Column ABCD Variable Name Label Subject's Identification ID Number Codes N/A Sex Age Subject Sex Identified at Birth M= Male; F=Female Age at time of Visit Range 0-100 BMI Body Mass Index at Visit 0-70 E Income Annual Income Range 0-10,000; >10,000-30,000; >30,000-50,000; >50,000- 100,00; >100,000 >HS; HS or equivalent; F Education Level of Education Some College; College or Graduate School G Systolic Blood Pressure SBP reading at Visit 0-200 Unemployed; Employed Format X X X X X X part-time; full-time H Employment | Diabetes Subject Employment Status Self-Reported Diabetes Status employment X Y=Yes; N=No X J TV Time Spent watching TV per day in hours 0-24 X K Physical Time Spent working out per week in hours 0-168 X Σ Housing N Zipcode Housing Status Zip code of Current or Last Residence 0=rent; 1=Own; 2-Unhoused; 3=Living with Friends or Family N/A X X
Column ABCD Variable Name Label Subject's Identification ID Number Codes N/A Sex Age Subject Sex Identified at Birth M= Male; F=Female Age at time of Visit Range 0-100 BMI Body Mass Index at Visit 0-70 E Income Annual Income Range 0-10,000; >10,000-30,000; >30,000-50,000; >50,000- 100,00; >100,000 >HS; HS or equivalent; F Education Level of Education Some College; College or Graduate School G Systolic Blood Pressure SBP reading at Visit 0-200 Unemployed; Employed Format X X X X X X part-time; full-time H Employment | Diabetes Subject Employment Status Self-Reported Diabetes Status employment X Y=Yes; N=No X J TV Time Spent watching TV per day in hours 0-24 X K Physical Time Spent working out per week in hours 0-168 X Σ Housing N Zipcode Housing Status Zip code of Current or Last Residence 0=rent; 1=Own; 2-Unhoused; 3=Living with Friends or Family N/A X X
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 31PPS
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Question
Suppose you have access to a database with the variables listed below. Using the two variables BMI(the predictor) and diabetes(the outcome)
Name two types of data visualization examples you could use to explore the variables BMI and diabetes. (could be univariate (looking at one variable) or bivariate (looking at two variables together).
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