AQA Further AS Paper 2 Statistics 2019 June — Question 5 9 marks

Exam BoardAQA
ModuleFurther AS Paper 2 Statistics (Further AS Paper 2 Statistics)
Year2019
SessionJune
Marks9
PaperDownload PDF ↗
Mark schemeDownload PDF ↗
TopicUniform Distribution
TypeDerive general variance formula
DifficultyStandard +0.8 This question requires proving the variance formula for a discrete uniform distribution using summation formulas (∑r and ∑r²), which is a standard Further Maths Statistics derivation. While it involves multiple steps and algebraic manipulation, it's a well-known result that students are expected to be able to derive, making it moderately challenging but not exceptional for Further AS level.
Spec5.02b Expectation and variance: discrete random variables5.02e Discrete uniform distribution

5 The discrete random variable \(X\) has the following probability distribution function $$\mathrm { P } ( X = x ) = \begin{cases} \frac { 1 } { n } & x = 1,2 , \ldots , n \\ 0 & \text { otherwise } \end{cases}$$ 5
    1. Prove that \(\mathrm { E } ( X ) = \frac { n + 1 } { 2 }\) [0pt] [3 marks]
      5
      1. (ii) Prove that \(\operatorname { Var } ( X ) = \frac { n ^ { 2 } - 1 } { 12 }\)
        5
      2. State two conditions under which a discrete uniform distribution can be used to model the score when a cubic dice is rolled.
        [2 marks]

Question 5(a)(i):
AnswerMarks Guidance
Answer/WorkingMark Guidance
\(E(X) = \sum_{x=1}^n \frac{x}{n} = \frac{1}{n}\sum_{x=1}^n x\)M1 Applies formula for \(E(X)\)
\(= \frac{\frac{n}{2}(1+n)}{n}\)A1 Applies formula for \(\sum x\)
\(= \frac{n+1}{2}\)R1 Shows \(E(X)=\frac{n+1}{2}\); fully correct solution, no slips, condone missing \(E(X)\)
Question 5(a)(ii):
AnswerMarks Guidance
Answer/WorkingMark Guidance
\(E(X^2) = \sum_{i=1}^n \frac{x^2}{n} = \frac{1}{n}\sum_{i=1}^n x^2\)M1 Applies formula for \(E(X^2)\)
\(= \frac{\frac{1}{6}n(n+1)(2n+1)}{n} = \frac{(n+1)(2n+1)}{6}\)A1 Applies formula for \(\sum x^2\)
\(\text{Var}(X) = E(X^2) - (E(X))^2\) using their \(E(X^2)\) and \(E(X)=\frac{n+1}{2}\)M1 Applies variance formula
\(= \frac{(n+1)(2n+1)}{6} - \left(\frac{n+1}{2}\right)^2 = \frac{4n^2+6n+2-3n^2-6n-3}{12} = \frac{n^2-1}{12}\)R1 Shows \(\text{Var}(X)=\frac{n^2-1}{12}\); needs intermediate line of working; fully correct
Question 5(b):
AnswerMarks Guidance
Answer/WorkingMark Guidance
Dice is unbiased / fair / each score has equal probability of occurringE1 States dice is unbiased or fair (condone "chance")
\(n=6\) or dice is numbered 1, 2, 3, 4, 5, 6E1 States \(n=6\) or dice numbered 1 to 6
## Question 5(a)(i):

| Answer/Working | Mark | Guidance |
|---|---|---|
| $E(X) = \sum_{x=1}^n \frac{x}{n} = \frac{1}{n}\sum_{x=1}^n x$ | M1 | Applies formula for $E(X)$ |
| $= \frac{\frac{n}{2}(1+n)}{n}$ | A1 | Applies formula for $\sum x$ |
| $= \frac{n+1}{2}$ | R1 | Shows $E(X)=\frac{n+1}{2}$; fully correct solution, no slips, condone missing $E(X)$ |

## Question 5(a)(ii):

| Answer/Working | Mark | Guidance |
|---|---|---|
| $E(X^2) = \sum_{i=1}^n \frac{x^2}{n} = \frac{1}{n}\sum_{i=1}^n x^2$ | M1 | Applies formula for $E(X^2)$ |
| $= \frac{\frac{1}{6}n(n+1)(2n+1)}{n} = \frac{(n+1)(2n+1)}{6}$ | A1 | Applies formula for $\sum x^2$ |
| $\text{Var}(X) = E(X^2) - (E(X))^2$ using their $E(X^2)$ and $E(X)=\frac{n+1}{2}$ | M1 | Applies variance formula |
| $= \frac{(n+1)(2n+1)}{6} - \left(\frac{n+1}{2}\right)^2 = \frac{4n^2+6n+2-3n^2-6n-3}{12} = \frac{n^2-1}{12}$ | R1 | Shows $\text{Var}(X)=\frac{n^2-1}{12}$; needs intermediate line of working; fully correct |

## Question 5(b):

| Answer/Working | Mark | Guidance |
|---|---|---|
| Dice is unbiased / fair / each score has equal probability of occurring | E1 | States dice is unbiased or fair (condone "chance") |
| $n=6$ or dice is numbered 1, 2, 3, 4, 5, 6 | E1 | States $n=6$ or dice numbered 1 to 6 |
5 The discrete random variable $X$ has the following probability distribution function

$$\mathrm { P } ( X = x ) = \begin{cases} \frac { 1 } { n } & x = 1,2 , \ldots , n \\ 0 & \text { otherwise } \end{cases}$$

5
\begin{enumerate}[label=(\alph*)]
\item (i) Prove that $\mathrm { E } ( X ) = \frac { n + 1 } { 2 }$\\[0pt]
[3 marks]\\

5 (a) (ii) Prove that $\operatorname { Var } ( X ) = \frac { n ^ { 2 } - 1 } { 12 }$\\

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5
\item & \begin{tabular}{l}
State two conditions under which a discrete uniform distribution can be used to model the score when a cubic dice is rolled. \\[0pt]
[2 marks] \\
\end{tabular} \\
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 &  \\
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\end{tabular}
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\end{enumerate}

\hfill \mbox{\textit{AQA Further AS Paper 2 Statistics 2019 Q5 [9]}}