# HIV contraction rates

The topic revolves around controlling HIV contraction rates.
This is viewed by health care and medical professionals as a “current” problem.

1. Descriptive Analysis of Dataset
The first step in evaluating a set of data is to conduct univariate and multi-variate descriptive analysis of each variable in the dataset.
a. Continuous variables
Prepare tables that show the key continuous variable descriptive statistics.

Below each table describe the insights you gain about your dataset from the table.

(i). Central Tendency
Prepare a table that lists the Mean, Median and Mode for each of the 3 continuous variables in the dataset.
(ii). Variation
Prepare a table that lists the Maximum and Minimum values and the standard deviation for each of the 3 continuous variables in the dataset.
b. Categorical variables
Prepare tables that show the frequency of the values for the categorical variables in the data set. The tables should show both the count of the number of occurrences of each value and the % of the total number of observations (200) represented by each value. An easy way to create these tables is to use the PivotTable function within Excel. You may need to use the Microsoft or YouTube tutorials to learn how to use the PivotTable function. You can also generate these tables in Brightstat.

Below each table describe the insights you gain about your dataset from the table.

(i). One-way Table
For each categorical variable, prepare a table that shows the count and percentage of total of each value of the variable in the dataset.
(ii). Multi-way Table
For each pair of categorical variables, prepare a table that shows the count and percentage of total of each value of the variables in the dataset.

Gender by Smoke
Gender by Ethnic
Ethnic by Smoke
Smoke by (Ethnic by Gender)

2. Graphic Analysis of Dataset
One of the easiest techniques for developing an basic understanding of your data is graphic analysis. Prepare a graph for each pairing listed below. The first listed variable goes on the “Y” axis and the “by” variable goes on the “X” axis. For example a graph of Height by Age places the “Height” variable on the “Y” axis (vertical) and “Age” on the “X” axis (horizontal).

Below each graph describe the insights you gain about your dataset from the graph.

• FEV by Age
• FEV by Height
• Age by Ethnic
• Age by Gender
• Age by Smoke
• FEV by Ethnic
• FEV by Gender
• FEV by Smoke

3. Describe your overall interpretation of what the data seems to indicate.

• If you think you have enough information to formulate a research question at this point, clearly state the research question.
• If you stated a research question, what is your hypothesis?

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