Lesson video

Hello and welcome to this lesson from the Unit: Reproduction in plants.

The title of today's lesson is Seed dispersal practical: data analysis.

Now, I hope you've had the opportunity to carry out a seed dispersal practical using a model seed, but if not, there's gonna be sample data that you can use throughout the lesson today.

My name's Mrs. Barnard and I'm going to be taking you through today's lesson.

So by the end of today's lesson, you should be able to display your data as a graph or a chart, and you should be able to identify patterns so that you can conclude on the relationship between the independent and the dependent variable.

Now we've got some key terms in today's lesson and our key terms are as follows: line graph, bar chart, line of best fit, and trend.

I'm gonna put the definitions up, so if you want to pause the video in order to write them down, you can, but we will be defining them as we go through today's lesson as well.

Okay, so our lesson today is in two parts.

The first part of today's lesson is displaying your data.

So we're going to display it as a graph, a line graph or a bar chart.

I'm gonna talk you through how to do that and then we're gonna look at identifying trends and patterns in our data and how we include that in our conclusions.

So let's get started with the first part of today's lesson, which is displaying data in graphs.

So after a practical investigation has been concluded, the data is then analysed.

So during the investigation, the measurements are written in a table.

So hopefully you've got a table full of all of the results that you collected from your investigation.

But if not, here is an example.

So you can see that we include all of the data for our investigation.

So any repeats that we carried out, and then we will have worked out a mean for those repeat measurements.

Now it might not be three repeats, you might have done more repeats if you've had the opportunity to do repeats at all.

You can see here we've got two pieces of anomalous data.

So anomalous data are pieces of data that don't fit the patterns, so they look different to the other repeats we've carried out.

It's one of the reasons that we carry out repeats so that we can see if any of our data is inaccurate.

So in this example here, when the seed had a wing length of two centimetres, we got two dispersals of one and two centimetres, and then we got a third one of seven.

So we're gonna take that seven as anomalous data and we are going to add those two together, one and two, and then we're gonna work out a mean.

So we're gonna divide it by two because we're only including two values.

So information in your table is displayed in a set way.

So you can see here that in the first column you have your independent variable.

So your independent variable is the variable that you chose to change in order to measure its effect on the dependent variable.

And the dependent variable always goes in the second column.

Now you can see that this dependent variable column is divided up into four other columns, which are the repeats and the mean.

The reason that these all come under the same heading is because they all give information on how far the seed was dispersed, so they come under the dependent heading column.

Also, you can see that in the headings they are full descriptive titles.

So again, it's very clear what was investigated.

And if it's applicable, there should also be units in these column headings.

Now that isn't always the case, but if there is units, they must be in the headings and they mustn't be in the body of the table.

In this case, you can see that the values for the independent variable are then shown below in the column.

Now that is the case, if your independent variable has number values, if not, those would be labels in that column.

And you can see that all of the dependent variable data is in this second section of the table with any anomalous data indicated.

So here is an example if you were doing a categoric investigation.

So that's where your independent variable, the things that you changed, have a label rather than a number.

So in this case, the seed model was made out of different types of paper.

So printed paper, card, and tissue paper, they're still shown there in the first column in the independent variable, but they're not number values.

So time for a quick check.

Which variable do you put in the first column of a table? So pause up and then we'll check if you've got it right.

Okay, so hopefully you got the fact that it's the independent variable that is in the first column of the table.

So after you've collected your data in a table, so hopefully you've got this from where you carried out your investigation.

The results that can then be displayed in a line graph or a bar chart.

So displaying data allows you to be able to see patterns and trends.

And this information you are going to include in your conclusion at the end of your investigation.

For the seed dispersal investigation, we are going to use one of these two ways to display our data.

So either a line graph or a bar chart, and I'm gonna talk you through both of these.

So in both line graphs and bar charts, the independent variable is displayed on the x-axis and the dependent variable is displayed on the y-axis.

Now the x-axis, remember, is the independent variable that's in the first column of your table.

So you transfer those values across to your x-axis, and then the dependent variable, it goes on the y-axis, which is in the second column of your table.

Now I like to remember that x is a cross and it goes across, and that helps me to remember to put the independent variable on the bottom of my graph.

So we've got the independent variable there and we've got the dependent variable there on the y-axis.

So a line graph is used to display data where the independent variable has numeric values.

These values are continuous.

So what that means is that the values can have any number within a range.

So examples will be things like height, weight, mass, length.

And all of those, if you think about the number could be any number.

So it could be one, 1.

1, or it could be 1.

11.

So it could be any number within a range.

It doesn't mean that you are measuring all of those values, but it could be any number within the range.

So therefore we call them continuous and they go on the x-axis.

A bar chart doesn't use numbers, it uses labels, but again, they go on the x-axis at the bottom of the graph.

So time for a quick check.

Which variable is displayed on the y-axis of a line graph or a bar chart.

So pause up and then we'll see if you've got it right.

Okay, so the correct answer is the dependent variable, that goes on the y-axis.

Okay, time for another check.

So you want to display some data where the independent variable is categoric.

Which of the following would you use? A line graph, a bar chart, or a table.

So again, pauses up and then we'll check back.

Okay, hopefully you got the correct answer is bar chart for categoric data.

So if you did, well done.

So now let's look at displaying our data.

So we're going to display it firstly as a line graph.

So you can see from this sample data in this table here, this is what the graph would look like.

So the information from the independent variable in the first column has been placed there on the x-axis, both the values, and the heading, and the units.

And on the y-axis you can see that the information from the heading is at the side with the units and the values are ranged up the side and scaled there.

All of the points are plotted with a cross because the centre of the cross is the exact point where the value from the independent variable matches with the value from the dependent variable.

So we don't plot with anything other than crosses.

And we plot the mean data in the final column.

So even though we show all of the data that we've collected in our investigation, the means, so that we can show that we've identified the anomalies, but we plot the mean data on our tables, so we only need the first and the last column.

So I'm gonna take you through how you would do this by hand because you are going to be doing this on graph paper.

So I've drawn this graph, so I'm gonna take you through my steps.

So first of all, you need to draw an evenly spaced scale on your x-axis.

Now what that means is that you need to go up in numbers that are evenly spaced based on the range of the values that you are looking at.

So you don't just plot the numbers that you actually investigated.

So we went from naught to six, and then you've got to work out how you can evenly space them in order that they fit on more than half of the paper.

You need to use more than half of your graph paper because it means that the results are gonna be spread out so it's easier for you to identify a pattern.

So in this case, you can see, because I was going from zero to six, I use the large boxes.

Now a good rule of thumb when you're deciding on scaling on an axis is to think about coins.

So you could go up in ones, twos, fives, tens, twenties, that's a good way of deciding how to go up.

Try to avoid numbers like three and nine 'cause they're difficult to divide by those little boxes in between, which you can see there's 10 little boxes in between each one.

Okay? So once you've done your x-axis, the scale, then you're going to do your y-axis scale.

Again, you won't know this until you've collected your results and calculated your mean, but you are going to have a scale that's a range from the lowest to the highest value.

Again, you've got to try to work out how you're gonna use more than half of your paper and what's the best way to scale it out.

So you can see here I've gone from naught to 14 and my scale is slightly different this time 'cause I've used those smaller boxes and then I've gone up in 1, 2, 3, 4.

But it takes up more of the paper so it will spread out our pattern.

Once you've decided on your scales, the next thing you have to do is make sure that both axes are labelled with the full descriptive titles that you take from the headings on your table.

And if they need a units, then you need to put your units on too.

Okay? So once you've got your axis sorted, we now need to think about plotting your points.

So what you need to do is you need to use a cross and you move up from the value of your independent variable, user ruler, and then you match across to the value on your dependent variable.

So you can see I've done this here.

And so for a value of two centimetres for the length of my seed wings, you can see I've got 1.

5 for the distance that it travelled.

It's really important that you do your plots accurately.

So you need to take time to use a ruler to make sure that these are in the correct space.

The middle of the cross should be exactly the point where these two values meet.

Okay? Continue to plot all of your plots now in the same way.

Once you've done that, then your line graph is complete.

So let's have a look at a bar chart now.

So if in your data you had a categoric values in your independent variable, you can see that they're shown in this table here.

Then you would do a bar chart.

Again, you would put your categories on the bottom of the independent variable on the x-axis, and you would put the distance, the seed dispersed up the y-axis.

You can see that again, both of those axes are labelled.

If they have units, they are labelled too, and we use bars.

The bars are all the same width and they have gaps between them.

So again, let's go through how you would do this on graph paper.

So to start with, again, you do your scale on your x-axis.

Now this time you're looking to space out your bars evenly.

So all your bars need to be the same width depending on how many you have.

And then you need to have gaps between them that are all the same width.

So sometimes this takes a little bit of time to try to get it right, but you can see I've marked them out here and I've put the categories underneath where the bars will be to show you where they are on the piece of paper.

And then let's have a look at our y-axis.

So the same as we did for the other graph, the line graph, I've spaced my dependent variable values up the side.

Now in the other example, they went from naught to 14.

In this case you can see that they go from naught to nine.

So I've actually used a slightly bigger scale.

I've put bigger gaps between each of the numbers one to nine, and that's so that I can space out my data so that I can see the pattern more clearly.

Once you've done that, again, you need to make sure that you've got descriptive titles on both of your axes and units if they are applicable.

In this case, there is only units on our dependent variables, so we've got them there on our y-axis.

So the next thing we need to do is we need to plot those bars accurately.

So we do that by using a ruler to draw across and then we draw the top of the bar across and we make sure it's the width that we marked out at the bottom on the x-axis.

And then we use a ruler to draw our lines down to the bottom to match where we marked it out.

You need to continue and do that for each of the bars.

We don't colour in the bars, we keep them nice and plain.

It's easy to read, keeps it nice and neat.

Okay, so time for a quick check.

So for the investigation title, the effect of height on seed dispersal, the data will be displayed in a bar chart.

Now do you think that's true or false, first of all? So I'll pause while you decide.

Okay, so that is false.

So which of these two statements justify why that is false? So this is statement one, a, and statement b.

So pause up while you decide and then we'll come back and see how you got on.

So the correct answer is that it is false because height is a continuous independent variable so it can have any value, so it would be displayed on a line graph.

So if you got that right, well done.

So it's time for a practise test for you now.

So use the data from your seed dispersal investigation to draw a graph or a bar chart.

Now if you don't have any data, that's fine, you can use the data in the table below and it is this data that I will be using to give you feedback on the graph.

So here's how the task goes.

Number one, select whether you are going to use a bar chart or a line graph to display your data.

And then once you've decided, please think about these criteria as you are drawing it.

So number one, independent variable on the x-axis and dependent variable on the y-axis.

Make sure that your axes have descriptive titles and units.

Make sure that your scale is evenly spaced and reaches over half of the paper, and your plots or bars are drawn accurately.

Okay? So you'll need to pause for this because this will take you a little bit of time, but come back and I'll give you some feedback after.

Okay, I hope everyone got the opportunity to draw a graph.

So let's have a look at some feedback for this then.

So for this particular data in this example, it would be a line graph, and that's because the independent variable that the height that the seed was dropped from has numeric values, it's continuous.

If you had label values, and the correct answer to that would be bar chart.

So let's have a look at this example line graph.

So we have got our independent variable title there.

We've got a dependent variable title on the side, and we've got units.

We've also got evenly space scale, and we've got all of our points plotted with an x.

So if you've got all of those things, then your graph should be right.

If you did a bar chart, most of these things apply.

The only thing that we need that's different is that you need to have your bars accurately plotted and you need to have gaps between them and they all need to be the same width.

So if you've got that right, then well done.

You've done a graph.

So now we need to move on to looking for patterns and trends from our graph.

So this is the second part of our lesson.

We're gonna be writing a conclusion, but to write a conclusion, we need to use our graph to find some patterns and trends in our data.

So plotting data on graphs and charts allows patterns to be more easily identified.

So have a look at this graph here.

How would you describe the pattern shown in the line graph? Perhaps talk to the person next to you and we'll see what you think.

Okay, so you may have phrased it in different ways, but hopefully you got the idea that the distance that the seed dispersed increased as the wing length increased, or you might have said it the other way around, as the length of wings increased, the distance the seed dispersed increased.

A pattern is much easier to identify if you put a line of best fit in.

So here's an example of a line of best fit that I would draw through this data.

The line should be drawn to show the pattern.

It's the line that best fits the data.

The line should be drawn so that there are equal numbers of points either side of the line, unless there are outliers.

If there's an outlier way out, you don't need to have that at an even distance away.

Sometimes I find it's useful to sort of squint with your eyes slightly and go, okay, what does the general pattern show me? And then that's where you draw your line.

So the line of best fit helps to conclude on the relationship between the independent and the dependent variables.

This is called the trend.

So you can see in this example here that the trend here would be as the length of the wings increases, the distance that the seed dispersed increases.

The description of the trend, it's useful if you start with the independent variable.

So you would say as the, and in this case we've got length of wings.

As the length of wings increases, the distance that the seed dispersed increases.

So let's have a go at that.

So we'll go through this one together and then you can have a go at one on your own.

So conclude on the trend in this line graph.

So we would say, as the height the seed is dropped from increases, the distance dispersed is also increased.

So you can see that both variables are included in the description of this trend.

So can you have a go at this one now, just pause the video while you do and then we'll come back and we'll see how you got on.

Okay.

So you can see in this example that the line of best fit goes the opposite way.

So again, we start with the bottom and we say as the mass added to the tail increases, the distance that the seed dispersed decreases.

So if you got that right, well done.

So the trend in a bar chart is identified by discussing the label values.

So for example, the seed model made of tissue disperse the furthest, followed by the paper model.

The card model disperse the smallest or the shortest distance.

A mathematical relationship may also be commented on.

So the tissue disperse three times further than the card, for example.

Sometimes there might not be much of a mathematical relationship, but if you can spot one, then you should say it in your conclusion.

So let's have a go here.

So we've got conclude the trend on this bar chart.

So we've got fan speeds.

So we've got three different fan speeds, low, medium, and high.

And we've got distance dispersed.

So we would say for this one, as the fan speed increases, the distance dispersed is also increased.

At medium fan speed, the distance dispersed is double compared to low.

So I found a mathematical relationship there and I've commented on it in the conclusion.

So you have a go at this one now.

So this one is about the shape of the wing, and again, we're measuring the distance dispersed.

So pause while you do it and then we'll come back and see how you've got on.

Okay, so for this one, you could have the wing shape affected the distance that the seed dispersed.

The triangle shape was double the dispersal of the oval shape, and the rectangle was double that of the triangle.

So if you found that mathematical relationship, then well done.

So let's have a quick check.

So choose the correct description of the trend that is shown in this table.

So pause while you decide, 'cause there's a bit to read there, and then we'll check back and see how you got on.

Okay, so the correct answer is C, as the length of the seed tail increased, the distance the seed dispersed decreased.

So if you got that right? Well done.

So let's move on to a practise task.

So now we need to use your graphs that you did in the first task, Task A.

So if you've got a line graph, you need to draw a line of best fit first, and then you need to describe the trend.

If you've got a bar chart, you describe the patterns, talking about the different category labels and finding any mathematical relationship.

So pause up while you do that, and then I'll give you some feedback after.

Okay, hopefully everyone's had the chance to write a conclusion.

So a line of best fit for this particular graph would've looked like this.

So if you use the model data, then this is what your line of best fit should look like.

You can see that piece of data at 250 is a bit of an outlier.

So you can see it's a bit further away from the line of best fit there.

So to describe this trend, we would say as the heights that the seed was dropped from increased, the distance that the seed dispersed also increased.

So that would be my conclusion.

So if you've got something similar, then well done.

So time for a summary of today's lesson.

So the scientific data is presented in a table in a set format.

This information can then be transferred over to a line graph or a bar chart.

Line graphs are used for data where the independent and dependent data are both continuous.

Bar charts are used where the independent variable is categoric.

The independent data is always on the x-axis and the dependent is on the y-axis.

The axes must have an evenly spaced scale, full titles and units if applicable.

For line graphs, a line of best fit can be used to show a pattern and indicate the trend in the data.

The trend is the relationship between the two variables.

In a bar chart, the pattern is descriptive, but may include details of mathematical relationships.

So well done for your work in today's lesson.