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DARE lesson 1

Data Analysis Research Experience

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Relationship between two variables

Mask wearing and gender: Is a certain gender more likely to wear masks than another gender?
Weather and traffic in NYC: Does better weather mean there will be more traffic on the roads? Does bad weather mean more traffic?
Vaccine rates and hospitalizations: If an area has higher vaccine rates, are they more likely to have fewer hospitalizations?
Other examples in health care: Patient satisfaction and Race, Age and Number of hospital visits.
Come up an example of two variables related to health care that could be related to each other and post it in the comments here
All of these are examples of BIVARIATE data (“bi” = two)

In this section, we are going to explore data that describes the relationship between two variables. Variables are basically factors that affect things or pieces of information. So in life, we often care about how two variables are related to each other. If you recall from the assessment you did at the beginning of the semester, a few questions were showing data that connected mask wearing and gender. In this question, the data was exploring whether aa certain gender is more likely to wear masks than other genders. So let’s spend some time thinking about different exampless of two variables that might be related to each other and where numerical data could help us figure that out. In all of these examples, we are basically asking a question about whether one variable (or thing) affects or is related to another variable (or thing). We all care about traffic, so Weather and traffic in NYC is an example of two variables that might be related to each other. The questsion is ---.

Vaccine rates and hospitalizations. This I something we think about a lot lately. The covid vaccine has shown to reduce hospitalizations related to covid, and therefore a question we may ak is: If an area.

So since this is a health care course, let’s think about some examples in health care. 1. Patient satisfaction aadd race. So the question here is: does race affect patient satisfaction rates? In other words, will certain races have more or fewer satisfied patients? Another example would be AGE and number of Hospital visits.

So here, you have a required comment: Come up with an example of two variables related to health care that could be relatd to each other and post it in the comments here.

So, you would be able to answer theses questions listed on this slide by lookoing at data. Data that compares the relationship between two variables I called BIVARIATE data.

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BIVARIATE DATA: INDEPENDENT and DEPENDENT variables

One variable is INDEPENDENT

The variable you are manipulating

One variable is DEPENDENT

The variable you are measuring

In the example from the last slide (vaccination rates and hospitalizations)

vaccine rates = independent variable

hospitalizations = dependent variable

In all these examples, we care about the effect of one variable on the other. For example, with the weather and traffic example, we care about whether WEAATHER affects traffic. So generally speaking, when you are dealing with bivariate data, one variable is considered to be INDEPENDENT and the other variable is considered to be DEPENDENT. The independent variable is the variable you are manipulating and dependent variable is the variable you are measuring. Another way to think about it is one variable should be dependent on the other variable. So if we think about weather and traffic, we aare asking whether traffic is dependent on weather. Therefore, the traffic is the dependent variable and the weather is the independent variable. In the example from the last slided aabout vaccine rates an hospitalizations, the vaccine rates = independent variable and hospitalizations is the dependent variable.

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Bivariate tables

	Currently covered by any health insurance	Not covered by any health insurance
HEALTH STATUS
Excellent	98006	7996
Very Good	97137	9502
Good	70463	8481
Fair	23032	2352
Poor	7861	439

INDEPENDENT

DEPENDENT

COMPARING HEALTH CARE ACCESS (having insurance) and HEALTH STATUS

Does having insurance affect your health status?

This table shows absolute numbers. Think about why it would be more useful to look at this data in terms of percentages.

In class, we will discuss the advantage of using percentages and learn how to transform this data into useful percentages.