1 / 36

Section 1.3 Density Curves and Normal Distributions

Learn about density curves, normal distributions, and their applications in statistics with examples and explanations. Explore how to interpret curves and calculate probabilities using normal distributions.

rosalynm
Download Presentation

Section 1.3 Density Curves and Normal Distributions

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. Section 1.3 Density Curves and Normal Distributions

  2. Basic Ideas • One way to think of a density curve is as a smooth approximation to the irregular bars of a histogram. • It is an idealization that pictures the overall pattern of the data but ignores minor irregularities. • Oftentimes we will use density curves to describe the distribution of a single quantitative continuous variable for a population (sometimes our curves will be based on a histogram generated via a sample from the population). • Heights of American Women • SAT Scores • The bell-shaped normal curve will be our focus!

  3. Density Curve Shape? Center? Spread? Page 64 Sample Size =105

  4. Page 65 Density Curve Shape? Center? Spread? Sample Size=72 Guinea pigs

  5. 2. What is the probability (i.e. how likely is it?) that a randomly chosen seventh grader from Gary, Indiana will have a test score less than 6? Two Different but Related Questions! • What proportion • (or percent) of seventh • graders from Gary, • Indiana scored below 6? Example 1.22 Page 66 Sample Size = 947

  6. Relative “area under the curve” VERSUS Relative “proportion of data” in histogram bars. Page 67 of text

  7. The classic “bell shaped” density curve. Shape? Center? Spread?

  8. Median separates area under curve into two equal areas (i.e. each has area ½) A “skewed” density curve. What is the geometric interpretation of the mean?

  9. The mean as “center of mass” or “balance point” of the density curve

  10. We usually denote the mean of a density curve by m rather than . • We usually denote the standard deviation of a density curve by s instead of s.

  11. Assume Same Scale on Horizontal and Vertical (not drawn) Axes. The normal density curve! Shape? Center? Spread? Area Under Curve? How does the standard deviation affect the shape of the normal density curve? How does the magnitude of the standard deviation affect a density curve?

  12. (aka the “Empirical Rule”) The distribution of heights of young women (X) aged 18 to 24 is approximately normal with mean mu=64.5 inches and standard deviation sigma=2.5 inches (i.e. X~N(64.5,2.5)). Lets draw the density curve for X and observe the empirical rule!

  13. How many standard deviations from the mean height is the height of a woman who is 68 inches? Who is 58 inches? Example 1.23, page 72

  14. Note: the z-score of an observation x is simply the number of standard deviations that separates x from the mean m.

  15. The Standard Normal Distribution (mu=0 and sigma=1) Notation: Z~N(0,1) Horizontal axis in units of z-score!

  16. Let’s find some proportions (probabilities) using normal distributions! Example 1.25 (page 75) Example 1.26 (page 76) (slides follow) Let’s draw the distributions by hand first!

  17. Example 1.25, page 75 TI-83 Calculator Command: Distr|normalcdf Syntax: normalcdf(left, right, mu, sigma) = area under curve from left to right mu defaults to 0, sigma defaults to 1 Infinity is 1E99 (use the EE key), Minus Infinity is -1E99

  18. Example 1.26, page 76 On the TI-83: normalcdf(720,820,1026,209) Let’s find the same probabilities using z-scores!

  19. The Inverse Problem:Given a normal density proportion or probability, find the corresponding z-score! What is the z-score such that 90% of the data has a z-score less than that z-score? • Draw picture! • Understand what you are solving for! • Solve approximately! (we will also use the invNorm key on the next slide) Now try working Example 1.30 page 79! (slide follows)

  20. Syntax: invNorm(area,mu,sigma) gives value of x with area to left of x under normal curve with mean mu and standard deviation sigma. TI-83: Use Distr|invNorm How can we use our TI-83s to solve this?? invNorm(0.9,505,110)=? invNorm(0.9)=? Page 79

  21. How can we tell if our data is “approximately normal?” Box plots and histograms should show essentially symmetric, unimodal data. Normal Quantile plots are also used!

  22. Histogram and Normal Quantile Plot for Breaking Strengths (in pounds) of Semiconductor Wires (Pages 19 and 81 of text)

  23. Histogram and Normal Quantile Plot for Survival Time of Guinea Pigs (in days) in a Medical Experiment (Pages 38 (data table), 65 and 82 of text)

  24. Using Excel to Generate Plots • Example Problem 1.30 page 35 • Generate Histogram via Megastat • Get Numerical Summary of Data via Megastat or Data Analysis Addin • Generate Normal Quantile Plot via Macro (plot on next slide)

  25. Normal Quantile plot for Problem 1.30 page 35

  26. Extra Slides from Homework • Problem 1.80 • Problem 1.82 • Problem 1.119 • Problem 1.120 • Problem 1.121 • Problem 1.222 • Problem 1.129 • Problem 1.135

  27. Problem 1.80 page 84

  28. Problem 1.83 page 85

  29. Problem 1.119 page 90

  30. Problem 1.120 page 90

  31. Problem 1.121 page 92

  32. Problem 1.122 page 92

  33. Problem 1.129 page 94

  34. Problem 1.135 page 95-96

More Related