DMV road assessment for applicants who had applied for a new driver’s license. This dataset is simulated and contains information on whether an applicant passed the test, including applicant's ID, gender, age, race, trainings, and all road assessment indicators. By analyzing this data, various insights can be derived, such as predicting the likelihood of future applicants passing the road test.
This data would be useful for performing both univariate and multivariate analysis. Data is unique for multivariate statistics such as classification analysis. There are are 500 observations on 16 attributes and they include:
Gender: Gender identity of applicant.
Age Group: Factor of three levels indicating applicants' age group.
Race: Applicant's race or ethnicity.
Training: Factor of three levels indicating applicants' previous training before the test.
Signals: Assessment score of proper signalling, lane changes, and staying within lanes.
Yield: Assessment score of right of way to other vehicles and pedestrians.
Speed Control: Measure of ability to maintain appropriate speed based on traffic and road conditions.
Night Drive: Performance score out of 100 in simulated or actual night driving conditions.
Road Signs: Score on applicant's knowledge indicating familiarity and correct interpretation of road signs.
Steer Control: Score of the applicant's ability to control the vehicle under normal and stressful conditions.
Mirror Usage: Score on proper and consistent use of mirrors during various manoeuvres.
Confidence: Evaluator's subjective score on how confidently the applicant handled driving tasks.
Parking: Evaluation score for parallel, angle, and perpendicular parking tasks.
Theory Test: Score out of 100 on the in-car theoretical assessment covering traffic laws, road signs, and general driving theory.
Reactions: Factor of three levels indicating applicants' response to driving scenarios.
Qualified: Indicator for whether applicant qualifies for a driver's license.