Edexcel S1 (Statistics 1) 2018 June

1. A random sample of 10 cars of different makes and sizes is taken and the published miles per gallon, \(p\), and the actual miles per gallon, \(m\), are recorded. The data are coded using variables \(x = \frac { p } { 10 }\) and \(y = m - 25\)

The results for the coded data are summarised below. (You may use \(\sum y ^ { 2 } = 2628.25 \quad \sum x y = 768.58 \quad \mathrm {~S} _ { x x } = 9.25924 \quad \mathrm {~S} _ { x y } = 74.664\) )

1. Show that \(\mathrm { S } _ { y y } = 626.025\)
2. Find the product moment correlation coefficient between \(x\) and \(y\).
3. Give a reason to support fitting a regression model of the form \(y = a + b x\) to these data.
4. Find the equation of the regression line of \(y\) on \(x\), giving your answer in the form \(y = a + b x\).
   Give the value of \(a\) and the value of \(b\) to 3 significant figures. A car's published miles per gallon is 44
5. Estimate the actual miles per gallon for this particular car.
6. Comment on the reliability of your estimate in part (e). Give a reason for your answer.

2. Two youth clubs, Eastyou and Westyou, decided to raise money for charity by running a 5 km race. All the members of the youth clubs took part and the time, in minutes, taken for each member to run the 5 km was recorded. The times for the Westyou members are summarised in Figure 1. \begin{figure}[h] \end{figure}

1. Write down the time that is exceeded by \(75 \%\) of Westyou members. The times for the Eastyou members are summarised by the stem and leaf diagram below.

   Stem	Leaf
   2	0	2	3	4											\(( 4 )\)
   2	5	6	8	8	8	9	9
   3	0	0	0	0	0	1	1	1	2	2	2	2	3	4	\(( 14 )\)
   3	5	5	5	7	9										\(( 5 )\)

   Key: 2|0 means 20 minutes
2. Find the value of the median and interquartile range for the Eastyou members. An outlier is a value that falls either
3. On the grid on page 7, draw a box plot to represent the times of the Eastyou members.
4. State the skewness of each distribution. Give reasons for your answers. $$\begin{aligned} & \text { more than } 1.5 \times \left( Q _ { 3 } - Q _ { 1 } \right) \text { above } Q _ { 3 }
   & \text { or more than } 1.5 \times \left( Q _ { 3 } - Q _ { 1 } \right) \text { below } Q _ { 1 } \end{aligned}$$
   \includegraphics[max width=\textwidth, alt={}]{b115bffa-1190-4a2b-b6f2-b006580e8dbd-06_2255_50_314_1976}
   \includegraphics[max width=\textwidth, alt={}, center]{b115bffa-1190-4a2b-b6f2-b006580e8dbd-07_406_1390_2224_262} Turn over for a spare grid if you need to redraw your box plot. \begin{figure}[h]
   \captionsetup{labelformat=empty} \caption{Only use this grid if you need to redraw your box plot.} \includegraphics[alt={},max width=\textwidth]{b115bffa-1190-4a2b-b6f2-b006580e8dbd-09_401_1399_2261_258}
   \end{figure}

1. A manufacturer of electric generators buys engines for its generators from three companies, \(R , S\) and \(T\).

Company \(R\) supplies 40\% of the engines. Company \(S\) supplies \(25 \%\) of the engines. The rest of the engines are supplied by company \(T\). It is known that \(2 \%\) of the engines supplied by company \(R\) are faulty, \(1 \%\) of the engines supplied by company \(S\) are faulty and \(2 \%\) of the engines supplied by company \(T\) are faulty. An engine is chosen at random.

1. Draw a tree diagram to show all the possible outcomes and the associated probabilities.
2. Calculate the probability that the engine is from company \(R\) and is not faulty.
3. Calculate the probability that the engine is faulty. Given that the engine is faulty,
4. find the probability that the engine did not come from company \(S\).

4. A discrete random variable \(X\) has probability function $$\mathrm { P } ( X = x ) = \left\{ \begin{array} { c l } k ( 2 - x ) & x = 0,1
k ( 3 - x ) & x = 2,3
k ( x + 1 ) & x = 4
0 & \text { otherwise } \end{array} \right.$$ where \(k\) is a constant.

1. Show that \(k = \frac { 1 } { 9 }\) Find the exact value of
2. \(\mathrm { P } ( 1 \leqslant X < 4 )\)
3. \(\mathrm { E } ( X )\)
4. \(\mathrm { E } \left( X ^ { 2 } \right)\)
5. \(\operatorname { Var } ( 3 X + 1 )\)

5. The weights, in grams, of a random sample of 48 broad beans are summarised in the table. (You may assume \(\sum \mathrm { fy } { } ^ { 2 } = 101.56\) ) A histogram was drawn to represent these data. The \(2.1 < x \leqslant 2.7\) class was represented by a bar of width 1.5 cm and height 1 cm .

1. Find the width and height of the \(0.9 < x \leqslant 1.1\) class.
2. Give a reason to justify the use of a histogram to represent these data.
3. Estimate the mean and the standard deviation of the weights of these broad beans.
4. Use linear interpolation to estimate the median of the weights of these broad beans. One of these broad beans is selected at random.
5. Estimate the probability that its weight lies between 1.1 grams and 1.6 grams. One of these broad beans having a recorded weight of 0.95 grams was incorrectly weighed. The correct weight is 1.4 grams.
6. State, giving a reason, the effect this would have on your answers to part (c). Do not carry out any further calculations.

6. The waiting time, \(L\) minutes, to see a doctor at a health centre is normally distributed with \(L \sim \mathrm {~N} \left( \mu , \sigma ^ { 2 } \right)\). Given that \(\mathrm { P } ( L < 15 ) = 0.9\) and \(\mathrm { P } ( L < 5 ) = 0.05\)

1. find the value of \(\mu\) and the value of \(\sigma\). There are 23 people waiting to see a doctor at the health centre.
2. Determine the expected number of these people who will have a waiting time of more than 12 minutes.

1. Events \(A\) and \(B\) are such that

$$\mathrm { P } ( A ) = 0.5 \quad \mathrm { P } ( A \mid B ) = \frac { 2 } { 3 } \quad \mathrm { P } \left( A ^ { \prime } \cup B ^ { \prime } \right) = 0.6$$

1. Find \(\mathrm { P } ( B )\)
2. Find \(\mathrm { P } \left( A ^ { \prime } \mid B ^ { \prime } \right)\) The event \(C\) has \(\mathrm { P } ( C ) = 0.15\) The events \(A\) and \(C\) are mutually exclusive. The events \(B\) and \(C\) are independent.
3. Find \(\mathrm { P } ( B \cap C )\)
4. Draw a Venn diagram to illustrate the events \(A , B\) and \(C\) and the probabilities for each region.