PowerPoint presentation for statistics.ppt

PowerPoint presention for vicmar

1. 1
Review
•Definitions of Statistics, Population,
Sample, Experimental Unit, Inference,
Parameter, Statistic, Variable, Reliability.
•Observational versus defined experiment
studies
•Sampling and Bias
•Classification of Variables (qualitative,
quantitative, discrete and continuous)

2. 2
Key Elements of a Statistical
Problem
•Describe the population
•Describe the variable/s of interest
•Describe the sample
•Describe the inference
•Describe sources of possible errors/bias

3. 3
Example
•Michael Gray and Jessica Sauerbeck researchers at Northern
Kentucky University designed and tested a speed training
program for a junior-varsity and varsity high school football
players Each participant was timed in a 40-yard sprint prior to
the start of the training program and timed again after completing
the program. Based on these sprint times, each participant was
classified as having an “improved” time, “no change” in time, or
a “decrease” in time. In a sample of 15 players selected from
different schools in the area, 13 had an “improved” time. The
results show that nearly 87% of players who participated in this
speed training program improved their sprint times.

4. 4
Chapter 2: Descriptive Statistics

5. 5
Chapter 2: Descriptive Statistics
•Two types of variables
–Qualitative
–Quantitative

6. 6
Chapter 2: Descriptive Statistics
•Two types of variables
–Qualitative
–Quantitative
•There are different ways to represent each
type of Data, but we will find there are more
techniques for describing Quantitative data.

7. 7
Qualitative Data
•To describe Qualitative data we must place
the data into a certain classes.

8. 8
Qualitative Data
•To describe Qualitative data we must place the
data into a certain classes.
•Each class has an associated class frequency
and relative frequency and class percentage.

9. 9
Qualitative Data
•To describe Qualitative data we must place the
data into a certain classes.
•Each class has an associated class frequency
and relative frequency and class percentage.
•Sometimes we keep track of these cumulatively.

10. 10
Example
•A total of 22 StFX students were tested and
found to have the following blood types:

11. 11
Example
•A total of 22 StFX students were tested and found to
have the following blood types:
Frequency is how often each class occurs
Blood Type Frequency
0 2
A 11
B 5
AB 4

12. 12
Example
•A total of 22 StFX students were tested and found to
have the following blood types:
Frequency is how often each class occurs
Blood TypeFrequencyCumulative
Frequency
0 2 2
A 11 13
B 5 18
AB 4 22

13. 13
Example
•A total of 22 StFX students were tested and
found to have the following blood types:
Blood TypeFrequency Relative
Frequency
0 2 2/22
A 11 11/22
B 5 5/22
AB 4 4/22
n
Frequency
Frequency Realtive 

14. 14
Example
•A total of 22 StFX students were tested and
found to have the following blood types:
Blood TypeFrequencyPercentage
0 2 9.09%
A 11 50.00%
B 5 22.73%
AB 4 18.18%
100*
Frequency
Percentage
n


15. 15
Example
•A total of 22 StFX students were tested and
found to have the following blood types:
100*
Frequency
Percentage
n

Blood
Type
FrequencyRelative
Frequency
Percentage
0 2 2/22 9.09
A 11 11/22 50.00
B 5 5/22 22.70
AB 4 4/22 18.18

16. 16
Example
•A total of 22 StFX students were tested and
found to have the following blood types:
100*
Frequency
Percentage
n

Blood
Type
FrequencyPercentageCumulative
Percentage
0 2 9.09 9.09
A 11 50.00 59.09
B 5 22.73 81.82
AB 4 18.18 100.00

17. 17
Qualitative Data
•With qualitative data (and any other data we wish to
separate into certain classes), tables, charts and
diagrams are often the best way to present the data.
•It gives us a visual feel for the data and pictures can
be more easily understood quickly and information
can be passed on without technical jargon.

18. 18
Example
•A total of 22 StFX students were tested and
found to have the following blood types:
100*
Frequency
Percentage
n

Blood
Type
FrequencyPercentageCumulative
Percentage
0 2 9.11 9.11
A 11 50.00 59.11
B 5 22.70 81.72
AB 4 18.28 100.00

19. 19
Example
• Pie Chart
50
22.7
18.2
9.1

20. 20
Example
• Bar Graph
0
2.4
4.8
7.2
9.6
12
A B AB O
11
4
5
2

21. 21
Example

•We may also ask you to draw a histogram where
the height of each bar is the class percentage or
class frequency.
0
2.4
4.8
7.2
9.6
12
A B AB O
11
4
5
2

22. 22
Example
• Pareto Graph – bar graph arranged from
highest to lowest.
0
2
4
6
8
10
12
A B AB O
Frequency

23. Use of “side-by-side” charts
Copyright © 2013
Pearson Education,
Inc.. All rights
reserved.

24. Physicians studied 114
coronary bipass patients
•57 given a drug to reduce blood-loss
• Concerns raised about side-effects
•Variables surveyed:
Did or did not receive drug
Type of complications:
1. Redo surgery
2. Post-op infection
3. Both 1 and 2
4. None
Copyright © 2013
Pearson Education,
Inc.. All rights
reserved.

25. Copyright © 2013
Pearson Education,
Inc.. All rights
reserved.
Figure 2.5 SAS summary tables for DRUG
and COMP

26. Copyright © 2013
Pearson Education,
Inc.. All rights
reserved.
Figure 2.6 MINITAB side-by-side bar
graphs for COMP by value of DRUG

27. Copyright © 2013
Pearson Education,
Inc.. All rights
reserved.
SPSS summary tables for COMP by value
of drug (used to produce preceding slide)