Clinical Calculation 5th Edition Computer Assignments Need to Know

Graphical Representations of Data Pie Chart

Graphical Representations of Data Bar Chart

Graphical Representations of Data Histogram

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

Web site to find data http://www.hospitalcompare.hhs.gov/Hospital/Search/SearchCriteria.asp?version=default&browser=IE%7C6%7CWinXP&language=English&pagelist=Home&dest=NAV|Home|Search|SearchCriteria&Type=State#astep1a http://www.census.gov/prod/www/abs/income.html

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.

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.

Assignment 3

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

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: 19 20 21 22 22 23 25 31 40 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

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: 19 20 21 22 22 25 31 40 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.

Assignment 4

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

Displaying Relationships Scatter Plot

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.

Look for patterns in the Data

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

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.

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

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

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

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

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.