Exercise 3 If we increase the number of replicates to 1000, then we will encounter a larger percentage of possible values for p at frequencies proportional to their probabilities of occurrence. By plotting these replicates in a histogram, we can start to visualize the probability distribution of p. The code below provides an example with sample size n = 10. Alter the plot so that the color is red, the x-axis reads "Proportion of Heads", y-axis reads "Frequency", and the title (main) says "Distribution of Sample Proportions, n=10": Code Start Over 1 volues-c("H","T") 2 phats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10) 3 hist(phots, xlab-"X label here",ylab-"y label here", main-"Put title here", col-"orange") Run Code Exercise 4 Besides plotting the distribution, we can also use simulations to estimate the expected value/mean or standard deviation of random variables. The output below computes the mean. Change it so that it computes the standard deviation (command-sd): Code Start Over 1 volues-c("H","T") 2 repeats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10) 3 mean(repeats) Run Code
Exercise 3 If we increase the number of replicates to 1000, then we will encounter a larger percentage of possible values for p at frequencies proportional to their probabilities of occurrence. By plotting these replicates in a histogram, we can start to visualize the probability distribution of p. The code below provides an example with sample size n = 10. Alter the plot so that the color is red, the x-axis reads "Proportion of Heads", y-axis reads "Frequency", and the title (main) says "Distribution of Sample Proportions, n=10": Code Start Over 1 volues-c("H","T") 2 phats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10) 3 hist(phots, xlab-"X label here",ylab-"y label here", main-"Put title here", col-"orange") Run Code Exercise 4 Besides plotting the distribution, we can also use simulations to estimate the expected value/mean or standard deviation of random variables. The output below computes the mean. Change it so that it computes the standard deviation (command-sd): Code Start Over 1 volues-c("H","T") 2 repeats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10) 3 mean(repeats) Run Code
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
![Exercise 3
If we increase the number of replicates to 1000, then we will encounter a larger percentage of possible values for p at frequencies proportional to
their probabilities of occurrence. By plotting these replicates in a histogram, we can start to visualize the probability distribution of p. The code
below provides an example with sample size n = 10. Alter the plot so that the color is red, the x-axis reads "Proportion of Heads", y-axis reads
"Frequency", and the title (main) says "Distribution of Sample Proportions, n=10":
Code Start Over
1 values-c("H","T")
2 phats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10)
3 hist(phots, xlab-"X label here",ylab-"y label here", main-"Put title here",col="orange")
Exercise 4
Besides plotting the distribution, we can also use simulations to estimate the expected value/mean or standard deviation of random variables.
The output below computes the mean. Change it so that it computes the standard deviation (command=sd):
Code
Run Code
Start Over
1 volues-c("H","T")
2 repeats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10)
3 mean(repeats)
Run Code](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F190a0e86-2c74-4ef5-82da-d76bb2f30fa4%2Fe88358d5-ab9c-4c05-83f7-f251cd9283a2%2Fogvkgpb_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Exercise 3
If we increase the number of replicates to 1000, then we will encounter a larger percentage of possible values for p at frequencies proportional to
their probabilities of occurrence. By plotting these replicates in a histogram, we can start to visualize the probability distribution of p. The code
below provides an example with sample size n = 10. Alter the plot so that the color is red, the x-axis reads "Proportion of Heads", y-axis reads
"Frequency", and the title (main) says "Distribution of Sample Proportions, n=10":
Code Start Over
1 values-c("H","T")
2 phats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10)
3 hist(phots, xlab-"X label here",ylab-"y label here", main-"Put title here",col="orange")
Exercise 4
Besides plotting the distribution, we can also use simulations to estimate the expected value/mean or standard deviation of random variables.
The output below computes the mean. Change it so that it computes the standard deviation (command=sd):
Code
Run Code
Start Over
1 volues-c("H","T")
2 repeats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10)
3 mean(repeats)
Run Code
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