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This document provides an in-depth analysis of Nicole's pottery factory financial scenario, Exercises of Mathematics

This document provides an in-depth analysis of Nicole's pottery factory financial scenario for a microeconomics course. It includes detailed calculations of her accounting and economic profits, considering explicit costs, opportunity costs, and alternative employment options. The content covers key economic concepts such as explicit and implicit costs, opportunity cost, and profit measurement. It is designed as part of a university-level course in microeconomics, specifically course number 2103, taught by Professor [Name], focusing on beginner to intermediate economic analysis for students. The document is suitable for students studying microeconomics, economic decision-making, and firm profitability analysis in a classroom setting.

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Review for final exam : probability unit
MIT 18.05 Spring 2022
Sets and counting
Sets: , union, intersection, complement Venn diagrams, products
Counting: inclusion-exclusion, rule of product,
= (𝑛
permutations 𝑛𝑃𝑘, combinations 𝑛𝐶𝑘 𝑘)
Problem 1. Consider the nucleotides 𝐴, 𝐺, 𝐶, 𝑇 .
(a) How many ways are there to make a sequence of 5 nucleotides.
(b) How many sequences of length 5 are there where no adjacent nucleotides are the same
(c) How many sequences of length 5 have exactly one 𝐴?
Problem 2. (a) How many 5 card poker hands are there?
(b) How many ways are there to get a full house (3 of one rank and 2 of another)?
(c) What’s the probability of getting a full house?
Problem 3. (Counting)
(a) How many arrangements of the letters in the word probability are there?
(b) Suppose all of these arrangements are written in a list and one is chosen at random.
What is the probability it begins with ‘b’.
Probability
Sample space, outcome, event, probability function. Rule: 𝑃 (𝐴 ∪𝐵) = 𝑃 (𝐴) +𝑃 (𝐵 )−
𝑃 (𝐴 𝐵).
Special case: 𝑃 (𝐴𝑐) = 1 𝑃 (𝐴)
(𝐴 and 𝐵 disjoint 𝑃 (𝐴 𝐵) = 𝑃 (𝐴) + 𝑃 (𝐵).)
Conditional probability, multiplication rule, trees, law of total probability, indepen-
dence
Bayes’ theorem, base rate fallacy
Problem 4. Let 𝐸 and 𝐹 be two events. Suppose the probability that at least one of
them occurs is 2/3. What is the probability that neither 𝐸 nor 𝐹 occurs?
Problem 5. Let 𝐶 and 𝐷 be two events with 𝑃(𝐶 ) = 0.3, 𝑃 (𝐷) = 0.4, and 𝑃 (𝐶 𝑐 𝐷) =
0.2.
What is 𝑃 (𝐶 𝐷)?
Problem 6. Suppose we have 8 teams labeled 𝑇1, …, 𝑇8. Suppose they are ordered by
placing their names in a hat and drawing the names out one at a time.
1
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pf5

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Review for final exam : probability unit

MIT 18.05 Spring 2022

Sets and counting

  • Sets: ∅, union, intersection, complement Venn diagrams, products
  • Counting: inclusion-exclusion, rule of product, permutations (^) 𝑛𝑃𝑘, combinations (^) 𝑛𝐶𝑘 = (𝑛𝑘) Problem 1. Consider the nucleotides 𝐴, 𝐺, 𝐶, 𝑇. (a) How many ways are there to make a sequence of 5 nucleotides. (b) How many sequences of length 5 are there where no adjacent nucleotides are the same (c) How many sequences of length 5 have exactly one 𝐴? Problem 2. (a) How many 5 card poker hands are there? (b) How many ways are there to get a full house (3 of one rank and 2 of another)? (c) What’s the probability of getting a full house? Problem 3. (Counting) (a) How many arrangements of the letters in the word probability are there? (b) Suppose all of these arrangements are written in a list and one is chosen at random. What is the probability it begins with ‘b’. Probability
  • Sample space, outcome, event, probability function. Rule: 𝑃 (𝐴∪𝐵) = 𝑃 (𝐴)+𝑃 (𝐵)− 𝑃 (𝐴 ∩ 𝐵). Special case: 𝑃 (𝐴𝑐) = 1 − 𝑃 (𝐴) (𝐴 and 𝐵 disjoint ⇒ 𝑃 (𝐴 ∪ 𝐵) = 𝑃 (𝐴) + 𝑃 (𝐵).)
  • Conditional probability, multiplication rule, trees, law of total probability, indepen- dence
  • Bayes’ theorem, base rate fallacy Problem 4. Let 𝐸 and 𝐹 be two events. Suppose the probability that at least one of them occurs is 2/3. What is the probability that neither 𝐸 nor 𝐹 occurs? Problem 5. Let 𝐶 and 𝐷 be two events with 𝑃 (𝐶) = 0.3, 𝑃 (𝐷) = 0.4, and 𝑃 (𝐶𝑐^ ∩ 𝐷) = 0.2. What is 𝑃 (𝐶 ∩ 𝐷)? Problem 6. Suppose we have 8 teams labeled 𝑇 1 , …, 𝑇 8. Suppose they are ordered by placing their names in a hat and drawing the names out one at a time.

(a) How many ways can it happen that all the odd numbered teams are in the odd numbered slots and all the even numbered teams are in the even numbered slots? (b) What is the probability of this happening? Problem 7. More cards! Suppose you want to divide a 52 card deck into four hands with 13 cards each. What is the probability that each hand has a king? Problem 8. Suppose we roll a fair die twice. Let 𝐴 be the event ‘the sum of the rolls is 5’ and let 𝐵 be the event ‘at least one of the rolls is 4.’ (a) Calculate 𝑃 (𝐴|𝐵). (b) Are 𝐴 and 𝐵 independent? Problem 9. On a quiz show the contestant is given a multiple choice question with 4 options. Suppose there is a 70% chance the contestant actually knows the answer. If they don’t know the answer they guess with a 25% chance of getting it right. Suppose they get it right. What is the probability that they were guessing? Problem 10. Suppose you have an urn containing 7 red and 3 blue balls. You draw three balls at random. On each draw, if the ball is red you set it aside and if the ball is blue you put it back in the urn. What is the probability that the third draw is blue? (If you get a blue ball it counts as a draw even though you put it back in the urn.) Problem 11. Suppose that 𝑃 (𝐴) = 0.4, 𝑃 (𝐵) = 0.3 and 𝑃 ((𝐴 ∪ 𝐵)𝐶^ ) = 0.42. Are 𝐴 and 𝐵 independent? Problem 12. Suppose now that events 𝐴, 𝐵 and 𝐶 are mutually independent with 𝑃 (𝐴) = 0.3, 𝑃 (𝐵) = 0.4, 𝑃 (𝐶) = 0.5. Compute the following: (Hint: Use a Venn diagram) (i) 𝑃 (𝐴 ∩ 𝐵 ∩ 𝐶𝑐) (ii) 𝑃 (𝐴 ∩ 𝐵𝑐^ ∩ 𝐶) (iii) 𝑃 (𝐴𝑐^ ∩ 𝐵 ∩ 𝐶) Problem 13. Suppose 𝐴 and 𝐵 are events with 0 < 𝑃 (𝐴) < 1 and 0 < 𝑃 (𝐵) < 1. (a) If 𝐴 and 𝐵 are disjoint can they be independent? (b) If 𝐴 and 𝐵 are independent can they be disjoint? (c) If 𝐴 ⊂ 𝐵 can they be independent? Random variables, expectation and variance

  • Discrete random variables: events, pmf, cdf

Problem 20. (a) Suppose that 𝑋 is uniform on [0, 1]. Compute the pdf and cdf of 𝑋. (b) If 𝑌 = 2𝑋 + 5, compute the pdf and cdf of 𝑌. Problem 21. (a) Suppose that 𝑋 has probability density function 𝑓𝑋(𝑥) = 𝜆e−𝜆𝑥^ for 𝑥 ≥ 0. Compute the cdf, 𝐹𝑋(𝑥). (b) If 𝑌 = 𝑋^2 , compute the pdf and cdf of 𝑌. Problem 22. Suppose that 𝑋 is a random variable that takes on values 0, 2 and 3 with probabilities 0.3, 0.1, 0.6 respectively. Let 𝑌 = 3(𝑋 − 1)^2. (a) What is the expectation of 𝑋? (b) What is the variance of 𝑋? (c) What is the expection of 𝑌? (d) Let 𝐹𝑌 (𝑡) be the cumulative density function of 𝑌. What is 𝐹𝑌 (7)? Problem 23. Suppose you roll a fair 6-sided die 25 times (independently), and you get $3 every time you roll a 6. Let 𝑋 be the total number of dollars you win. (a) What is the pmf of 𝑋. (b) Find 𝐸[𝑋] and Var(𝑋). (c) Let 𝑌 be the total won on another 25 independent rolls. Compute and compare 𝐸[𝑋+𝑌 ], 𝐸[2𝑋], Var(𝑋 + 𝑌 ), Var(2𝑋). Explain briefly why this makes sense. Problem 24. A continuous random variable 𝑋 has PDF 𝑓(𝑥) = 𝑥 + 𝑎𝑥^2 on [0,1] Find 𝑎, the CDF and 𝑃 (0.5 < 𝑋 < 1). Problem 25. For each of the following say whether it can be the graph of a cdf. If it can be, say whether the variable is discrete or continuous. 𝑥

(i) 𝑥

(ii) 𝑥

(iii) 𝑥

(iv)

(v) 𝑥

(vi) 𝑥

(vii) 𝑥

(viii) Problem 26. Correlation Flip a coin 5 times. Use properties of covariance to compute the covariance and correlation between the number of heads on the first 3 and last 3 flips. Distributions with names Problem 27. Exponential Distribution Suppose that buses arrive are scheduled to arrive at a bus stop at noon but are always 𝑋 minutes late, where 𝑋 is an exponential random variable with probability density function 𝑓𝑋(𝑥) = 𝜆e−𝜆𝑥. Suppose that you arrive at the bus stop precisely at noon. (a) Compute the probability that you have to wait for more than five minutes for the bus to arrive. (b) Suppose that you have already waiting for 10 minutes. Compute the probability that you have to wait an additional five minutes or more. Problem 28. Normal Distribution: Throughout these problems, let 𝜙 and Φ be the pdf and cdf, respectively, of the standard normal distribution Suppose 𝑍 is a standard normal random variable and let 𝑋 = 3𝑍 + 1. (a) Express 𝑃 (𝑋 ≤ 𝑥) in terms of Φ (b) Differentiate the expression from (𝑎) with respect to 𝑥 to get the pdf of 𝑋, 𝑓(𝑥). Remember that Φ′(𝑧) = 𝜙(𝑧) and don’t forget the chain rule (c) Find 𝑃 (−1 ≤ 𝑋 ≤ 1) (d) Recall that the probability that 𝑍 is within one standard deviation of its mean is approximately 68%. What is the probability that 𝑋 is within one standard deviation of its mean? Problem 29. Transforming Normal Distributions Suppose 𝑍 ∼ N(0,1) and 𝑌 = e𝑍^.

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18.05 Introduction to Probability and Statistics

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