1 / 20

General Divisions

General Divisions. Descriptive Statistics Goal is to summarize or describe the data Inferential Statistics Using data from a sample to make inferences (generalizations) about the population. Major Descriptors. Center: Where the “middle” of the data is Variation: How spread out the data is

dora
Download Presentation

General Divisions

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. General Divisions Descriptive Statistics • Goal is to summarize or describe the data Inferential Statistics • Using data from a sample to make inferences (generalizations) about the population

  2. Major Descriptors • Center: Where the “middle” of the data is • Variation: How spread out the data is • Distribution: The shape of the distribution of the data (if the data follows a pattern) • Outliers: Data that is unusually separated from rest of data • Time: How data changes over time

  3. Frequency Distribution • A frequency distribution lists data values (or groups of data values) along with how many data had that value (the frequency, or count)

  4. Some data: Quiz scores

  5. Quiz scores: Frequency Distribution

  6. Quiz scores: Frequency DistributionUsing Classes

  7. Definitions • Lower class limits • Smallest numbers that can belong to a class • Upper class limits • Largest numbers that can belong to a class • Class boundaries • Numbers used to separate classes so that there are no gaps • For our purposes, we will just use lower class limits • Class midpoint • Add upper and lower limits and divide by 2 • Class width • The difference between consecutive lower class limits

  8. Example Lower class limits 12, 15, 17 Upper class limits 14, 17, 20 Class midpoints 13, 16, 19 Class width 3

  9. Constructing a Frequency Distribution • Choose number of classes you want • Usually 5 to 20, based on data and convenience • Calculate class width • (highest value – lowest)/number classes • Usually round (up) • Sometimes handy to work backwards • Choose starting point • Usually lowest value, or a little smaller

  10. Constructing a Frequency Distribution • Use starting point and class width to list other lower class limits • Add class width to previous lower limit • Add upper class limits • Tally data into frequency table

  11. Example: Hours slept by caffeine drinkers Data: 1.2, 2.9, 3.1, 3.5, 4.1, 4.6, 4.8, 5.0, 5.3, 5.3, 5.4, 5.7, 6.3, 6.7, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.2, 7.4, 7.5, 7.5, 7.8, 8.1, 8.2, 8.2, 8.5, 8.6, 9.3, 10.1 Choose number of classes: 5 (?) Class width: (10.1 – 1.2)/5 = 1.78 Lets round up to 2 and use that

  12. Example: Hours slept by caffeine drinkers Data: 1.2, 2.9, 3.1, 3.5, 4.1, 4.6, 4.8, 5.0, 5.3, 5.3, 5.4, 5.7, 6.3, 6.7, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.2, 7.4, 7.5, 7.5, 7.8, 8.1, 8.2, 8.2, 8.5, 8.6, 9.3, 10.1 Starting point: Probably 1.0 (could start at 0.0)

  13. Example: Hours slept by caffeine drinkers Data: 1.2, 2.9, 3.1, 3.5, 4.1, 4.6, 4.8, 5.0, 5.3, 5.3, 5.4, 5.7, 6.3, 6.7, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.2, 7.4, 7.5, 7.5, 7.8, 8.1, 8.2, 8.2, 8.5, 8.6, 9.3, 10.1 List lower class limits

  14. Example: Hours slept by caffeine drinkers Data: 1.2, 2.9, 3.1, 3.5, 4.1, 4.6, 4.8, 5.0, 5.3, 5.3, 5.4, 5.7, 6.3, 6.7, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.2, 7.4, 7.5, 7.5, 7.8, 8.1, 8.2, 8.2, 8.5, 8.6, 9.3, 10.1 Add upper class limits

  15. Example: Hours slept by caffeine drinkers Data: 1.2, 2.9, 3.1, 3.5, 4.1, 4.6, 4.8, 5.0, 5.3, 5.3, 5.4, 5.7, 6.3, 6.7, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.2, 7.4, 7.5, 7.5, 7.8, 8.1, 8.2, 8.2, 8.5, 8.6, 9.3, 10.1 Tally data

  16. Relative Frequency Relative frequency = class frequency / sum of all frequencies Relative frequencies are expressed as percents

  17. Example: Hours slept by caffeine drinkers Sum of Frequencies: 32 = sample size

  18. Cumulative Frequency Distribution • Class limits are replaced with “less than” statements • Frequency is frequency of data less than the class

  19. Example: Hours slept by caffeine drinkers

  20. Homework 2-2: 1, 5, 9, 15 The answer the books gives for class boundaries will be different than what we’ve discussed in class.

More Related