# BME100 f2015:Group2 1030amL1

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# OUR TEAM

 Name: Amar Joshi Name: Gage Schrantz Name: Anton Voronov Name: Colleen Rice Name: Cynthia Crocett Name: Lydia Chen

# LAB 1 WRITE-UP

## Independent and Dependent Variables

Independent variable: dosage. The dosage would be considered to be independent since it is not affected by other factors in the experiment Dependent variable: protein that causes inflammation in blood. The inflammation is dependent on the dosage, therefore, it is the dependent variable.

## Experimental Design

Groups
Control group— 10 mg is control group because we know it works.

5 groups—10 mg group, 8 mg group, 6 mg group, 4 mg group, 2 mg group. We chose to use intervals of two milligrams because it is a small enough interval between each group to see if it will still be effective while using the least amount of medicine, therefore it will reduce cost.

Age
65+, because the age of senior citizens is defined as 65+.

Number of subjects per group

20 total subjects per group, each group consisting of 10 males and 10 females.

## Subject Selection

The age of the subjects is 65 and up, struggle with inflammation issues, Out of those who sign up, use a random number generator to randomly choose 10 males and 10 females per group. This will allow the groups to be as random as possible. When selecting people of different age groups, we will avoid bias of getting an unbalanced group as compared to the others. An example of this would be a group that has lots of 65 year old's and another group of 90 year old's.

## Sources of Error and Bias

Age range in each group—if the group isn’t balanced (has too many 65 year olds compared to a group that has too many 90 year olds) this could cause issues. We can fix the problem by balancing the ages in each group.

Subjects could have a protein deficiency which could throw off the data. We will avoid this by having subjects take a physical and rule out all protein related illnesses

If the elderly are already taking medicines that affect inflammation this could throw off the data. We will avoid this by excluding people who are taking similar medications