory, BFS DFS, that stuff.

  • So... Q 2 means you need to cover till Week 8.

Stat 2​

Stats 2 basically goes like:

Week 0 is basically intro to probability statistics, so random variables, sampling, that stuff.

Week 1-4 is Discrete random variables. So distribution, probability, sample spaces, all this in the scope of discrete random variables. So ig binomial distribution and stuff.

Week 5-8 is continuous random variables, so normal distribution, poisson distribution, these stuffs.

Week 9-12 is hypothesis testing, t test, p test, that stuff, with random variables so all that.

In this, you had ig... 4-5 distributions.

Exam will have the necessary formulae in place already, so you legit need not memorise anything.

Pro tip here, check out all the concepts, and most definitely learn how to apply these formulae. That's all you need to do well.

You should see the formulae, and know, ok this is what it asks, this is what to plug in. That's it.

Binomial distribution,

Bernoulli distribution (?!),

Normal distribution,

Poisson distribution,

And... I remember these 4. Check the others. Ig there are 5.

So discrete variable distributions, had about 5 formulae, PDF and CDF, and same for continuous distribution (technically, it was something like, discrete distributions had continuous counterparts, they looked similar).

  • PDF and CDF formulae are what you need for every distribution.