week7 - Lecture 17 Nancy Pfenning Stats 1000 In general, if...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Lecture 17 Nancy Pfenning Stats 1000 In general, if we want to know the probability that any normal variable X falls in a given interval, we rewrite this as a problem about the standardized normal variable Z = X- , then use Table A.1, which gives the probability of a standard normal variable Z being less than any value from -3.49 to +3.49. Example If X is normal with mean 80, standard deviation 10, find... 1. P ( X 93) = P ( X- 80 10 93- 80 10 ) = P ( Z 1 . 3) = . 9032. 2. P ( X < 93) = P ( X- 80 10 < 93- 80 10 ) = P ( Z < 1 . 3) = . 9032. Note that for a finite data set like female class members heights, represented with a histogram, whether or not we had strict inequality was important: the proportion with height less than or equal to 65 could be quite a bit more than the proportion with height strictly less than 65. For an abstract infinite population of values represented with a density curve, the area under the curve X is the same as the area < X , and so we need not be concerned with strict inequality or not for normal distributions. 3. P ( X 80) = P ( X- 80 10 80- 80 10 ) = P ( Z 0) = . 5 This makes sense because the normal curve is symmetric, so the mean is the middle, and the proportion below the mean must equal the proportion above and together they equal 1, so each is .5. 4. P (65 < X < 100) = P ( 65- 80 10 < Z < 100- 80 10 ) = P (- 1 . 5 < Z < 2) = . 9772- . 0668 = . 9104 . 5. P ( X > 70) = P ( Z > 70- 80 10 ) = P ( Z >- 1) = P ( Z < +1) = . 8413. 6. P ( X < 35) = P ( Z <- 4 . 5) = 0. 7. P ( X > 35) = P ( Z >- 4 . 5) = 1. 8. P ( X < 120) = P ( Z < 4) = 1. 9. P ( X > 120) = P ( Z > 4) = 0. Just as we worked standard normal problems going in both directions, we have two kinds of non-standard normal problems. In the previous example, we were given a non-standard normal value x and were asked to find the corresponding probability. In the following example, we will be given a probability, and must find the corresponding non-standard normal value. The best approach is in two steps: first find the z value corresponding to the given probability, then unstandardize: Note that since z = x- , it follows that x = + z ....
View Full Document

Page1 / 4

week7 - Lecture 17 Nancy Pfenning Stats 1000 In general, if...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online