Questions Further Statistics A AS (50 questions)

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OCR MEI Further Statistics A AS 2024 June Q1
7 marks Easy -1.2
1 The probability distribution for a discrete random variable \(X\) is given in the table below.
\(x\)0123
\(\mathrm { P } ( \mathrm { X } = \mathrm { x } )\)\(2 c\)\(3 c\)\(0.5 - c\)\(c\)
  1. Find the value of \(c\).
  2. Find the value of each of the following.
    The random variable \(Y\) is defined by \(Y = 2 X - 3\).
  3. Find the value of each of the following.
OCR MEI Further Statistics A AS 2024 June Q2
9 marks Standard +0.3
2 In a game of chance there are 32 slots, numbered 1 to 32, and on each turn a ball lands in one of them. You may assume that the process is completely random. You are given that \(X\) is the random variable denoting the number of the slot that the ball lands in on a given turn.
  1. Suggest a suitable distribution to model \(X\). You should state the value(s) of any parameter(s).
  2. Write down \(\mathrm { P } ( X = 7 )\). Players of the game start with a score of 0 . On each turn a player may choose to play the game by selecting a number. If the ball lands in the slot with that number then 15 is added to the player's score. Otherwise, the player's score is reduced by 1 . A player's score may become negative. A player decides to play the game, selecting the number 7 on each turn, until the ball lands in the slot numbered 7. You are given that \(Y\) is the random variable denoting the number of turns up to and including the turn in which the ball lands in the slot numbered 7.
  3. Determine \(\mathrm { P } ( Y \leqslant 15 )\).
  4. Determine the player's expected final score.
OCR MEI Further Statistics A AS 2024 June Q3
14 marks Standard +0.3
3 A glassware factory produces a large number of ornaments each week. Just before they leave the factory, all the ornaments are checked and some may be found to be defective. The Quality Assurance Manager of the factory wishes to model the number of defective ornaments that are found each week using a Poisson distribution. The numbers of defective ornaments found each week in a period of 40 weeks are shown in Table 3.1. \begin{table}[h]
\captionsetup{labelformat=empty} \caption{Table 3.1}
No. of defective ornaments in a week, \(r\)0123456\(\geqslant 7\)
No. of weeks with \(r\) defective ornaments, \(f\)2141353120
\end{table} You are given that summary statistics for the data are \(\sum f = 40 , \sum \mathrm { rf } = 84\) and \(\sum \mathrm { r } ^ { 2 } \mathrm { f } = 256\).
  1. By using the summary statistics to determine estimates for the mean and variance of the number of defective ornaments produced by the factory each week, explain how the data support the suggestion that the number of defective ornaments produced each week can be modelled using a Poisson distribution. The Quality Assurance Manager is asked by the head office to carry out a chi-squared hypothesis test for goodness of fit based on a \(\operatorname { Po } ( 2 )\) distribution.
  2. Table 3.2, which is incomplete, gives observed frequency, probability, expected frequency and chi-squared contribution. \begin{table}[h]
    \captionsetup{labelformat=empty} \caption{Table 3.2}
    No. of defective ornaments in a week, \(r\)Observed frequencyProbabilityExpected frequencyChi-squared contribution
    020.135345.41342.15232
    114
    2130.270670.43620
    357.2179
    \(\geqslant 4\)60.142880.01421
    \end{table}
    1. Complete the copy of the table in the Printed Answer Booklet.
    2. Carry out the test at the \(10 \%\) significance level.
  3. On one occasion a fork-lift truck in the factory drops a crate containing eight ornaments and all of them are subsequently found to be defective. Explain why the Poisson model cannot model defects occurring in this manner.
OCR MEI Further Statistics A AS 2024 June Q4
10 marks Standard +0.3
4 A chemist is conducting an experiment in which the concentration of a certain chemical, A , is supposed to be recorded at the start of the experiment and then every 30 seconds after the start. The time after the start is denoted by \(t \mathrm {~s}\) and the concentration by \(\mathrm { z } \mathrm { mg } \mathrm { cm } ^ { - 3 }\). The collected data are shown in the table below. Note that the concentration at \(t = 90\) was not recorded.
Time, \(t\)03060120150
Concentration of A, \(z\)40.031.327.512.811.4
The chemist wishes to plot the data on a graph.
  1. Explain why \(t\) should be plotted on the horizontal axis. You are given that the summary statistics for the data are as follows. \(n = 5 \quad \sum t = 360 \quad \sum z = 123.0 \quad \sum t ^ { 2 } = 41400 \quad \sum z ^ { 2 } = 3629.74 \quad \sum \mathrm { t } = 5835\) The regression line of \(z\) on \(t\) is given by \(\mathbf { z = a + b t }\) and is used to model the concentration of chemical A for \(t \geqslant 0\).
    1. Use the summary statistics to determine the value of \(a\) and the value of \(b\).
    2. Find the value of the residual at each of the following values of \(t\).
      • \(t = 60\)
      • \(t = 120\)
        1. Use the equation of the regression line to estimate the value of the concentration at 90 seconds.
        2. With reference to your answers to part (b)(ii), comment on the reliability of your answer to part (c)(i).
      Further experiments indicate that the model is reasonably reliable for times greater than 150 seconds up to about 200 seconds.
  3. Show that the model cannot be valid beyond a time of about 200 seconds.
OCR MEI Further Statistics A AS 2024 June Q5
10 marks Moderate -0.3
5 A student is investigating possible association between the amount of coffee that an adult drinks each day and the number of hours that they remain awake each day. In an initial investigation, a random sample of 8 adults is selected. The student obtains the following information from each of these adults: the amount of coffee that they drink each day and the number of hours that they remain awake each day. The student analyses the data and finds that the associated product moment correlation coefficient is 0.6030 .
  1. State one assumption that must be made for a hypothesis test based on the product moment correlation coefficient to be carried out. For the remainder of this question you may assume that this assumption is true.
  2. Carry out a test at the \(5 \%\) significance level to investigate whether there is any correlation between amount of coffee drunk and number of hours awake. The student conducts a second investigation which is similar to the first but this time based on a random sample of 30 adults. The product moment correlation coefficient for the new data is 0.5487 . The student carries out an equivalent hypothesis test to the one carried out in part (b), again using a 5\% significance level.
  3. Identify any differences between the two tests and their results. You do not need to restate the hypotheses or explain the conclusion in context.
  4. You may assume the following guidelines for considering effect size.
    Product moment
    correlation coefficient
    		Effect size
    0.1Small
    0.3Medium
    0.5Large
    Explain briefly why the results of the student's second investigation are likely to be more reliable than the results of the initial investigation.
OCR MEI Further Statistics A AS 2024 June Q6
10 marks Moderate -0.8
6 A bank monitors the amounts of cash withdrawn from a cash machine. It categorises any withdrawal of an amount of \(\pounds 50\) or less as 'small' and any withdrawal of an amount greater than \(\pounds 50\) as 'large'. Over a long period of time the bank finds that the proportion of withdrawals that are small is 0.43 .
The bank wishes to model a sample of 10 withdrawals to examine the number of small withdrawals.
    1. State a suitable probability distribution for such a model, justifying your answer.
    2. State one assumption needed for the model to be valid.
    1. Find the probability that exactly 4 of the 10 withdrawals are small.
    2. Find the probability that exactly 4 of the 10 withdrawals are large.
    3. Find the probability that no more than 4 of the 10 withdrawals are large.
  3. Find the probability that, in the 10 withdrawals, the 7th withdrawal is large and there are exactly 3 that are small.
OCR MEI Further Statistics A AS 2020 November Q1
12 marks Moderate -0.3
1 The random variable \(X\) represents the number of cars arriving at a car wash per 10-minute period. From observations over a number of days, an estimate was made of the probability distribution of \(X\). Table 1 shows this estimated probability distribution. \begin{table}[h]
\(r\)01234\(> 4\)
\(\mathrm { P } ( X = r )\)0.300.380.190.080.050
\captionsetup{labelformat=empty} \caption{Table 1}
\end{table}
  1. In this question you must show detailed reasoning. Use Table 1 to calculate estimates of each of the following.
    You should now assume that \(X\) can be modelled by a Poisson distribution with mean equal to the value which you calculated in part (a).
  2. Find each of the following.
OCR MEI Further Statistics A AS 2020 November Q2
12 marks Standard +0.3
2 A researcher is investigating the concentration of bacteria and fungi in the air in buildings. The researcher selects a random sample of 12 buildings and measures the concentrations of bacteria, \(x\), and fungi, \(y\), in the air in each building. Both concentrations are measured in the same standard units. Fig. 2 illustrates the data collected. The researcher wishes to test for a relationship between \(x\) and \(y\). \begin{figure}[h]
\includegraphics[alt={},max width=\textwidth]{ba3fcd3c-6834-4116-be0e-d5b27aed0a7e-3_595_844_513_255} \captionsetup{labelformat=empty} \caption{Fig. 2}
\end{figure}
  1. Explain why a test based on the product moment correlation coefficient is likely to be appropriate for these data. Summary statistics for the data are as follows. \(n = 12 \quad \sum x = 18030 \quad \sum y = 15550 \quad \sum x ^ { 2 } = 31458700 \quad \sum y ^ { 2 } = 21980500 \quad \sum x y = 25626800\)
  2. In this question you must show detailed reasoning. Calculate the product moment correlation coefficient between \(x\) and \(y\).
  3. Carry out a test at the \(5 \%\) significance level based on the product moment correlation coefficient to investigate whether there is any correlation between concentrations of bacteria and fungi.
  4. Explain why, in order for proper inference to be undertaken, the sample should be chosen randomly.
OCR MEI Further Statistics A AS 2020 November Q3
8 marks Moderate -0.3
3 A child is trying to draw court cards from an ordinary pack of 52 cards (court cards are Kings, Queens and Jacks; there are 12 in a pack). She draws cards, one at a time, with replacement, from the pack. Find the probabilities of the following events.
  1. She draws a court card for the first time on the sixth try.
  2. She draws a court card at least once in the first six tries.
  3. She draws a court card for the second time on the sixth try.
  4. She draws at least two court cards in the first six tries.
OCR MEI Further Statistics A AS 2020 November Q4
8 marks Easy -1.2
4 A fair 8 -sided dice has faces labelled \(1,2 , \ldots , 8\). The random variable \(X\) represents the score when the dice is rolled once.
  1. State the distribution of \(X\).
  2. Find \(\mathrm { P } ( X < 4 )\).
  3. Find each of the following.
OCR MEI Further Statistics A AS 2020 November Q5
8 marks Moderate -0.3
5 A doctor is investigating the relationship between the levels in the blood of a particular hormone and of calcium in healthy adults. The levels of the hormone and of calcium, each measured in suitable units, are denoted by \(x\) and \(y\) respectively. The doctor selects a random sample of 14 adults and measures the hormone and calcium levels in each of them. The spreadsheet in Fig. 5 shows the values obtained, together with a scatter diagram which illustrates the data. The equation of the regression line of \(y\) on \(x\) is shown on the scatter diagram, together with the value of the square of the product moment correlation coefficient. \begin{figure}[h]
\includegraphics[alt={},max width=\textwidth]{ba3fcd3c-6834-4116-be0e-d5b27aed0a7e-5_801_1644_646_255} \captionsetup{labelformat=empty} \caption{Fig. 5}
\end{figure}
  1. Use the equation of the regression line to estimate the mean calcium level of people with the following hormone levels.
OCR MEI Further Statistics A AS 2020 November Q6
12 marks Standard +0.3
6 A researcher is investigating whether there is any relationship between whether a cyclist wears a helmet and the distance, \(x \mathrm {~m}\), the cyclist is from the kerb (the edge of the road). Data are collected at a particular location for a random sample of 250 cyclists. The researcher carries out a chi-squared test. Fig. 6 is a screenshot showing part of a spreadsheet used to analyse the data. Some values in the spreadsheet have been deliberately omitted. \begin{table}[h]
ABCDEFG
1\multirow{2}{*}{}Observed frequency
2\(\boldsymbol { x } \boldsymbol { \leq } \mathbf { 0 . 3 }\)\(0.3 < x \leq 0.5\)\(0.5 < x \leq 0.8\)x > 0.8Totals
3\multirow[t]{2}{*}{Wears helmet}Yes26272346122
4No45312131128
5\multirow{2}{*}{}Totals71584477250
6
7Expected frequency
8\(\boldsymbol { x } \boldsymbol { \leq } \mathbf { 0 . 3 }\)\(0.3 < x \leq 0.5\)\(0.5 < x \leq 0.8\)\(\boldsymbol { x } \boldsymbol { > } \mathbf { 0 . 8 }\)
9\multirow[t]{2}{*}{Wears helmet}Yes34.648037.5760
10No36.352039.4240
11
12\multirow{2}{*}{}Contribution to the test statistic
13\(\boldsymbol { x } \boldsymbol { \leq } \mathbf { 0 . 3 }\)\(0.3 < x \leq 0.5\)\(0.5 < x \leq 0.8\)\(\boldsymbol { x } \boldsymbol { > } \mathbf { 0 . 8 }\)
14\multirow[t]{2}{*}{Wears helmet}Yes2.15850.06010.10871.8885
15No2.05730.05731.8000
16
\captionsetup{labelformat=empty} \caption{Fig. 6}
\end{table}
  1. Showing your calculations, find the missing values in each of the following cells.
    Carry out a hypothesis test at the \(10 \%\) significance level to investigate whether there is any association between helmet wearing and distance from the kerb.
  2. Discuss briefly what the data suggest about helmet wearing for different distances from the kerb.
OCR MEI Further Statistics A AS 2021 November Q1
4 marks Easy -1.3
1 The random variable \(X\) represents the clutch size (the number of eggs laid) by female birds of a particular species. The probability distribution of \(X\) is given in the table.
\(r\)234567
\(\mathrm { P } ( X = r )\)0.030.070.270.490.130.01
  1. Find each of the following.
    • \(\mathrm { E } ( X )\)
    • \(\operatorname { Var } ( X )\)
    On average 65\% of eggs laid result in a young bird successfully leaving the nest.
    1. Find the mean number of young birds that successfully leave the nest.
    2. Find the standard deviation of the number of young birds that successfully leave the nest.
OCR MEI Further Statistics A AS 2021 November Q2
10 marks Moderate -0.3
2 A football player is practising taking penalties. On each attempt the player has a \(70 \%\) chance of scoring a goal. The random variable \(X\) represents the number of attempts that it takes for the player to score a goal.
  1. Determine \(\mathrm { P } ( X = 4 )\).
  2. Find each of the following.
    • \(\mathrm { E } ( X )\)
    • \(\operatorname { Var } ( X )\)
    • Determine the probability that the player needs exactly 4 attempts to score 2 goals.
    • The player has \(n\) attempts to score a goal.
      1. Determine the least value of \(n\) for which the probability that the player first scores a goal on the \(n\)th attempt is less than 0.001 .
      2. Determine the least value of \(n\) for which the probability that the player scores at least one goal in \(n\) attempts is at least 0.999.
OCR MEI Further Statistics A AS 2021 November Q3
9 marks Standard +0.3
3 A student is investigating the link between temperature (in degrees Celsius) and electricity consumption (in Gigawatt-hours) in the country in which he lives. The student has read that there is strong negative correlation between daily mean temperature over the whole country and daily electricity consumption during a year. He wonders if this applies to an individual season. He therefore obtains data on the mean temperature and electricity consumption on ten randomly selected days in the summer. The spreadsheet output below shows the data, together with a scatter diagram to illustrate the data. \includegraphics[max width=\textwidth, alt={}, center]{5be067ff-4668-48d6-8ed2-b8dfa3e678f7-3_798_1593_639_251}
  1. Calculate Pearson's product moment correlation coefficient between daily mean temperature and daily electricity consumption. The student decides to carry out a hypothesis test to investigate whether there is negative correlation between daily mean temperature and daily electricity consumption during the summer.
  2. Explain why the student decides to carry out a test based on Pearson's product moment correlation coefficient.
  3. Show that the test at the \(5 \%\) significance level does not result in the null hypothesis being rejected.
  4. The student concludes that there is no correlation between the variables in the summer months. Comment on the student's conclusion.
OCR MEI Further Statistics A AS 2021 November Q4
6 marks Standard +0.3
4 It is known that in an electronic circuit, the number of electrons passing per nanosecond can be modelled by a Poisson distribution. In a particular electronic circuit, the mean number of electrons passing per nanosecond is 12 .
    1. Determine the probability that there are more than 15 electrons passing in a randomly selected nanosecond.
    2. Determine the probability that there are fewer than 50 electrons passing in a randomly selected period of 5 nanoseconds.
  2. Explain what you can deduce about the electrons passing in the circuit from the fact that a Poisson distribution is a suitable model.
OCR MEI Further Statistics A AS 2021 November Q5
7 marks Moderate -0.3
5 A fair spinner has five faces, labelled 0, 1, 2, 3, 4.
  1. State the distribution of the score when the spinner is spun once.
  2. Determine the probability that, when the spinner is spun twice, one of the scores is less than 2 and the other is at least 2.
  3. Find the variance of the total score when the spinner is spun 5 times.
OCR MEI Further Statistics A AS 2021 November Q6
11 marks Moderate -0.3
6 A health researcher is investigating the relationship between age and maximum heart rate. A commonly quoted formula states that 'maximum heart rate \(= 220\) - age in years'. The researcher wants to check if this formula is a satisfactory model for people who work in the large hospital where she is employed. The researcher selects a random sample of 20 people who work in her hospital, and measures their maximum heart rates.
  1. Explain why the researcher selects a sample, rather than using all of the people who work in the hospital. The ages, \(x\) years, and maximum heart rates, \(y\) beats per minute, of the people in the researcher's sample are summarised as follows. \(n = 20 \quad \sum x = 922 \quad \sum y = 3638 \quad \sum x ^ { 2 } = 47250 \quad \sum y ^ { 2 } = 664610 \quad \sum x y = 164998\) These data are illustrated below. \includegraphics[max width=\textwidth, alt={}, center]{5be067ff-4668-48d6-8ed2-b8dfa3e678f7-5_758_1246_1027_244}
    1. Draw the line which represents the formula 'maximum heart rate \(= 220 -\) age in years' on the copy of the scatter diagram in the Printed Answer Booklet.
    2. Comment on how well this model fits the data.
  3. Determine the equation of the regression line of maximum heart rate on age.
  4. Use the equation of the regression line to predict the values of the maximum heart rate for each of the following ages.
OCR MEI Further Statistics A AS 2021 November Q7
13 marks Standard +0.3
7 A biologist is investigating migrating butterflies. Fig. 7.1 shows the numbers of migrating butterflies passing her location in 100 randomly chosen one-minute periods. \begin{table}[h]
Number of butterflies01234567\(\geqslant 8\)
Frequency6918261316930
\captionsetup{labelformat=empty} \caption{Fig. 7.1}
\end{table}
    1. Use the data to show that a suitable estimate for the mean number of butterflies passing her location per minute is 3.3.
    2. Explain how the value of the variance estimate calculated from the sample supports the suggestion that a Poisson distribution may be a suitable model for these data. The biologist decides to carry out a test to investigate whether a Poisson distribution may be a suitable model for these data.
  2. In this question you must show detailed reasoning. Complete the copy of Fig. 7.2 of expected frequencies and contributions for a chi-squared test in the Printed Answer Booklet. \begin{table}[h]
    Number of butterfliesFrequencyProbabilityExpected frequencyChi-squared contribution
    060.03693.68831.4489
    190.121712.17140.8264
    2180.2160
    3260.6916
    4130.182318.22521.4981
    5160.120312.0286
    690.06626.61580.8593
    \(\geqslant 7\)30.05105.09660.8625
    \captionsetup{labelformat=empty} \caption{Fig. 7.2}
    \end{table}
  3. Complete the chi-squared test at the \(5 \%\) significance level.
OCR MEI Further Statistics A AS Specimen Q1
6 marks Moderate -0.8
1 The number of failures of a machine each week at a factory is modelled by a Poisson distribution with mean 0.45.
  1. Write down the variance of the distribution.
  2. Find the probability that there are exactly 2 failures in a week.
  3. State a distribution which can be used to model the number of failures in a period of 4 weeks.
  4. Find the probability that there are at least 2 failures in a period of 4 weeks.
OCR MEI Further Statistics A AS Specimen Q2
6 marks Moderate -0.8
2 The discrete random variable \(Y\) is uniformly distributed over the values \(\{ 12,13 , \ldots , 20 \}\).
  1. Write down \(\mathrm { P } ( Y < 15 )\).
  2. Two independent observations of \(Y\) are taken. Find the probability that one of these values is less than 15 and the other is greater than 15 .
  3. Find \(\mathrm { P } ( Y > \mathrm { E } ( Y ) )\).
OCR MEI Further Statistics A AS Specimen Q4
18 marks Moderate -0.3
4 The discrete random variable \(X\) has probability distribution defined by $$\mathrm { P } ( X = r ) = k ( 2 r - 1 ) \quad \text { for } r = 1,2,3,4,5,6 \text {, where } k \text { is a constant. }$$
  1. Complete the table in the Printed Answer Booklet giving the probabilities in terms of \(k\).
    \(r\)123456
    \(\mathrm { P } ( X = r )\)
  2. Show that the value of \(k\) is \(\frac { 1 } { 36 }\).
  3. Draw a graph to illustrate the distribution.
  4. In this question you must show detailed reasoning. Find
    • \(\mathrm { E } ( X )\)
    • \(\operatorname { Var } ( X )\).
    A game consists of a player throwing two fair dice. The score is the maximum of the two values showing on the dice.
  5. Show that the probability of a score of 3 is \(\frac { 5 } { 36 }\).
  6. Show that the probability distribution for the score in the game is the same as the probability distribution of the random variable \(X\).
  7. The game is played three times. Find
    • the mean of the total of the three scores.
    • the variance of the total of the three scores.
OCR MEI Further Statistics A AS Specimen Q5
8 marks Moderate -0.8
5 In a recent report, it was stated that \(40 \%\) of working people have a degree. For the whole of this question, you should assume that this is true. A researcher wishes to interview a working person who has a degree. He asks working people at random whether they have a degree and counts the number of people he has to ask until he finds one with a degree.
  1. Find the probability that he has to ask 5 people.
  2. Find the mean number of people the researcher has to ask. Subsequently, the researcher decides to take a random sample from the population of working people.
  3. A random sample of 5 working people is chosen. What is the probability that at least one of them has a degree?
  4. How large a random sample of working people would the researcher need to take to ensure that the probability that at least one person has a degree is 0.99 or more?
OCR MEI Further Statistics A AS Specimen Q6
12 marks Standard +0.3
6 A motorist decides to check the fuel consumption, \(y\) miles per gallon, of her car at particular speeds, \(x \mathrm { mph }\), on flat roads. She carries out the check on a suitable stretch of motorway. Fig. 6 shows her results. \begin{figure}[h]
\includegraphics[alt={},max width=\textwidth]{880026ad-1cd3-40bb-bc87-8dcc94bd9bbd-4_707_1091_1320_477} \captionsetup{labelformat=empty} \caption{Fig. 6}
\end{figure}
  1. Explain why it would not be appropriate to carry out a hypothesis test for correlation based on the product moment correlation coefficient.
  2. (A) One of the results is an outlier. Circle the outlier on the copy of Fig. 6 in the Printed Answer Booklet.
    (B) Suggest one possible reason for the outlier in part (ii) (A) not being used in any analysis. The motorist decides to remove this item of data from any analysis. The table below shows part of a spreadsheet that was used to analyse the 14 remaining data items (with the outlier removed). Some rows of the spreadsheet have been deliberately omitted.
    Data item\(x\)\(y\)\(x ^ { 2 }\)\(y ^ { 2 }\)\(x y\)
    15053.625002872.962680
    25053.325002840.892665
    137044.849002007.043136
    147044.249001953.643094
    Sum8406865115033779.740812
  3. Calculate the equation of the regression line of \(y\) on \(x\).
  4. Use the equation of the regression line to predict the fuel consumption of the car at
    (A) 58 mph ,
    (B) 30 mph .
  5. Comment on the reliability of your predictions in part (iv). }{www.ocr.org.uk}) after the live examination series. If OCR has unwittingly failed to correctly acknowledge or clear any third-party content in this assessment material, OCR will be happy to correct its mistake at the earliest possible opportunity.
    For queries or further information please contact the Copyright Team, First Floor, 9 Hills Road, Cambridge CB2 1GE.
    OCR is part of the }\section*{}
OCR MEI Further Statistics A AS Specimen Q3
10 marks Standard +0.3
3 In this question you must show detailed reasoning. A student is investigating what people think about organic food. She wishes to see if there is any difference between the opinions of females and males. She takes a random sample of 100 people and asks each of them if they think that organic food is better for their health than non-organic food. She will use the data to conduct a hypothesis test. The table below shows the opinions of these 100 people.
\cline { 3 - 4 } \multicolumn{2}{c|}{}Sex
\cline { 3 - 4 } \multicolumn{2}{c|}{}FemaleMale
\multirow{2}{*}{
Opinion on
organic food
}		Organic better3518
\cline { 2 - 4 }Not better2225
  1. Explain why the student should use a random sample.
  2. Carry out a test at the \(5 \%\) significance level to examine whether there is any association between a person's sex and their opinion on organic food. Show your calculations.