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## AP®︎/College Statistics

### Course: AP®︎/College Statistics > Unit 5

Lesson 5: Analyzing departures from linearity- R-squared intuition
- R-squared or coefficient of determination
- Standard deviation of residuals or root mean square deviation (RMSD)
- Interpreting computer regression data
- Interpreting computer output for regression
- Impact of removing outliers on regression lines
- Influential points in regression
- Effects of influential points
- Identify influential points
- Transforming nonlinear data
- Worked example of linear regression using transformed data
- Predict with transformed data

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# Worked example of linear regression using transformed data

AP.STATS:

DAT‑1 (EU)

, DAT‑1.J (LO)

, DAT‑1.J.1 (EK)

Worked example of linear regression using transformed data. Adapted from 2007 AP Statistics free response, form b, question 6, part d.

## Want to join the conversation?

- Why don't we take the ln(3 *15)?(7 votes)
- I think you mean "why don't we take the ln(3*5), right? Meaning, "why don't we use the total area of the islands, instead of the area of just one of them," right? The line Sal calculates gives the proportion of animals to go extinct versus CONTIGUOUS area (i.e. area all together in one piece). Note that the regression line has a negative slope, so the smaller the piece of land, the bigger the proportion of animals that go extinct. So, to get an accurate proportion for each of the five smaller parcels, the area for just one must be used to calculate the proportion for that one. Then the proportion for each would be multiplied times the number of animals on that one. Since they're all are the same size, and since they all have the same number of animals, once the proportion is calculated for one, using the area for just that one, or 3 sq. km, then the total predicted extinctions for the group can be calculated by just multiplying by 5. So, 5*[16*(0.28996 - 0.05323*ln(3))] would be the correct calculation.(12 votes)

- The conclusion reached is backwards. It is question of what is the original data saying. If the regression showed a relationship between large PRESERVED islands and small PRESERVED islands then the conclusion would make sense. However, what it appears to be asking is whether one large or five small islands should be preserved given the proportion of extinct species that has been observed on UNPRESERVED islands of various sizes. The correct answer is to preserve the 5 small islands, thereby preserving 19 total species that would have otherwise become extinct.(5 votes)
- The conservation group believes that
*all*at-risk species will disappear once their habitat gets developed, so obviously the data shown must have been gathered from undeveloped lands.(5 votes)

- Why is it ln for natural log and not nl? It just confuses me.(2 votes)
- It should be mention that the only result that we should care about is, the number of species we are able to save. The number of species lost is not that relevant since it does not reflect the total.

To show this, you could have a third option where you have a little island with just 2 species, where you lost just one. No one would think this is the best choice. This would be the worst choice since you would have only saved one species.(2 votes) - How does one go from a result in the ln form to a regular result(2 votes)
- with the five nature preserves would't the save 71 species because with five nature preserves containing 16 species each the total species would be 80 at-risk species?(2 votes)
- Why the data of the table, does not relate to the data in the scatter plot? I mean, data point number 12 is not represented in the scatter plot, and its proportion value has been increased..(1 vote)
- shouldn't be ln(15) be used? (that is 3 times 5, there were 5 smalls lands, after all the total species 80 are being sum up too) In the calculators appears ln(3)(1 vote)
- table of linear regression of saving rate on population under 15(1 vote)
- Shouldn't we also consider the statistical error for each of those estimates? If the estimates overlap at say a 95% confidence interval, wouldn't that be good evidence that there is no effective difference between the conservation methods?(1 vote)

## Video transcript

- [Lecturer] We are told
that a conservation group with a long-term goal
of preserving species believes that all at-risk
species will disappear when land, inhabited by
those species, is developed. It has an opportunity to
purchase land in an area about to be developed. The group has a choice of
creating one large nature preserve with an area of 45 square kilometers and containing 70 at-risk species, or five small nature preserves, each with an area of
three square kilometers and each containing 16 at-risk species unique to that preserve. Which choice would you recommend and why? There are some interesting data here. It looks like some data they have gathered for different islands. We have their areas. This is the number of
species at risk in 1990. The species extinct by 2000. We can see for these various islands, we can see their areas and the proportion that got extinct. It looks like they're
plotted on this scatter plot. Now be very careful when you look at this because look at the two axes. The vertical axis is the
proportion extinct in 2000. It's these numbers. But the horizontal axes isn't
just a straight-up area. It's the natural log of the area. Why did they do this? Notice, when you make the horizontal axis the natural log of the area, it looks like there is
a linear relationship. But be clear, it's a linear relationship between the natural log of the area and proportion extinct in 2000. But the reason why it's valuable to do this type of transformation is now we can apply our
tools of linear regression to think about what
would be the proportion extinct for the 45 square kilometers versus for the five small
three-kilometer islands. Pause this video and see if you can figure
it out on your own. They gave us the regression data for a line that fits this data. All right. Now let's work through it together. To make some space because
all of it is already plotted right over here, and we
have our regression data. The regression line, we know
it's a slope in y-intercept. The y-intercept is right
over here, 0.28996. 0.2, this is, let's see,
one, two, three, four, five. 28996. It's almost 29. It's gonna be right over here would be the y-intercept. Its slope is negative 0.05 approximately. I could eyeball. It probably is gonna
look something like this. That's the regression line. Or another way to think about it is the regression line tell
us in general the proportion, proportion, obviously a proportion, shorthand for proportion extinct, is going to be equal to
our y-intercept 0.28996 minus 0.05323. We have to be careful here. You might be tempted
to say times the area, but no, the horizontal axis here is the natural log of the area. Times the natural log of the area. We can use this equation
for both scenarios to think about what is
going to be the proportion that we would expect to get
extinct in either situation, and then how many actual
species will get extinct. The one that maybe has fewer
species that get extinct might maybe the best one, or the one that the more
that we can preserve is maybe the best one. Let's look at the two scenarios. The first scenario is the
45 square kilometer island. This is just one, so times one. What is gonna be the proportion, proportion that we would
expect to go extinct? Based on this regression, it's going to be 0.28996 minus 0.05323 times the natural log of 45. If we want to know the actual
number that go extinct, so number extinct would be
equal to the proportion, would be equal to the proportion times how many, let's see,
the 45 square kilometers and it contains 70 at-risk species, so times our 70 species. We can get our calculator
out to figure that out. This is the proportion we
would expect to go extinct in the 45 square kilometer island based on our linear regression. This would be equal to. It looks like almost 9%. If we want to figure out the actual number we would expect to go extinct, we would just multiply that
times the number of species on that island, so times 70, and we get approximately about 6.11. Let me write that down. This is going to be approximately 6.11. We could say there would be approximately if we, let's just say six extinct, and this is all very approximate. Extinct. Approximately 64 saved. Now let's think about the other scenario. Let's think about the scenario where we have five small nature preserves. So it's going to be
three square kilometers times five islands. We're gonna just do the same exercise. Our proportion that goes
extinct is gonna be 0.28996, that's just the y-intercept
for our regression line, minus 0.05323, and you
have a negative sign there 'cause we have a negative slope, and this is not just times the area, it's times the natural log of the area. It's going to be three square kilometers. Three square kilometers. Our number extinct, Our number extinct is going to be equal to our proportion that we will calculate
in the line above times. Let's see. Five small nature preserves, each with an area of
three square kilometers and each containing 16 at-risk species. Five times 16, if each island has 16 and there's five islands,
that's going to be, five times 16 is 80. Times 80. Let's figure out what this is. Get the calculator out again. We are going to get. This is going to be the proportion. It's a much higher proportion. We'll multiply that times
our number of species, so times 80 to figure out how
many species will go extinct. We have here it's approximately 18.52. This is approximately 18.52. Another way to think about it is we're gonna have approximately. If we round let's just say
19 extinct, 19 extinct. And then if we have 19 extinct, how many are we gonna save? We're gonna have 61 saved. 61 saved. Even if you said 18
1/2 here and 61.5 here, on either measure, the 45
square, the big island is better. You're gonna have fewer
species that are extinct and more that are saved. Which choice would you recommend and why? I'd recommend the one large island because you're gonna save, you would expect to save more species, and you would expect that
fewer are going to get extinct based on this linear regression.