Select four (4) variables (your ideal data-set) that you think would be important to investigate whether or not these statistics on heart disease apply to your township (community).

This assignment should be at least 450 words and no more than 1-1/2 pages long with background information about heart disease and addressing each question below. You must attach a paper containing your submission to this assignment so it can be checked via Turnitin. After reviewing this video on heart disease statistics, complete the following:

Part 1. Select four (4) variables (your ideal data-set) that you think would be important to investigate whether or not these statistics on heart disease apply to your township (community). A data set with 100 observations is attached (see below) to allow you select four (4) variables of your choice. SPSS code must be included to describe descriptive analysis you plan to use for each variable. For example, if you use the mean, provide SPSS code (copy-paste) or share steps to produce mean for that particular variable. Use this website to learn more about how to use SPSS or refer to the Help section with your SPSS software. For example to find the mean of a variable consider reviewing this step-by-step guide. For categorical variables, consider counts and percents for gender, heart disease or not, etc.

Part 2. Generate two hypotheses about why heart disease is prevalent in the United States. You should have a null hypothesis and an alternative hypothesis.

Now that you have observed the population statistics video on heart disease and identified six (4) variables from the Household Data data set, develop hypotheses based on your observations that would help better understand the epidemic of heart disease in America. Review the heart disease map of Governors Township (your community) labeled Map Key.api graphic to create a hypothesis for each of the following:

(1) Create Hypothesis #1?

(2) Create Hypothesis #2?

Be sure to study the map key legend (May Key.ai file attached below) to gain an understanding of each symbol on the map. Now that you have information graphic (statistics) of your community (TownRoads_Final.ai file attached below), describe your next steps in your investigation into heart disease. The less you rush, the more likely it is that you’ll perform the experiment correctly and record your findings accurately. Also, take some time to think about the best way to organize the data before you have to start putting numbers down. If you can design a table to account for the data, that will tend to work much better than jotting results down hurriedly on a scrap piece of paper. Generate a basic table using SPSS and identify the variables from the map and legend and the categories.

Record the data carefully so you get them right. You won’t be able to trust your conclusions if you have the wrong data, and your professor will know you messed up if the other three people in your group have “97 degrees” and you have “87.”

Part 3. Explain how your hypotheses connect to previous research data (from the video) and how your 4 variables would define the purpose of the experiment data.

Material to review

I. Review a video. The videos for the lab exercises can also be find in this dropbox. Select this link WEB ved-1 (1).mp4

II. Read about measurement and observation (see below) and from the lessons.

Measurement and observation are essential elements of science. Either process can be applied to the systematic collection of information, called data (datum is the seldom-used singular). Data sets are essential to hypothesis testing, and also for the purpose of generating scientific hypotheses. Whenever you measure or observe something in particular, the thing under scrutiny becomes a variable. Variables are either qualitative, quantitative, or ranks. Qualitative variables must be defined in such a way that every measurement falls into a discrete category. It is easiest to deal with the data if every measurement falls into only one of several mutually exclusive categories, i.e. awake vs. asleep, living vs. dead, male vs. female.

Quantitative data can be either counts, or measurements. Count data are discontinuous, and consist of whole numbers. Measurement data are obtained by applying an in instrument such as a ruler or a spectrophotometer. The precision of the measurement depends upon the fineness of the scale of the instrument. A linear caliper is much more precise than a meter stick, for instance. The accuracy of the measurement is defined by the extent to which the instrument was being applied correctly. Thus, precision and accuracy are not the same thing. An inaccurate measurement can be very precise. Precision takes time, is sometimes expensive, and may sacrifice accuracy in some cases. The best measuring instrument for one job will not be the best for the next.

Whenever measurements are made, there are potential sources of error. Instruments, and the humans who read them, make spontaneous random errors. Generally, these errors are small relative to the measurement, and tend to be “scattered” in a roughly normal distribution around the true value of the measurement. Thus, the average of multiple measurements will be closer to the true value than any single measurement. In some cases, however, other sources of error (systematic error) may creep into the measurements. Biases occur when instruments are not calibrated correctly, or when a person makes a consistent error in reading them.