![]() ![]() From the completed scatter graph above, we can see that there is one point which is much further from the line of best fit than any of the other points, and this is the point that corresponds to the student who scored 85 with only 6 hours of revision. Since the line of best fit goes up and, generally, the points are close to this line, there is a positive correlation.Ĭ) Since the y values (the exam scores) increase as the x values (time spent revising) increase, we can say that the more revision you do, the better your exam score is likely to be.ĭ) An outlier is any point which is a long way from the line of best fit. This line should go through the middle of as many of the points as possible. Your completed scatter graph should look like the one below:ī) In order to work out the type of correlation shown, you will need to draw in a line of best fit. As far as the exam results are concerned, they range from 20 to 85, so it would be sensible to start at 0 and finish at 90 or 100, with each large box representing a change of 10 marks. As far as the scale is concerned, the hours range from 0 to 25, so it would be sensible to use either a large box (or half a large box) for every 5 hours. Taking this to a ridiculous extreme, a person could practically starve themselves to death, but this would not likely make them a fast runner!Ī) Since the top row of the data is the number of hours of revising, this should be plotted on the x-axis and the exam score should be plotted on the y-axis. Since all the points are very close to the line of best fit, this graph has strong positive correlation.Ĭ) Since the y variable increases as the x variable increases, this tells us that the time taken to run 5 kilometres is greater for a heavier runner (the heavier the runner, the slower the time / the lighter the runner, the quicker the time).ĭ) It would be inappropriate to find an estimate for the time taken for a runner of 40 kilograms since 40 kilograms is beyond the range of the data.Īlthough the data suggests that the lighter you are, the quicker you are, there has to be a limit. On the y-axis, you can use one large box, or even two large boxes for every 10 minutes.ī) As the x variable increases, the y variable increases, so there is a posit ive correlation. Since there are no values between 0 and 50, it makes sense to start the scale from 50. This could be before 45 minutes have elapsed.)Ī) Since time is the first row of the data table, time should be on the y-axis.Īs far as your scale is concerned, on the x-axis, it would make sense to use one box to represent 10 kilograms in weight. After a certain amount of time, the cup of tea will reach room temperature and will stay at this temperature. (The temperature of the cup of tea will not continue to drop forever. ![]() (A couple of degrees above or below 66 will be acceptable since drawing the line of best fit is never that precise.)Į) It would be inappropriate to find an estimate for the temperature after 45 minutes as 45 minutes is beyond the range of the data. Since all the points are very close to the line of best fit, this graph has strong negative correlation.Ĭ) Since the y variable decreases as the x variable increases, this tells us that the temperature of the cup of tea is reducing over time (the cup of tea is getting colder over time).ĭ) In order to find an estimate for the temperature of the cup of tea after 6 minutes, we need to locate 6 minutes on the x-axis and draw a vertical line from 6 until it touches your line of best fit, then draw a horizontal line to the left to find the corresponding temperature value. As the x variable increases, the y variable decreases, so there is a negative correlation. Draw a line that cuts through the middle of as many of the dots as possible. Your completed scatter graph should look like the below:ī) In order to work out what type of correlation there is, we need to draw in a line of best fit. (Since the temperature starts at 45\degree and goes up to 95\degree, you could start your temperature at 40\degree instead of 0\degree if you prefer.) On the y-axis, you can use can go up in increments of 10 or 20 degrees. As far as your scale is concerned, on the x-axis, it would make sense to go up in increments of one or two minutes. ![]() ![]() A) Since time is the first row of the data table, time should be on the x-axis. ![]()
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