https://mids-w203.github.io/practice_problems/

Several of these problems are adapted from Durrett's Elementary Probability for Appliations

practice_problems

This repo contains pointers to practice problems that run alongside the weekly concepts covered in MIDS w203.

Unit 1 Concepts

Concept	Example	Page Number
Probability Spaces	1.1: Dice Rolling	1
Probability Spaces	1.2: Dice Rolling	2
Probability Spaces	1.3: Letters in Word	4
Set Theory Probability	1.1.2: Basic Properties	5
Set Theory Probability	1.4: Car Stereos	5
Set Theory Probability	1.5: World Series	6-8
Independent Events	1.7: Draw 2 cards	10
Independent Events	1.8: Roll 2 dice	11
Independent Events	1.9: Birthdays	11
Independent Events	1.10: Roll 3 dice	12
Conditional Probability	3.1: Dice Rolling	81
Conditional Probability	3.2: Betting Games	82
Conditional Probability	3.4: Cards	83
Conditional Probability	3.5: Hockey Playoff	83
Conditional Probability	3.6: Graduating	84
Conditional Probability	3.8: Monty Hall Problem	84
Conditional Probability	3.9: Cognitive Dissonance	85

Unit 1 Practice Problems

Concept	Questions	Page Number
Probability Spaces	1-2, 4-6, 8-18	26-27
Statistical Independence	All	27-28

Unit 2 Concepts

Concept	Example	Page Number
Discrete R.V.s	1.11: Roll 2 dice	13
Discrete R.V.s	1.12: Geometric Distr.	13
Discrete R.V.s	1.13: Birthday problem II	13
Discrete R.V.s: Joint Distributions	3.25: Two 4 sided die	100
Discrete R.V.s: Joint Distributions	3.26: Draw 2 balls	100
Discrete R.V.s: Joint Distributions	3.27: Calculus Grades	101
Discrete R.V.s: Independence	3.28: Poisson Distr.	103
Discrete R.V.s: Conditional Distributions	3.29: AP Calculus	104
Discrete R.V.s: Conditional Distributions	3.30: Simpson's Paradox	105
Continuous R.V.s	5.1: Uniform Distr.	162
Continuous R.V.s	5.2: Exponential Distr.	162
Continuous R.V.s: Distribution Functions	5.7: Uniform Distr.	167
Continuous R.V.s: Distribution Functions	5.8: Exponential Distr.	167
Continuous R.V.s: Distribution Functions	5.10: Binomial Distr.	169
Continuous R.V.s: Functions of R.V.s	5.16: Functions of R.V.s	173
Continuous R.V.s: Joint Distributions	5.21: Uniform Distr. Ball	178
Continuous R.V.s: Joint Distributions	5.23: Uniform Distr. Square	180
Continuous R.V.s: Marginal Density	5.24: Marginal Density	182
Continuous R.V.s: Independence	5.25: Independence	182
Continuous R.V.s: Conditional Distributions	5.28: Conditional Distr. I	184
Continuous R.V.s: Conditional Distributions	5.29: Conditional Distr. II	185

Unit 2 Practice Problems

Concept	Questions	Page Number
Discrete Distributions	35-39	29
Discrete Joint Distributions	60-66	112
Density Functions	1-2	185
Continuous Distribution Functions	6-14	186
Functions of Random Variables	18-21	187
Continuous Joint Distributions	All	187

Concert Time

You are excited about a concert featuring your favorite a cappella group: the Pitch Estimators. Tickets go on sale at noon, but before you can buy a ticket, you have to wait for your turn in an online waiting room. Because tickets are in high demand, you enlist two of your friends to help you. All three of you enter the waiting room at noon, and as soon as any one of you gets a ticket, you are done and can all sign off.

Suppose your waiting time in minutes is a continuous random variable $T$. Your first friend's waiting time in minutes is a continuous random variable $U$. Your second friend's waiting time in minutes is a continuous random variable $V$.

Suppose these random varibles are have probability density functions given by:

$$f_T(t)=\begin{cases} \frac{1}{3}e^{-\frac{1}{3}t}, &t \geq 0\\ 0, &otherwise \end{cases}$$

$$f_U(t)=\begin{cases} \frac{1}{2}e^{-\frac{1}{2}t}, &t \geq0\\ 0, &otherwise \end{cases}$$

$$f_V(t)=\begin{cases} \frac{1}{6}e^{-\frac{1}{6}t}, &t\geq 0\\ 0, &otherwise \end{cases}$$

These are examples of what we call exponential random variables.

a. Please sketch the probability density for all three random variables on one graph.

b. For a particular time $t$, compute the probability that $T>t$, $U>t$, and $V>t$.

c. Let $O$ represent your overall waiting time. $O$ is the minimum of the waiting times for each of the three websites, $O = min(T,U,V)$. Compute the probability density function of $O$. (Hint: use the previous answer to write down the cdf)

Lecture #Fail

Suppose that the time of an event is a random variable $T$. One way to describe the distribution of $T$ is with the hazard rate. If $T$ has pdf $f_T$ and cdf $F_T$, the hazard rate is given by:

$$ h_T(t) = \frac{f_T(t)}{1-F_T(t)} $$

The hazard rate at time $t$ is the probability density conditional on the event $T \geq t$. To put it roughly, the hazard rate indicates how likely an event is to occur right away, given that it has not already occured.

1. Say that the time that a server breaks down is a random variable $B$, which is uniformly distributed on $[0,2]$. Compute the hazard rate of $B$.
2. Prove that if $X$ is a random variable with hazard rate $h_X$ and $Y$ is a random variable with hazard rate $h_Y$, then the hazard rate of $min(X,Y)$ is $h_X + h_Y$. (Hint: write the hazard rate in terms of just the cdf - no pdf. Then remember what the cdf of a minimum of random variables looks like from the previous problem)

Can Independence Carry Through?

The following statements are either true or false. Prove them or provide a counterexample:

1. If X, Y, and Z are random variables, X and Y are independent, Y and Z are independent, then X and Z must be independent.
2. If X, Y, and Z are random variables, X and Y are not independent, Y and Z are not independent, then X and Z must be not independent.

Unit 3 Concepts

Concept	Examples	Page Number
Expected Value	1.18, 1.20, 1.21, 1.22	17-19
Expected Value of Sums	6.8, 6.9, 6.10, 6.11, 6.12	197-200
Variance and Covariance	1.26, 1.27, 1.29,	23-25

Unit 3 Practice Problems

Concept	Questions	Page Number
Expected Value	40-49	29-30
Expected Value of Sums	1-9	223
Variance and Covariance	(i) 50-56; (ii) 10-16	(i) 30-31; (ii) 223-224

Unit 4 Concepts

Unit 4 Practice Problems

Stacking Blocks

In $\R^2$, define square region A with corners at ${ (0,1), (1,1),(1,2), (0,2) }$ and square region B with corners at ${ (0.5,0),(1.5,0),(1.5,1),(0.5,1) }$. Suppose that random variables X and Y have joint density given by,

$$f(x,y) = \begin{cases} 0.5, &(x,y) \in A \cup B \\ 0, &otherwise \end{cases} $$

1. Compute the conditional expectation function of Y given X.

2. Compute the conditional expectation function of X given Y.

Fanning Out

Suppose random variables X and Y have joint density given by,

$$f(x,y) = \begin{cases} 1/x, &0 < x < 1, 0 < y < x \\ 0, &otherwise \end{cases} $$

This is a long (but revealing) way to compute $var[Y]$

1. Compute the marginal distribution of X.
2. Compute $var[Y|X]$. This will be a fuction of X.
3. Compute $\E[ var[Y|X]]$. This will be a number.
4. Compute $\E[Y|X]$. This will be a function of X.
5. Compute $var[ \E[Y|X]]$. This will be a number.
6. Using the law of total variance, compute $var[Y]$.

BLP for a Square

Suppose X and Y have joint density given by,

$$f(x,y) = \begin{cases} 1, &0 \leq x \leq 1, 0 \leq y \leq 1\\ 0, &otherwise \end{cases} $$

1. Prove that the linear predictor $g(x) = 0.5$ fulfills the first population moment condition.

2. Prove that the linear predictor $g(x) = 0.5$ fulfills the second population moment condition.

You conclude that $g$ is the BLP.

Closer Look at Moment Conditions

Suppose X and Y have joint mass function given by,

$$f(x,y) = \begin{cases} 0.25, &(x,y) \in { (1,1),(1,-1). (-1,1), (-1,-1) } \\ 0, &otherwise \end{cases} $$

Consider a linear predictor, $g(x) = \beta_0 + \beta_1 x$, and let $\epsilon = Y - g(X)$ be the error term.

1. Compute $\E[\epsilon]$ in terms of $\beta_0$ and $\beta_1$.

Now set $\beta_0$ to fulfill the first moment condition: $\E[\epsilon] = 0$.

2. Compute $\E[X \epsilon]$ in terms of $\beta_1$

Now set $\beta_1$ to fulfill the second moment condition: $\E[X \epsilon] = 0$.

3. What is the BLP?

Regression: Origins

Consider random variables $X$ and $Y$. In the technique called regression through the origin, we are interested in linear predictors of the form,

$$g(x) = b_1 x$$

In other words, linear predictors that pass through the origin. Given such a predictor, define $\epsilon = Y - g(X)$ as always. We are interested in minimizing mean squared error:

$$\beta_1 = \text{argmin}_{b_1} \E[\epsilon^2]$$

Examine the proof on page 77 of Agnostic Statistics and consider how it would be different for regression through the origin.

1. Prove that $\E[\epsilon X] = 0$ as before.
2. Is it still true that $cov[\epsilon, X] = 0$? Prove it or give a counterexample.
3. Compute an expression for $\beta_1$.