Density Curves Normal Distribution Area under the curve

Learning Objectives By the end of this lecture, you should be able to: – Describe what is meant by a density curve – Be able to identify normal (bell-shaped), skewed, bimodal, uniform distributions from a density curve – Describe the most common type of distribution encountered in nature – Be able to estimate areas under a Normal density curve

* Density Curve Density curve: When applied to a histogram, a density curve is a line drawn as a smooth approximation to the graph. A density curve is a mathematical model of a distribution. That is, we do not draw this by hand. We will allow our statistical software to do it for us. Here we have a bell-shaped distribution. It gets this name because when you draw a density curve over the histogram, the curve is shaped like a bell. This, then, is the famous “bell curve”. The proper name for a bell-shaped distribution, is the normal distribution. You should know this term.

Left skewed Right skewed Bimodal Uniform Density curves can be in any shape. It all depends on the distribution. However, there are some shapes that we tend to see much more frequently than others. Here are some of the common distributions we’ve discussed. For each of these histograms, a density curve has been drawn over it.

You should be able to identify a distribution by looking at only the density curve. That is, even if the histogram is not visible. Try to identify each of these three: Right skewed Bimodal Uniform

* Normal Distribution If you took a large sample of people (or observations) and graphed any of the following: – Heights – Corn yield per year in Indiana – SAT (or ACT) Scores – Blood pressure – Age of graduate students at DePaul – Weight of M&Ms per large package – Etc, etc, etc You would see that they all result in a bell-shaped distribution. When looking at data, the bell-distribution the most common distribution that shows up in the ‘real world’. As a result, we give this distribution it’s own name: the Normal distribution. And because this distribution is so common, we’re going to spend quite a lot of time studying it and learning how to find out all kinds of statistics from it.

Example of a dataset that shows a Normal distribution One study looked at the gestation (pregnancy) time of a group of women who were given prenatal vitamins. After creating the histogram, the following density curve was drawn. You can see that it is a normal distribution. This tells us that the most common gestation period was a range in the area of, say, days. As you might expect, as you go further and further out (i.e. longer and longer or shorter and shorter gestation times), there are fewer and fewer women. For example, as you might expect, while there are some women who had gestatiosn of less than 210 days (or longer than, say, 290 days), they are relatively rare. That is a normal distribution: The majority of people cluster around some value in the middle (in this case, about 250 days), but as you go higher and higher (or lower and lower) you find relatively few observations.

A density curve drawn over a Normal distribution is called (not surprisingly) the Normal density curve (or just the ‘normal curve’). Notice that while the density curve is exactly symmetric, it does not perfectly outline the histogram. That is, a density curve is an idealized description of the data. Still, even though the curve is higher than the histogram at some points, and below it at others, the mathematical model used to generate the density curve will turn out to be very accurate for our calculations. The “Normal Curve”

Not all distributions are normal! However, do be aware that just because many things fall into a Normal distribution, many other things do not. – For example, income distribution is not Normal. (It is typically right-skewed). – The age at which people are diagnosed with Inflammatory Bowel Disease is typically bimodal.

“Normal” Curves Normal curves have the following properties: – Symmetric – Unimodal – Bell-shaped Curves like this are called ‘Normal curves’ and the data distributions they describe are called ‘Normal distributions’ The idea of a Normal curve does not imply that other kinds of curves are somehow abnormal! It’s simply the term that we use – and it is a term you must be comfortable using!

How we use density curves One of the reasons we love density curves, is that by estimating the area under the curve, we can make various predictions and calculations about the population. Important: Be sure you understand, however, that the rules we are going to study, only apply to density curves of Normal distributions. These tools will not apply to density curves for, say, skewed distributions. Example: Suppose we take our sample of 25 women’s heights, plot them on a histogram, and then create a density curve. If that the density curve turns out to show a Normal distribution, we can use this density curve to make all kinds of statistical estimates such as: – What percentage of women in our population would be more than 6’ tall? – What percentage of women are between 5’0 and 5’5? – What is the likelihood of encountering women sorter than 4’6? – What is the height of the tallest 90 th percetile of women? – Etc However, in order to do all of this, we must learn how to calculate the area under the density curve.

* Area under the curve Here is an example of a histogram and density curve showing the score results of a group of students on a certain exam. Scores range from 0 to 12. If we want to know the percentage of students who scored below, say, 6 on this exam, we would need to find out the area under the curve to the left of 6.0. It is shaded on this diagram. This is somewhere in the vicinity of 30%. Determining the exact number will be the subject of an upcoming lecture. Note: I hope it also makes sense that if the shaded area tells us that 30% of students scored below 6, it stands to reason that 70% of students scored higher than 6.

Mean of a Normal distribution On a Normal density curve, the peak / midpoint / midline is the mean. (Represented by the black line). I hope you can see that the area to the left of the line contains 50% of the area under the curve, while the area to the right also contains 50% of the area under the curve. In terms of the graph seen here, if we estimate the midpoint to be a score of 7, we can say that about 50% of the population scores below 7 and 50% scores above.

Examples: Area under the curve How would you determine the percentage of students who scored greater than 10? In this case, we would want to calculate the area under the curve above the score of 10. It would probably be somewhere around 5%. Again, we will learn how to accurately determine this number in an upcoming lecture. How would you determine the percentage of students who scored between 6 and 8? In this case, we would want to calculate the area under the curve between those two numbers. About 50% of students achieved a score higher than _____ ? Answer: Draw a line down the very center of the curve. The area under the curve to the right of that line represents 50% of students. That line is right about a score of 7. So you could say that 50% of students scored above 7 (and, or course, about 50% of students scored below 7).

* Estimate the area under the curve While we will shortly learn how to accurately estimate the area under a curve, you shold be able to make some ballpark ‘guesstimates’. Example: What percentage of students scored below 6 on this exam? Answer: On the graph, it would be the shaded area here. A ballpark estimate would be somewhere around 30-40%. Example: What percentage of students scored above 9? Answer: A reasonable guess might be a number, in the vicinity of 20-30%. Don’t worry about accuracy here, just focus on being in the general area. Example: What percentage of students scored less than 2? Answer: A very low number! Eg: 1% would be a good guess. Example: What percentage of students scored more than 7? Answer: Since 7 is right around the midpoint, then the area under the curve to the right of the midline is 50%. I will ask you to do at least a couple of these on your quiz and/or exams. However, you will not have to make super- accurate estimates – you just need to be in the ballpark.

More practice estimating: Example: About what percentage of women had a gestation longer than 250 days? Answer: About 50%. Example: About what percentage of women had a gestation less than 210 days? Answer: A reasonable guess would be a low-ish number such as 15%. Example: About what percentage of women had a gestation less than 310 days? Answer: A high number! Eg: 99% would be a good guess. Example: About 30% of women had a gestation longer than _____? Choose among the following: 210 days, 230 days, 250 days, 270 days. Answer: The only reasonable option here would be 270 days. Note the last question: Turning it around like that is a common way that stats people love to throw on exams!

Coming up… For the moment, we have been estimating the area under the curve. Shortly, we will look at how to accurately determine the area under a Normal density curve.