Lesson 3B.7.2-3B.7.3
3B.7.2-3B.7.3 Using probability generating functions for mean and variance Quiz: Pearson Edexcel Further Maths, Unit 16
20 questions
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Lesson 3B.7.2-3B.7.3, Using probability generating functions for mean and variance: 20 multiple choice questions for the Pearson Edexcel Further Maths (9FM0), Unit 16: Probability generating functions, written with Revision Ninja.
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The 20 questions
-
In terms of the PGF G(t), what is E(X)?
- G'(1)
- G''(1)
- G'(0)
- G(1)
-
In terms of the PGF G(t), what is Var(X)?
- G''(1) - G'(1) - [G'(1)]^2
- G''(0) - G'(0) + [G'(0)]^2
- G'(1) + [G'(1)]^2 - G''(1)
- G''(1) + G'(1) - [G'(1)]^2
-
For X ~ Po(lambda), what is G'(1)?
- lambda
- 1
- lambda^2
- 0
-
For X ~ Po(lambda), what is G''(1)?
- 0
- e^lambda
- lambda
- lambda^2
-
What is the mean of a geometric distribution with p = 0.2 found from its PGF?
- 5
- 4
- 0.8
- 0.2
-
A random variable has G(t) = (t + t^2)/2. What is G'(1)?
- 1.5
- 2
- 0.5
- 1
-
For X ~ B(n, p), what is G'(1)?
- p/n
- n(1 - p)
- np
- n^2 p
-
Given G(t) = (0.4 + 0.6t)^3, what is Var(X)?
- 0.24
- 1.8
- 0.72
- 0.6
-
X and Y are independent with X ~ Po(2) and Y ~ Po(3). What is E(X + Y)?
- 2.5
- 1
- 6
- 5
-
X and Y are independent with X ~ Po(2) and Y ~ Po(3). What is Var(X + Y)?
- 5
- 13
- 9
- 2
-
For G(t) = 0.2 + 0.3t + 0.5t^2, what is G'(1)?
- 1
- 1.3
- 0.8
- 1.5
-
X ~ B(2, 0.5) and Y ~ B(3, 0.5) are independent. What is E(X + Y)?
- 2.5
- 2
- 1.5
- 3
-
If two PGFs are equal, what can be concluded about the random variables?
- They are independent
- They have the same variance only
- They have the same mean only
- They have the same distribution
-
A random variable has G(t) = e^(4(t - 1)). What is P(X = 0)?
- 4e^(-4)
- 1 - e^(-4)
- e^4
- e^(-4)
-
G_X(t) = (0.5 + 0.5t)^2. What is E(X)?
- 0.25
- 1
- 0.5
- 2
-
What does E(X(X - 1)) equal in terms of the PGF?
- G''(1)
- E(X)
- Var(X)
- E(X^2)
-
G(t) = (t + t^2 + t^3)/3. What is E(X)?
- 6
- 3
- 2
- 1
-
Three independent Po(1) variables are summed. What is the PGF of the sum?
- e^(3t - 1)
- e^(t - 3)
- e^(3(t - 1))
- 3e^(t - 1)
-
Why is the PGF useful when working with sums of independent random variables?
- The PGF of the product is the sum of the PGFs
- The PGF of the sum is the sum of the individual PGFs
- The PGF of the sum equals the derivative of each PGF
- The PGF of the sum is the product of the individual PGFs
-
X has PGF G(t) = (0.3 + 0.7t)^4. What is E(X)?
- 4
- 2.8
- 0.84
- 1.2
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