1. UNR, MATH/STAT 352, Spring 2007

3. MATH/STAT 352: Quiz 0

4. A subset of the data from a study of a series of male patients from Greenlane Hospital in Aukland after a heart attack Goal of the study: How long will the patient live after the heart attack? UNR, MATH/STAT 352, Spring 2007

5. (Age, time, probability, etc.) (Color, surgery outcome, smoking, etc.) May take any value from some interval (probability) No order (surgery outcome) May take values from some grid (age in years) Order (Letter grade) UNR, MATH/STAT 352, Spring 2007

6. is determined by data you have and problem you consider Aging of a person is a continuous process Age is a quantitative, continuous variable Age in years (18, 25, 63,…) is quantitative, discrete Age as (Kid, Young, Middle-age, Senior) is qualitative, ordinal Time

7. A subset of the data from a study of a series of male patients from Greenlane Hospital in Aukland after a heart attack Goal of the study: How long will the patient live after the heart attack? UNR, MATH/STAT 352, Spring 2007

9. Original data: {8 3 6 4.5 4 4.5} Sorted data: {3 4 4.5 4.5 6 8} Simple graph: dot plot UNR, MATH/STAT 352, Spring 2007

10. Interesting features of the data emphasized by the dot plot UNR, MATH/STAT 352, Spring 2007

11. Exploiting gaps and clusters: UNR, MATH/STAT 352, Spring 2007

12. Data from Quiz 0 outliers body of data

13. Measurement Histogram is the most widely used statistical graph UNR, MATH/STAT 352, Spring 2007

14. Divide observational interval into subintervals, (also called bins, class intervals) Measurement Calculate number of observation within each bin n1n1 n2n2 n3n3 n4n4 n5n5 n6n6 n7n7 n8n8 n9n9 n 10 Draw a rectangle w/heigth = number of observations = frequency Relative frequency is the number of observations within a bin divided by the total number of observations UNR, MATH/STAT 352, Spring 2007

15. Measurement 12343124 Number of observations Measurement.05.1.15.2.15.05.1.2 Fraction of observations n=20 (sample size) k=8 (# of bins) n=20 (sample size) k=8 (# of bins) UNR, MATH/STAT 352, Spring 2007

16. 080 – right answer Data from Quiz 0

17. UNR, MATH/STAT 352, Spring 2007

18. UCLA, Stats 14, Fall 2004 UNR, MATH/STAT 352, Spring 2007

19. Data from Quiz 0

20. UNR, MATH/STAT 352, Spring 2007 Data from Quiz 0

21. UNR, MATH/STAT 352, Spring 2007 Data from Quiz 0

22. UNR, MATH/STAT 352, Spring 2007 Data from Quiz 0

23. UNR, MATH/STAT 352, Spring 2007 Data from Quiz 0

24. UNR, MATH/STAT 352, Spring 2007 WRB number Data from Quiz 0

25. UNR, MATH/STAT 352, Spring 2007 http://static.deliaonline.com/images/originals/cc444-apple-blackberry-pie-18775.jpg

26. UNR, MATH/STAT 352, Spring 2007

30. Central values and spread Measurement x 0 is the central value, characteristic value spread, most of the observed values UNR, MATH/STAT 352, Spring 2007

32. Definitions:  A population is the entire collection of objects or outcomes about which information is sought.  A sample is a subset of a population, containing the objects or outcomes that are actually observed.  A simple random sample (SRS) of size n is a sample chosen by a method in which each collection of n population items is equally likely to comprise the sample, just as in the lottery.

33. UNR, MATH/STAT 352, Spring 2007 Definition: A sample of convenience is a sample that is not drawn by a well-defined random method. Things to consider with convenience samples:  Differ systematically in some way from the population.  Only use when it is not feasible to draw a random sample.

34. UNR, MATH/STAT 352, Spring 2007 A SRS is not guaranteed to reflect the population perfectly. SRS’s always differ in some ways from each other; occasionally a sample is substantially different from the population. Two different samples from the same population will vary from each other as well.  This phenomenon is known as sampling variation.

35. UNR, MATH/STAT 352, Spring 2007 Definitions:  A tangible population is a finite population that consists of actual objects. Examples: People in our class, buildings in Reno.  A conceptual population consists of items that are not actual objects. Examples: All possible shootings from the riffle, all possible tossings of a coin, all possible results of weighting a rock sample.