Probability Trees and Conditional Expectations

50 questions available

Expected Value and Variance10 min
The expected value of a random variable is the central tendency of its distribution, representing the long-run average result. It is computed by multiplying each possible outcome by its probability of occurrence and summing the results. Variance quantifies the risk or spread of the distribution. It is the expected value of the squared differences between each outcome and the mean. The standard deviation, derived from the variance, provides a measure of dispersion in the same units as the original data.

Key Points

  • Expected Value (E(X)) = Sum of [P(Xi) * Xi]
  • Variance = Sum of [P(Xi) * (Xi - E(X))^2]
  • Standard Deviation is the square root of Variance
  • Variance and Standard Deviation are always non-negative
Total Probability Rule and Probability Trees10 min
The Total Probability Rule allows for the calculation of the unconditional probability of an event by considering all possible scenarios that could lead to it. This is often visualized using a probability tree, where branches represent conditional probabilities. By multiplying probabilities along a branch, one obtains the joint probability of that specific sequence of events. Summing the joint probabilities of all branches leading to a specific outcome yields the total probability of that outcome.

Key Points

  • Total Probability Rule combines conditional probabilities to find an unconditional probability
  • P(A) = P(A|S)*P(S) + P(A|Sc)*P(Sc)
  • Probability trees visualize joint and conditional probabilities
  • Joint Probability = Conditional Probability * Probability of Condition
Bayes' Theorem10 min
Bayes' Theorem is a powerful tool for updating beliefs when new data becomes available. It calculates a posterior probability (the probability of an event after seeing evidence) using the prior probability (initial belief) and the likelihood of the evidence given the event. It is essential for differentiating between the probability of evidence given an event and the probability of an event given the evidence.

Key Points

  • Used to update probabilities based on new information
  • Posterior Probability = (Likelihood * Prior) / Total Probability of Evidence
  • Distinguishes between P(A|B) and P(B|A)
  • Key in solving inverse probability problems

Questions

Question 1

What is the expected value of a random variable defined as?

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Question 2

Given a random variable X with outcomes 10 percent, 20 percent, and 30 percent, and probabilities 0.2, 0.5, and 0.3 respectively, what is the expected value?

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Question 3

Which of the following statements about variance is correct?

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Question 4

If the variance of a random variable is 25, what is the standard deviation?

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Question 5

Calculate the variance for a project with the following returns: 10 percent (Prob 0.5) and 20 percent (Prob 0.5).

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Question 6

In the context of the Total Probability Rule, what is P(S^c) if P(S) is 0.70?

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Question 7

Using the Total Probability Rule: P(Pass|Study) = 80 percent, P(Pass|Not Study) = 40 percent, P(Study) = 60 percent. What is the total probability of passing?

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Question 8

What does a node in a probability tree represent?

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Question 9

If P(A) = 0.5 and P(B|A) = 0.4, what is the joint probability P(AB)?

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Question 10

Bayes' Theorem helps us update the probability of a hypothesis based on what?

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Question 11

In Bayes' Theorem, the updated probability is also known as the:

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Question 12

You estimate the probability of EPS being 3 dollars at 25 percent and EPS being 4 dollars at 75 percent. What is the expected EPS?

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Question 13

Which calculator key is used to clear work in the STAT function of the TI BA II Plus?

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Question 14

Assume a scenario with a 60 percent probability of declining interest rates. If rates decline, there is a 75 percent chance EPS is 4 dollars. What is the joint probability of rates declining AND EPS being 4 dollars?

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Question 15

If the Expected Value is 15 and one observed outcome is 20 with a probability of 0.2, what is the squared deviation weighted by probability for this specific outcome?

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Question 16

A probability tree has two initial branches: A (30 percent) and B (70 percent). If branch A leads to outcome X with 50 percent probability, what is P(AX)?

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Question 17

In the formula for Bayes' Theorem, the numerator contains:

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Question 18

Given: P(Pass) = 68 percent. P(Pass|Study) = 80 percent. P(Study) = 70 percent. Calculate P(Study|Pass) using Bayes' Theorem.

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Question 19

Which of the following is true regarding standard deviation?

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Question 20

If the probability of studying is 70 percent, what is the probability of the complement (not studying)?

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Question 21

In a probability tree, if one branch has a probability of 40 percent, and the other branch represents the only other possibility, what is the probability of the second branch?

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Question 22

Calculate the expected return: Stock A (Return 5 percent, Prob 0.2), Stock B (Return 10 percent, Prob 0.3), Stock C (Return 20 percent, Prob 0.4), Stock D (Return 30 percent, Prob 0.1).

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Question 23

In the context of the FinTree Fruit 2 example, if earnings benefit from a declining interest rate, identifying the 'state' of the interest rate environment is an example of:

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Question 24

Which of the following values represents a valid variance?

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Question 25

Given P(A) = 0.4, P(B|A) = 0.5, and P(B|A^c) = 0.2, what is P(B)?

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Question 26

If we calculate a conditional mean, we are calculating the expected value:

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Question 27

When calculating variance using probabilities, the deviations are squared to:

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Question 28

If the Unconditional Probability of Passing is 0.68 and the Joint Probability of Passing AND Studying is 0.56, what is the Probability of Studying given Passing?

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Question 29

A 'Prior Probability' in Bayes' Theorem refers to:

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Question 30

If a stock has a 50 percent chance of returning 10 percent and a 50 percent chance of returning -10 percent, what is the expected return?

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Question 31

In the same stock scenario (50 percent chance of +10 percent, 50 percent chance of -10 percent), what is the variance?

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Question 32

Probability trees are particularly useful for visualizing:

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Question 33

In a probability tree, the sum of probabilities of all final outcomes (endpoints) must equal:

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Question 34

What is the standard deviation of a constant return (e.g., a guaranteed 5 percent)?

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Question 35

Given P(A) = 0.2 and P(B) = 0.3, if A and B are independent, what is P(A and B)?

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Question 36

If P(Info|Event) = 0.9, P(Event) = 0.1, and P(Info) = 0.2, what is P(Event|Info)?

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Question 37

A standard deviation of 7.75 corresponds to a variance of approximately:

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Question 38

Consider a probability tree where the first node splits into 'Study' (70 percent) and 'Not Study' (30 percent). If 'Not Study' branches into 'Pass' (40 percent) and 'Fail' (60 percent), what is the joint probability of 'Not Study and Fail'?

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Question 39

What is the primary difference between a conditional probability and a joint probability?

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Question 40

When using the Total Probability Rule to find P(Pass), we sum:

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Question 41

Given returns 5, 10, 20, 30 with probabilities 0.2, 0.3, 0.4, 0.1. Mean is 15. The squared deviation for return 10 is:

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Question 42

If E(X) = 15.40 percent and E(Y) = 10.30 percent, these values represent:

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Question 43

In a Bayes' scenario, if the likelihood of evidence given the event is 100 percent (certainty), the posterior probability depends on:

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Question 44

Calculate: P(A)=0.5, P(B|A)=0.2. What is P(AB)?

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Question 45

If a probability tree has 3 branches coming from a node, with probabilities 0.2, 0.3, and X. What is X?

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Question 46

Given Variance = 76. What is the approximate Standard Deviation?

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Question 47

In the HDFC Bank example, the 'Declining Interest Rate' scenario has a probability of 60 percent. This is best described as:

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Question 48

Which measure cannot be negative?

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Question 49

If P(A) = 0.5, P(B|A) = 0.5. What is P(AB)?

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Question 50

If you calculated a Posterior Probability of 1.2, what have you done wrong?

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