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- Quantitative investigations of variation can involve the interpretation of mean values and their standard deviations
- A mean value describes the average value of a data set
- Standard deviation is a measure of the spread or dispersion of data around the mean
- A small standard deviation indicates that the results lie close to the mean (less variation)
- Large standard deviation indicates that the results are more spread out

Two graphs showing the distribution of values when the mean is the same but one has a large standard deviation and the other a small standard deviation

**Comparison between groups**

- When comparing the results from different groups or samples, using a measure of central tendency, such as the mean, can be quite misleading
- For example, looking at the two groups below
- Group A: 2, 15, 14, 15, 16, 15, 14
- Group B: 1, 3, 10, 15, 20, 22, 20

- Both groups have the same mean of 13
- However, most of the values in group A lie close to the mean, whereas in group B most values lie quite far from the mean
- For comparison between groups or samples it is better practice to use standard deviation in conjunction with the mean
- Whether or not the standard deviations of different data sets overlap can provide a lot of information:
- If there is an overlap between the standard deviations then it can be said that the results are not significantly different
- If there is no overlap between the standard deviations then it can be said that the results are significantly different

A group of scientists wanted to investigate the effects of a specific diet on the risk of coronary heart disease. One group was given a specific diet for 8 weeks, while the other group acted as a control. After the 8 weeks scientists measured the diameter of the lumen of the main artery in the arm of the volunteers. The results of the experiment are shown in Table 1 below:

Use the standard deviations above to evaluate whether the diet had a significant effect?[2 marks]

Step one: find the full range of values included within the standard deviations for each data set

Experimental group before: 0.67 to 0.71mm

Experimental group after: 0.71 to 0.77mm

Control group before: 0.69 to 0.73mm

Control group after: 0.67 to 0.77mm

Step two: use this information to form your answer

There is an overlap of standard deviations in the experimental group before and after the experiment (0.67~0.71mm and 0.71~0.77mm) so it can be said that the difference before and after the experiment is not significant; [1 mark]

There is also an overlap of standard deviations between the experimental and control groups after the eight weeks (0.71~0.77mm and 0.67~0.77mm) so it can be said that the difference between groups is not significant; [1 mark]

The standard deviations of a data set are not always presented in a table, they can also be represented by standard deviation error bars on a graph.

- Plotting data from investigations in the appropriate format allows you to more clearly see the relationship between two variables
- This makes the results of experiments much easier to interpret
- First, you need to consider what type of data you have:
- Qualitative data (non-numerical data e.g. blood group)
- Discrete data (numerical data that can only take certain values in a range e.g. shoe size)
- Continuous data (numerical data that can take any value in a range e.g. height or weight)

- For qualitative and discrete data, bar charts or pie charts are most suitable
- For continuous data, line graphs or scatter graphs are most suitable
- Scatter graphs are especially useful for showing how two variables are correlated (related to one another)

- Whatever type of graph you use, remember the following:
- The data should be plotted with the independent variable on the x-axis and the dependent variable on the y-axis
- Plot data points accurately
- Use appropriate linear scales on axes
- Choose scales that enable all data points to be plotted within the graph area
- Label axes, with units included
- Make graphs that fill the space the exam paper gives you
- Draw a line of best fit. This may be straight or curved depending on the trend shown by the data. If the line of best fit is a curve make sure it is drawn smoothly. A line of best-fit should have a balance of data points above and below the line
- In some cases, the line or curve of best fit should be drawn through the origin (but only if the data and trend allow it)

- For linear graphs (i.e. graphs with a straight-line), the gradient is the same throughout
- This makes it easy to calculate the rate of change (rate of change = change ÷ time)

- However, many enzyme rate experiments produce non-linear graphs (i.e. graphs with a curved line), meaning they have an ever-changing gradient
- They are shaped this way because the reaction rate is changing over time

- In these cases, a tangent can be used to find the reaction rate at any one point on the graph:
- A tangent is a straight line that is drawn so it just touches the curve at a single point
- The slope of this tangent matches the slope of the curve at just that point
- You then simply find the gradient of the straight line (tangent) you have drawn

- The initial rate of reaction is the rate of reaction at the start of the reaction (i.e. where time = 0)

The graph below shows the results of an enzyme rate reaction. Using this graph, calculate the initial rate of reaction.

Step 1: Estimate the extrapolated curve of the graph

Step 2: Find the tangent to the curve at 0 seconds (the start of the reaction)

The tangent drawn in the graph above shows that 72 cm3 of product was produced in the first 20 seconds.

Step 3: Calculate the gradient of the tangent (this will give you the initial rate of reaction):

Gradient = change in y-axis ÷ change in x-axis

Initial rate of reaction = 72 cm3 ÷ 20 s

Initial rate of reaction = 3.6 cm3 s-1

When drawing tangents: always use a ruler and a pencil; make sure the line you draw is perfectly straight; choose the point where the tangent is to be taken and slowly line the ruler up to that point; try to place your ruler so that none of the line of the curve is covered by the ruler (it is much easier if the curve is entirely visible whilst the tangent is drawn).There is a handy phrase to help you remember how to calculate the gradient of a tangent or line. Rise over run means that any increase/decrease vertically should be divided by any increase/decrease horizontally.

转载自savemyexams

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