html5-img
1 / 11

Chapter 2 Technology

Chapter 2 Technology. Target Goal: I can use technology to find the area under a curve and the corresponding zscore and raw value. 2.2d. Finding Areas (plus graph) with “Shadenorm”. Use “shadenorm” to find the area to the left or right of a point without referencing Table A.

hanne
Télécharger la présentation

Chapter 2 Technology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 2 Technology Target Goal: I can use technology to find the area under a curve and the corresponding zscore and raw value. 2.2d

  2. Finding Areas (plus graph) with “Shadenorm”. Use “shadenorm” to find the area to the left or right of a point without referencing Table A. • Press 2nd VARS(DIST):DRAW • Using exercise 2.45 with N(100,15). We want to find the percent of children whose WISC scores are above 125. • Set WINDOW: X[55,145]15 and Y[-0.008, 0.028].01.

  3. “Shadenorm” (You will need to experiment with the y settings to get a good graph. Also, turn off all other graphs; Y= and STAT PLOT.) • Complete the command 2nd VARS(DIST): DRAW: ShadeNorm(125,E99,100,15)

  4. “Shadenorm” • To find the area to the left of 85: • Shadenorm(-E99,85,100,15) • When using standard normal values, only specify the end points (mean 0 and standard dev. 1 are understood). • Shadenorm(1,2) finds the area above the interval z = 1 to z = 2.

  5. Finding Areas with “normalcdf” It is quicker than shadenorm but does not provide a picture. You do not need to set the Window specs. • Press 2nd VARS(DIST): normalcdf • Complete the command normalcdf (125,E99,100,15). • We can conclude that about 5%of WISC scores are above 125.

  6. Finding Areas with “normalcdf” • If normal values have already been standardized ( ie z scores), only specify left and right endpoints. • normalcdf(-1,1) returns 0.6827 • the area from z = -1 to z = 1 is approximately 0.6827 correct to four decimal places.

  7. Finding z-values with “invNorm” Calculates the raw or standardized (z score) value corresponding to a known area under a normal distribution or relative frequency. • Press 2nd VARS(DIST):invNorm

  8. Complete command invNorm(.9,100,15). • Compare with command invNorm(.9)

  9. The first command invNorm(.9,100,15)finds that the rawscore that has 90% of the scores below it from N(100,150) is x = 119. • The second command invNorm(.9) gives the standardized z score, z = 1.28, which has 90% of the scores below it.

  10. Exercise 1: Quartiles from a Normal Distribution Find the quartiles for the distribution of blood cholesterol levels for 14-year-old boys. This distribution is N (170 mg/dl, 30mg/dl). • Q1: calculator command? 2ndVARS:invNorm (.25,170,30) = 149.765

  11. Finding Quartiles • Q2(M): calculator command? 2ndVARS:invNorm (.50,170,30) = 170 • Q3: calculator command? 2ndVARS:invNorm (.75,170,30) = 190.235

More Related