1. Clinical Calculation 5th Edition
Computer Assignments Need to Know

2. Graphical Representations of Data
Pie Chart

3. Graphical Representations of Data
Bar Chart

4. Graphical Representations of Data
Histogram

5. Symmetric and Skew Distribution If we look at the outline of histogram
Skewed to the Left
Skewed to the Right
Symmetric – Single Pick
Normal distribution
Symmetric – Two Picks

6. Web site to find data

7. Percent of Pneumonia Patients Given Initial Antibiotic(s) within 4 Hours After Arrival The rates displayed in this graph are from data reported for discharges January 2006 through December 2006.

8. Percent of Surgery Patients Who Received Preventative Antibiotic(s) One Hour Before Incision - The rates displayed in this graph are from data reported for discharges January 2006 through December 2006.

9. Assignment 3

10. Describing Distributions with Numbers
Mean - Average
Median – Measuring Center – Middle data
Minimum – Smallest data
Maximum – Greatest data
Mode – Most repeated

11. Minimum = 19, maximum = 40, Mode = 22
Describing Distributions with Numbers
Example 1: 20, 40, 22, 22, 21, 31, 19, 25, 23
Mean – Average
Median – Measuring Center
Minimum
Maximum
Mode
Sort the data:
Median: 9 different data + 1 is 10, the divide by 2 is 5 so the median is the 5th location. (22)
Minimum = 19, maximum = 40, Mode = 22

12. Minimum = 19, maximum = 40, Mode = 22.
Describing Distributions with Numbers
Example 2: 20, 40, 22, 22, 21, 31, 19, 25
Mean – Average
Median – Measuring Center
Minimum
Maximum
Mode
Sort the data:
Median: 8 different data + 1 is 9, the divide by 2 is 4.5 so the median is the average between data in 4th location and the 5th location. (22)
Minimum = 19, maximum = 40, Mode = 22.

13. Assignment 4

14. Relations between variables
Independent Variables (x-axis)
Dependent Variables (y-axis)
A dependent variable measures an outcome of a study.
Where independent variable explains or influences the changes in a dependent variable
Example:
1-The amount of time a student studies and the grade on the exam.
2-Car age and asking price for the car.
3- age and salary
4-the yield of flower and the amount of fertilizer used

15. Displaying Relationships
Scatter Plot

16. Displaying relationships
Scatter Plots
Shows the relationship between two variables
Values of independent variable are plotted on the horizontal axis.
Values of dependent variable are plotted on the vertical axis.
Each individual is displayed as a point with fixed value on the plot corresponding with its two independent and dependent variables.

17. Look for patterns in the Data

18. Measuring the Linear Associations between Variables
Correlation (r): Measures the direction and strength of the linear relationship between two variables.

19. Facts about Correlation
The choice between the independent and dependent variable does not influence its calculations.
Both variable has to be numbers.
Not influences by the units of measures
Positive correlation ( r ) indicates positive correlation between independent and dependent variable
negative correlation ( r ) indicates negative correlation between independent and dependent variable
Correlation r is always a number between –1 and 1.
r = +1 indicated strong positive relations.
r =-1 indicates strong negative correlations.
r = 0 indicates NO relationship between variables.
Describes linear relationships.
It is influenced by the value of outlier, if outlier exists in data.

20. Look for patterns in the Data
Use r = + or – 0.7 as guideline for establishing your conclusion.

21. Regression line
Describes how a dependent variable y changes as values of independent x increases.
Regression line is a line that describes the data.

22. Regression Line
It may not necessary crosses all the points in the data set. Unless correlation between the two variable is one (1). Otherwise you will find error when drawing and calculation the regression line.
This error is calculated by the difference between actual data (y-value) and predicated values of y for a given x.
Error = observed y – predicated y

23. Least Square Regression Line
Is the line that makes the sum of the squares of the error created as smallest possible.

24. Facts about least-squares regression (LSR)
Correlation coefficient (r):
Variation we expect as x moves and y moves with it along the regression line.
Positive correlations indicated positive increase in y,
where negative correlation indicated the decrease in y.
Correlation of independence (r2):
How successful the regression was in explaining the dependent variable.
It is always positive.