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| + | [[Category:ECE302Fall2008_ProfSanghavi]] | ||
| + | [[Category:probabilities]] | ||
| + | [[Category:ECE302]] | ||
| + | [[Category:homework]] | ||
| + | [[Category:problem solving]] | ||
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== Instructions == | == Instructions == | ||
Homework 8 can be [https://engineering.purdue.edu/ece302/homeworks/HW8FA08.pdf downloaded here] on the [https://engineering.purdue.edu/ece302/ ECE 302 course website]. | Homework 8 can be [https://engineering.purdue.edu/ece302/homeworks/HW8FA08.pdf downloaded here] on the [https://engineering.purdue.edu/ece302/ ECE 302 course website]. | ||
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*(a) What is (an upper bound on) the probability that it will take 3 hours? | *(a) What is (an upper bound on) the probability that it will take 3 hours? | ||
*(b) Landis only has 2 hours to spend fishing. What is (an upper bound on) the probability he will go home fish-less? | *(b) Landis only has 2 hours to spend fishing. What is (an upper bound on) the probability he will go home fish-less? | ||
| + | |||
| + | *[[Gregory Pajot 8.1a_ECE302Fall2008sanghavi]] | ||
| + | *[[Christopher Wacnik 8.1a_ECE302Fall2008sanghavi]] | ||
| + | *[[Ben Carter 8.1b_ECE302Fall2008sanghavi]] | ||
| + | *[[Zhongtian Wang 8.1_ECE302Fall2008sanghavi]] | ||
| + | *[[Tiffany Sukwanto 8.1_ECE302Fall2008sanghavi]] | ||
| + | *[[Chris Rush 8.1b_ECE302Fall2008sanghavi]] | ||
| + | *[[Nicholas BRowdues_ECE302Fall2008sanghavi]] | ||
| + | |||
| + | == Problem 2: Bounded Variance == | ||
| + | *(a) What is the maximum variance possible for a Bernoulli random variable? | ||
| + | *(b) What is the maximum variance possible for a binomial random variable, with parameter <math>n = 1000</math>? | ||
| + | *(c) If <math>X</math> is uniform on <math>[a,b]</math>, what is its variance? | ||
| + | *[[Problem 2a - Beau Morrison_ECE302Fall2008sanghavi]] | ||
| + | *[[Katie Pekkarinen 8.2b_ECE302Fall2008sanghavi]] | ||
| + | *[[2-c Seraj Dosenbach_ECE302Fall2008sanghavi]] | ||
| + | *[[ Divyanshu Kamboj - 2.a_ECE302Fall2008sanghavi]] | ||
| + | *[[ Shao-Fu Shih - 2.c_ECE302Fall2008sanghavi]] | ||
| + | *[[Anand Gautam -2.a_ECE302Fall2008sanghavi]] | ||
| + | *[[Ken Pesyna -2.a,b_ECE302Fall2008sanghavi]] | ||
| + | |||
| + | == Problem 3: "Bias" Estimate == | ||
| + | *(a) You have a coin of unknown bias. You flip it 10 times, and get TTHHTHTTHT as the sequence of outcomes. What is the maximum likelihood estimate of the bias (i.e. the probability, <math>p</math>, of heads)? | ||
| + | *(b) A friend has a coin of unknown bias. He flips it <math>n</math> times, and finds that <math>k</math> of them were heads. However, he neglects to record the exact sequence. What is the max-likelihood estimate for the bias in this case? | ||
| + | |||
| + | *[[Hamad Al Shehhi 8.3.a,b_ECE302Fall2008sanghavi]] | ||
| + | *[[Joon Young Kim 8.3.a_ECE302Fall2008sanghavi]] | ||
| + | *[[Zhongtian Wang easier way for finding P_ECE302Fall2008sanghavi]] | ||
| + | *[[Michael Allen 8.3.a_ECE302Fall2008sanghavi]] | ||
| + | *[[Arie Lyles 8.3.a_ECE302Fall2008sanghavi]] | ||
| + | *[[Patrick M. Avery Jr. 8.3a & b_ECE302Fall2008sanghavi]] | ||
| + | *[[Joshua Long 8.3_ECE302Fall2008sanghavi]] | ||
| + | *[[Jaewoo choi 8.3_ECE302Fall2008sanghavi]] | ||
| + | |||
| + | == Problem 4: Votes are In == | ||
| + | The election is over and the votes have been cast. If all the votes were counted, it would show that McCain has won Tippecanoe county. However, the Tippecanoe election officer is lazy however, and decides he is just going to count 1000 random votes. He does so, and finds 600 for Obama and 400 for McCain. He declares Obama has won Tippecanoe. What is (an upper bound on) such an event occuring? (Assume, as in class, that Tippecanoe has an infinite number of people.) | ||
| + | |||
| + | Hint: Let <math>p</math> be the true fraction of people that voted for Obama. We know that <math>p < 1/2</math>. Now find and upper bound on the event as a function of <math>p</math>, and then maximize over <math>p</math>. | ||
| + | |||
| + | |||
| + | *[[Andrew Hermann 8.4_ECE302Fall2008sanghavi]] | ||
| + | *[[Junzhe Geng 8.4_ECE302Fall2008sanghavi]] | ||
| + | *[[Brian Thomas 8.4_ECE302Fall2008sanghavi]] | ||
| + | *[[Spencer Mitchell 8.4_ECE302Fall2008sanghavi]] | ||
| + | *[[Justin Mauck 8.4_ECE302Fall2008sanghavi]] | ||
| + | ---- | ||
| + | [[Main_Page_ECE302Fall2008sanghavi|Back to ECE302 Fall 2008 Prof. Sanghavi]] | ||
Latest revision as of 11:58, 22 November 2011
Contents
Instructions
Homework 8 can be downloaded here on the ECE 302 course website.
Problem 1: Gone Fishin'
On average, it takes 1 hour to catch a fish.
- (a) What is (an upper bound on) the probability that it will take 3 hours?
- (b) Landis only has 2 hours to spend fishing. What is (an upper bound on) the probability he will go home fish-less?
- Gregory Pajot 8.1a_ECE302Fall2008sanghavi
- Christopher Wacnik 8.1a_ECE302Fall2008sanghavi
- Ben Carter 8.1b_ECE302Fall2008sanghavi
- Zhongtian Wang 8.1_ECE302Fall2008sanghavi
- Tiffany Sukwanto 8.1_ECE302Fall2008sanghavi
- Chris Rush 8.1b_ECE302Fall2008sanghavi
- Nicholas BRowdues_ECE302Fall2008sanghavi
Problem 2: Bounded Variance
- (a) What is the maximum variance possible for a Bernoulli random variable?
- (b) What is the maximum variance possible for a binomial random variable, with parameter $ n = 1000 $?
- (c) If $ X $ is uniform on $ [a,b] $, what is its variance?
- Problem 2a - Beau Morrison_ECE302Fall2008sanghavi
- Katie Pekkarinen 8.2b_ECE302Fall2008sanghavi
- 2-c Seraj Dosenbach_ECE302Fall2008sanghavi
- Divyanshu Kamboj - 2.a_ECE302Fall2008sanghavi
- Shao-Fu Shih - 2.c_ECE302Fall2008sanghavi
- Anand Gautam -2.a_ECE302Fall2008sanghavi
- Ken Pesyna -2.a,b_ECE302Fall2008sanghavi
Problem 3: "Bias" Estimate
- (a) You have a coin of unknown bias. You flip it 10 times, and get TTHHTHTTHT as the sequence of outcomes. What is the maximum likelihood estimate of the bias (i.e. the probability, $ p $, of heads)?
- (b) A friend has a coin of unknown bias. He flips it $ n $ times, and finds that $ k $ of them were heads. However, he neglects to record the exact sequence. What is the max-likelihood estimate for the bias in this case?
- Hamad Al Shehhi 8.3.a,b_ECE302Fall2008sanghavi
- Joon Young Kim 8.3.a_ECE302Fall2008sanghavi
- Zhongtian Wang easier way for finding P_ECE302Fall2008sanghavi
- Michael Allen 8.3.a_ECE302Fall2008sanghavi
- Arie Lyles 8.3.a_ECE302Fall2008sanghavi
- Patrick M. Avery Jr. 8.3a & b_ECE302Fall2008sanghavi
- Joshua Long 8.3_ECE302Fall2008sanghavi
- Jaewoo choi 8.3_ECE302Fall2008sanghavi
Problem 4: Votes are In
The election is over and the votes have been cast. If all the votes were counted, it would show that McCain has won Tippecanoe county. However, the Tippecanoe election officer is lazy however, and decides he is just going to count 1000 random votes. He does so, and finds 600 for Obama and 400 for McCain. He declares Obama has won Tippecanoe. What is (an upper bound on) such an event occuring? (Assume, as in class, that Tippecanoe has an infinite number of people.)
Hint: Let $ p $ be the true fraction of people that voted for Obama. We know that $ p < 1/2 $. Now find and upper bound on the event as a function of $ p $, and then maximize over $ p $.
- Andrew Hermann 8.4_ECE302Fall2008sanghavi
- Junzhe Geng 8.4_ECE302Fall2008sanghavi
- Brian Thomas 8.4_ECE302Fall2008sanghavi
- Spencer Mitchell 8.4_ECE302Fall2008sanghavi
- Justin Mauck 8.4_ECE302Fall2008sanghavi
