Cleaning Up Data Report

Cleaning Up Data Report - etc.) V. At this point we have...

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Steps taken to clean up data I. Analyzed data and found that all non-credit card holders were dropped after demographic information was collected in order to properly weight data set. This number is still off from 800 but more was done to clean up data. II. Created new data set with Credit Card holders using SAVE OUTFILE Select if Syntax III. Users who did not ‘qualify’ were removed from data set(see variable qualify) IV.A couple of participants were removed from the data set due to erroneous values within in a single participant(e.g Age= 110, Interest Rate=110%
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Unformatted text preview: etc.) V. At this point we have 800 users in our data set and we set out to see if the frequencies and cross tabs match that of the CFPB one year after What did the Card Act One Year Later Report say about this data? I. 77% noticed disclosure of cost making a late payment a. Compare this to my 76.5% II. 48% noticed 3 yr payment plan to pay off balance a. Compare this to my 48.8% III. 80% noticed standard due date a. Compare this 79.6% IV.70% noticed disclosed consequences of only paying minimum payments a. Compare this to my 70.8% V....
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Cleaning Up Data Report - etc.) V. At this point we have...

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