C207 WGU Data Driven Decision Making Exam Questions and Answers Top Graded 2024

Descriptive - Past data only; Not predicting or optimizing Predictive - Past to predict the future; Predicting, no optimizing Prescriptive - Past to predict the future and optimizing Omission - Missing information Out of Range - Doesn't match the data or not true Reliable - Constant and repeatable. A measure of the instrument Valid - Measures what is intended to be measured Measurement bias - Includes representative sample, random, large enough sample Information bias - Ignore the purpose of the information collection; not truthful answers Big Data - Both structure and unstructured; to large to process using traditional database and software techniques Data mining - Process of discovering pattern in large data sets Why collect big data? - Used to encourage buying behavior Analytics - the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and and add value Variable - An expression that can be assigned to data Continuous data - Data that can lay along any point in a range of data (age 22.6 old) Discrete data - Whole values only and clear boundaries Nominal data - Categorical data used to label subjects in a study; discrete (male female) Ordinal data - Allows you to place objects on some in some kind of order according to some quality; discrete (black belts 3rd degree higher than 1st degree) Interval data - has order; all objects are equal interval apart; no natural zero point and zero does not represent the absence of the property measured; Continuous (time, date, temperature) Ratio data - Has a unique zero point - numbers can be compared as multiples of one another, continuous (income, stock, repeat customers) Observational studies - used when impractical and impossible to control the conditions of the study Prospective cohort study - Observe people going forward in time from the time of their entry into the study Experimental studies - All variable measurements and manipulations are under researcher's control. Experimental studies: Experimental units - Subjects or objects under observations Experimental studies: Experimental treatments - Procedure applied to each subject Experimental studies: Responses - Effects of the experimental treatments 1st step of statistical experiment - Identify the experimental units from which you want to measure something 2nd step of statistical experiment - Id the treatments and controls that you will use on control group 3rd step of statistical experiment - Generate a testable hypothesis construct validity - study actual measure what is being investigated content validity - Construct measures what it claims to measure Internal validity - Biases may have entered the study Blind study - participants are not told if they are in treatment group or control group Double blind - neither treatment allocator nor participant know which group participant is in Triple blind - participant, allocator and response gather do not know which group the participant is in