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![]() However, these measures are almost always descriptive statistics arising from observational data so be careful to examine possible confounders before drawing conclusions. Relative risks and increased risks are reported in the news all the time. Increased risk interpretation: The risk of knee injury during a high school sporting event is 48.5% higher for male athletes than for female athletes. Relative risk interpretation: For each high school sporting event they take part in, a male athlete is 1.485 times more likely to experience a knee injury than a female athlete. Relative risk = Risk males / Risk females = 3.40 / 2.29 ≈ 1.485 ![]() For example, we might compare the risk of knee injuries for males to the risk for females competing in high school sports. Risks between groups or between different situations are then compared using the relative risk and the increased risk. Conditional Percents for Data in Table 6.2 Credit Card Response Figure 6.4 is an example of a cluster bar graph that displays the conditional percents for the data found in Table 6.3. Of course, the comparison of interest might also be displayed graphically in a cluster bar graph. However, since this doesn't usually happen, it is good practice to include the percentages most relevant to the problem at hand in the table and to include a total that allows the reader to quickly pick out what is adding to 100%. In this case, it was trivial to convert the counts into percents because the sample size is exactly 100 for each sample. Each of these percents is called conditional percents because each calculation is restricted to or contingent on the year in school. Table 6.3 shows the conversion of counts to percents for this sample. In this example, the most relevant percentages of interest for comparison are the ones that condition in the class rank. Conditioning on credit card ownership, we find that the percentage of credit card holders in the study who are seniors = 81 / 254 or about 32%. looking at the distribution within each column separately), we find that the percentage of Seniors who have a credit card is 81%. This is an example of a 2 × 4 contingency table because there are 2 rows and 4 columns to the data in the table. Responses to Credit Card Ownership by Year in School Credit Card Response The results for the responses to this question are found in Table 6.2 below. ![]() Question: Do you currently own at least one credit card? Numerical Summary of Hometown Description HometownĬonsider the following survey question that was asked of four different samples of Penn State students: 100 freshman (Fr), 100 sophomores (So), 100 juniors (Jr), and 100 seniors (Sr). Table 6.1 shows the distribution and the calculations for the data in Example 6.1. For one variable that just involves dividing the count in each category by the total to get the proportion - and then converting those to percents by multiplying the proportions by 100% (if percents are desired). Now, it is important to remember that before data is displayed in a bar graph like the one above, it must first be tabulated to calculate the percents that let us see the variable's distribution. The bar graph provides a more informative picture than a pie chart in this case as it allows us to see the natural ordering of the categories. 0 10 14 53 25 8 20 30 40 50 60 Hometown Rural Suburb Small town Big city Pe r centįigure 6.2. ![]()
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