If x is a binomial random variable, use the binomial probability table to find the probabilities below. a. P(x<11) for n= 20, p = 0.6 b. P(x2 19) for n = 25, p= 0.7 c. P(x = 1) for n = 15, p = 0.9 More Info Click here to view a portion of the binomial probability table for n = 15, Click here to view a portion of the binomial probability table for n = 20. Click here to view a portion of the binomial probability table for n = 25. Binomial Probabilities for n = 20 a. P(x<11) = (Round to three decimal places as needed.) .10 .20 .30 40 .50 .60 .70 .80 .90 .122 .012 .001 .000 .000 .000 .000 .000 .000 .001 .004 1 392 .677 .069 .008 .000 .000 .000 .000 .000 .206 .035 .000 .000 .000 .000 .000 .000 .000 3. 4 867 .411 .107 238 .016 .051 .001 .000 .000 .000 957 630 .006 .000 .000 .000 989 804 416 .126 .002 .000 .000 .021 .058 .132 252 412 .000 6. .000 .000 .998 913 250 .006 .021 .608 .000 .000 .772 887 1.000 .968 .416 000 001 .005 017 .048 000 .001 003 .010 .032 .087 8. 1.000 1.000 .990 596 .057 .000 9. 997 .952 755 .128 .000 1.000 1.000 999 1.000 1.000 1.000 1.000 872 943 588 .748 868 942 .979 994 10 983 .245 .000 11 995 .404 .113 000 228 .392 .000 .002 .999 979 12 13 .584 .750 1.000 1.000 1.000 .994 1.000 1.000 .196 .011 .043 133 323 .874 .998 1.000 1.000 584 .762 893 .965 14 1.000 1.000 1.000 370 589 .794 15 1.000 .949 1.000 .999 1.000 1.000 1.000 16 1.000 .984 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 .996 .999 17 1.000 .992 .931 .608 18 1.000 1.000 .999 988 878 19 1.000 Enter your answer in the answer box and then click Check Answe
If x is a binomial random variable, use the binomial probability table to find the probabilities below. a. P(x<11) for n= 20, p = 0.6 b. P(x2 19) for n = 25, p= 0.7 c. P(x = 1) for n = 15, p = 0.9 More Info Click here to view a portion of the binomial probability table for n = 15, Click here to view a portion of the binomial probability table for n = 20. Click here to view a portion of the binomial probability table for n = 25. Binomial Probabilities for n = 20 a. P(x<11) = (Round to three decimal places as needed.) .10 .20 .30 40 .50 .60 .70 .80 .90 .122 .012 .001 .000 .000 .000 .000 .000 .000 .001 .004 1 392 .677 .069 .008 .000 .000 .000 .000 .000 .206 .035 .000 .000 .000 .000 .000 .000 .000 3. 4 867 .411 .107 238 .016 .051 .001 .000 .000 .000 957 630 .006 .000 .000 .000 989 804 416 .126 .002 .000 .000 .021 .058 .132 252 412 .000 6. .000 .000 .998 913 250 .006 .021 .608 .000 .000 .772 887 1.000 .968 .416 000 001 .005 017 .048 000 .001 003 .010 .032 .087 8. 1.000 1.000 .990 596 .057 .000 9. 997 .952 755 .128 .000 1.000 1.000 999 1.000 1.000 1.000 1.000 872 943 588 .748 868 942 .979 994 10 983 .245 .000 11 995 .404 .113 000 228 .392 .000 .002 .999 979 12 13 .584 .750 1.000 1.000 1.000 .994 1.000 1.000 .196 .011 .043 133 323 .874 .998 1.000 1.000 584 .762 893 .965 14 1.000 1.000 1.000 370 589 .794 15 1.000 .949 1.000 .999 1.000 1.000 1.000 16 1.000 .984 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 .996 .999 17 1.000 .992 .931 .608 18 1.000 1.000 .999 988 878 19 1.000 Enter your answer in the answer box and then click Check Answe
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
Question
- [Portion of Binomial Probability Table for \( n = 20 \)](#)
- [Portion of Binomial Probability Table for \( n = 25 \)](#)
The image provided shows a binomial probability table for \( n = 20 \). The table specifies the cumulative probabilities \( P(X \leq k) \) for values of \( k \) ranging from 0 to 19 and for various probabilities \( p \).
#### Table Description
The table contains columns for different probabilities:
- 0.10
- 0.20
- 0.30
- 0.40
- 0.50
- 0.60
- 0.70
- 0.80
- 0.90
Each row corresponds to a cumulative count \( k \) from 0 to 19. The intersection of a row and column gives the cumulative probability \( P(X \leq k) \) for that particular \( k \) and \( p \). For example:
- For \( p = 0.60 \) and \( k = 10 \), the table entry is 0.167.
#### Example Calculation
To find \( P(x < 11) \) for \( n = 20 \) and \(](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb25aa5e3-8edb-4708-bfd5-abf65dd8da34%2F69433e2e-50b1-44ec-8b0f-3b582ebacfd0%2Fm874u6_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Educational Content on Using Binomial Probability Tables
#### Introduction
In probability theory, the binomial distribution is one of the most important discrete distributions. It describes the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. This educational content explains how to use binomial probability tables to find specific binomial probabilities.
#### Problem Statement
If \( x \) is a binomial random variable, use the binomial probability table to find the probabilities below:
- a. \( P(x < 11) \) for \( n = 20, p = 0.6 \)
- b. \( P(x \geq 19) \) for \( n = 25, p = 0.7 \)
- c. \( P(x = 1) \) for \( n = 15, p = 0.9 \)
#### Binomial Probability Table for \( n = 20 \)
You can click on the links below to view various portions of the binomial probability table:
- [Portion of Binomial Probability Table for \( n = 15 \)](#)
- [Portion of Binomial Probability Table for \( n = 20 \)](#)
- [Portion of Binomial Probability Table for \( n = 25 \)](#)
The image provided shows a binomial probability table for \( n = 20 \). The table specifies the cumulative probabilities \( P(X \leq k) \) for values of \( k \) ranging from 0 to 19 and for various probabilities \( p \).
#### Table Description
The table contains columns for different probabilities:
- 0.10
- 0.20
- 0.30
- 0.40
- 0.50
- 0.60
- 0.70
- 0.80
- 0.90
Each row corresponds to a cumulative count \( k \) from 0 to 19. The intersection of a row and column gives the cumulative probability \( P(X \leq k) \) for that particular \( k \) and \( p \). For example:
- For \( p = 0.60 \) and \( k = 10 \), the table entry is 0.167.
#### Example Calculation
To find \( P(x < 11) \) for \( n = 20 \) and \(
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