1 / 18

Measures of Central Location (Averages) and Percentiles

Measures of Central Location (Averages) and Percentiles. BUSA 2100, Section 3.1. Introduction. Values of a variable tend to cluster around a central point. A measure of central location indicates a center, average, or typical value.

Télécharger la présentation

Measures of Central Location (Averages) and Percentiles

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. Measures of Central Location(Averages) and Percentiles BUSA 2100, Section 3.1

  2. Introduction • Values of a variable tend to cluster around a central point. • A measure of central location indicates a center, average, or typical value. • A measure of central location (average) helps summarize data concisely with one value.

  3. 1st Type of Average: Mode • Definition:The mode is the item that occurs most often. • Example 1 – Consider this data set: {96,91,91,87,84, 82,79,75,72,69,62}. • The mode is 91 since it occurs twice. • But 91 isn’t a “central” or “typical” value. • Another problem: some data sets have no mode, e.g. set above with one 91 removed.

  4. Mode (Page 2) • Some data sets have more than one mode. Example: Height of adults is bimodal (two modes). Why? • So the mode is not very useful and not reliable except for categorical data. • Example 2: (categorical data) Ask students to state their favorite kind of pie.

  5. 2nd Type of Average: Median • Definition: The median is the middle item of a ranked set of data. • It is the (n+1)/2 th item in a ranked set of n items. • Example 1: Find the median of this set of 7 items. {75,64,82,96,72,47,59}

  6. Median (Page 2) • Example 2: Add 89 to the previous set. {89,75,64,82,96,72,47,59}

  7. 3rd Type of Average: Mean • Definition: The sample mean, X-bar = Sum of the X’s divided by n, where n = number of items in the data set (sample). • Example 1: {89,75,64,82,96,72,47,59}

  8. Mean (Page 2) • Mean is the most widely used and best measure of central location except in one situation (to be discussed later). • Advantages of the mean: (1) More comprehensive because it uses all of the data (not just the center item(s)). • (2) Combined or weighted means can be calculated.

  9. Mean (Page 3) • Example 2 (combined mean): Class #1 had a mean test score of 80; Class #2 had a mean test score of 60. • What is the overall mean for both classes combined? • Is it 70, the average of 80 and 60?

  10. Mean (Page 4) • Class #1 has 40 students; Class #2 has 10 students.

  11. Mean (Page 5) • Example 3 (weighted mean): In a course, a professor gives 3 tests and a final exam, and requires a project. • The final exam counts 1 1/2 times as much as each test and the project counts twice as much as each test. • Charles Malone made 80, 74, 67, 86, and 90. What is his course average?

  12. Mean (Page 6) • Note: The unweighted mean is 79.4.

  13. Mean (Page 7) • The median is preferred to the mean if there are extreme values present. • Example 4: Incomes for 5 families are: {$30K, $40K, $50K, $60K, $820K} • Mean = $1,000,000 / 5 = $200,000, but this is not a “center” or “typical” value. • Median = $50,000 (more accurate)

  14. Percentiles • Definition: The pth percentile is a value that is > p percent of the values in a data distribution. • Values for p: 0, 1, 2,...,98, 99, 99.5, 99.9 • Example: If you were in the 86th percentile on a test, what does that mean?

  15. Percentiles (Page 2) • Three steps for calculating percentiles: • (1) Arrange data in ascending order • (2) Calculate index (rank): i = (p/100)* n. • (a) If i is not a whole number, round up to the next whole number. • (b) If i is a whole number, use i + .5 . • (3) Identify the answer.

  16. Percentiles (Page 3) • Example: {2450, 2500, 2650, 2430, 2355, 2260, 2490, 2680, 2540, 2775, 2525, 2465, 2610, 2390} are monthly salaries for 14 business graduates. • Find the 67th percentile. • Step 1: Arrange in ascending order: {2260, 2355, 2390, 2430, 2450, 2465, 2490, 2500, 2525, 2540, 2610, 2650, 2680, 2775}

  17. Percentiles (Page 4) • Find the 50th percentile.

  18. Percentiles (Page 5) • The 50th percentile is the median. • The 25th percentile is the 1st quartile. • The 75th percentile is the 3rd quartile.

More Related