1 / 34

Density Curves and Normal Distributions

Density Curves and Normal Distributions. Section 2.1.

aislin
Télécharger la présentation

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. Density Curves and Normal Distributions Section 2.1

  2. Sometimes the overall pattern of a distribution can be described by a smooth curve. This histogram shows the distribution of vocab scores. We could use it to see how many students scored at each value, or what percent of students got 4’s, above 10, etc.

  3. Density Curves • A density curve is an idealized mathematical model for a set of data. • It ignores minor irregularities and outliers

  4. Density Curves Page 79-80

  5. Density Curves Page 79-80 0.303 0.293

  6. Density Curve • Always on or above the horizontal axis • Has an area of exactly 1 underneath it

  7. Types of Density Curves • Normal curves • Uniform density curves • Later we’ll see important density curves that are skewed left/right and other curves related to the normal curve

  8. Density Curve  Area = 1, corresponds to 100% of the data

  9. What would the results look like if we rolled a fair die 100 times? • Press STAT ENTER • Choose a list: highlight the name and press ENTER. • Type: MATH  PRB 5:randInt(1,6,100) ENTER • Look at a histogram of the results: 2ND Y= ENTER • Press WINDOW and change your settings • Press GRAPH. Use TRACE button to see heights.

  10. 30% or 0.3 20% or 0.2 10% or 0.1 Relative Frequency 1 2 3 4 5 6 Outcomes • What would the results look like if we rolled a fair die 100 times?

  11. In a perfect world… • The different outcomes when you roll a die are equally likely, so the ideal distribution would look something like this: An example of a uniform density curve.

  12. Other Density Curves • What percent of observations are between 0 and 2? (area between 0 and 2) Area of rectangle: 2(.2) = .4 Area of triangle: ½ (2)(.2) = .2 Total Area = .4 + .2 = .6 = 60%

  13. Other Density Curves • What percent of observations are between 3 and 4? Area: (1)(.2) = .2 = 20%

  14. Normal curve

  15. Density Curves: Skewed M Median: the equal-areas point of the curve Half of the area on each side

  16. Density Curves: Skewed Mean: the balance point of the curve (if it was made of solid material)

  17. Mean and Median Of Density Curves Just remember: • Symmetrical distribution • Mean and median are in the center • Skewed distribution • Mean gets pulled towards the skew and away from the median.

  18. Notation Since density curves are idealized, the mean and standard deviation of a density curve will be slightly different from the actual mean and standard deviation of the distribution (histogram) that we’re approximating, and we want a way to distinguish them

  19. Notation • For actual observations (our sample): use and s. • For idealized (theoretical): use μ (mu) for mean and σ (sigma) for the standard deviation.

  20. Normal Curves are always: • Described in terms of their mean (µ) and standard deviation (σ) • Symmetric • One peak and two tails

  21. Normal Curves Concave down Inflection points – points at which this change of curvature takes place. Inflection point σ Concave up µ

  22. Normal Curves

  23. The Empirical Rule • The 68-95-99.7 Rule -3 -2 -1 0 1 2 3

  24. The Empirical Rule • 68% of the observations fall within σ of the mean µ. 68 % of data -3 -2 -1 0 1 2 3

  25. The Empirical Rule • 95% of the observations fall with 2σofµ. 95% of data -3 -2 -1 0 1 2 3

  26. The Empirical Rule • 99.7% of the observations fall within 3σof µ. 99.7% of data -3 -2 -1 0 1 2 3

  27. Heights of Young Women • The distribution of heights of young women aged 18 to 24 is approximately normal with mean µ = 64.5 inches and standard deviation σ = 2.5 inches. 64.5 – 2.5 = 62 64.5 + 2.5 = 67 62 64.5 67 Height (in inches)

  28. Heights of Young Women • The distribution of heights of young women aged 18 to 24 is approximately normal with mean µ = 64.5 inches and standard deviation σ = 2.5 inches. 5 62 64.5 67 Height (in inches) 59.5 62 64.5 67 69.5 Height (in inches)

  29. Heights of Young Women • The distribution of heights of young women aged 18 to 24 is approximately normal with mean µ = 64.5 inches and standard deviation σ = 2.5 inches. 99.7% of data 62 64.5 67 Height (in inches) 59.5 62 64.5 67 69.5 Height (in inches)

  30. Shorthand with Normal Dist. • N(µ,σ) Ex: The distribution of young women’s heights is N(64.5, 2.5). What this means: Normal Distribution centered at µ = 64.5 with a standard deviation σ = 2.5.

  31. Heights of Young Women • What percentile of young women are 64.5 inches or shorter? 50% 99.7% of data 57 59.5 62 64.5 67 69.5 72 Height (in inches)

  32. Heights of Young Women • What percentile of young women are 59.5 inches or shorter? 2.5% 99.7% of data 57 59.5 62 64.5 67 69.5 72 Height (in inches)

  33. Heights of Young Women • What percentile of young women are between 59.5 inches and 64.5 inches? 64.5 or less = 50% 59.5 or less = 2.5% 50% – 2.5% = 47.5% 99.7% of data 57 59.5 62 64.5 67 69.5 72 Height (in inches)

  34. Practice • For homework: • 2.1, 2.3, 2.4 p. 83 • 2.6, 2.7, 2.8 p. 89

More Related