WGU C 207 Data Driven Decision Making Exam Questions And Answers, Complete Solution

WGU C 207 Data Driven Decision Making Exam Questions And Answers, Complete Solution Blind Study A study performed where the participants are not told if they are in the treatment group or control group Interval Data Data that is ordered within a range and with each data point being an equal interval apart Reliable Data Data that is consistent and repeatable Systematic Errors Errors in measurement that are constant within a data set, sometimes caused by faulty equipment or bias Triple Blind Study A study performed where neither the treatment allocator nor the participant nor the response gatherer knows which group the participant is in Big Data A catch-phrase that describes a massive volume of data that is so large that it's difficult to process using traditional database and software techniques. Ratio Data Similar to interval data in that the data that is ordered within a range and with each data point being an equal interval apart, also has a natural zero point which indicates none of the given quality. Statistics The science that deals with the interpretation of numerical facts or data through theories of probability. Also, the numerical facts or data themselves. Nominal Data Sometimes called categorical data or qualitative data, this data type is used to label subjects or data by name Discrete Data Data that can only take on whole values and has clear boundaries Informational Bias A prejudice in the data that results when either the respondent or the interviewer has an agenda and is not presenting impartial questions or responding with truly honest responses, respectively Data Management The management, including cleaning and storage, of collected data. Analytics The discovery, analysis, and communication of meaningful patterns in data. Ordinal Data Data that places data objects into an order according to some quality with higher order indicating more of that quality Double-Blind Study A study performed where neither the treatment allocator nor the participant knows which group the participant is in Measurement Bias A prejudice in the data that results when the sample is not representative of the population being tested Valid Data Data resulting from a test that accurately measures what it is intended to measure Davenport-Kim Three-Stage Model A decision-making model developed by Thomas Davenport and Jinho Kim that consists of three stages: framing the problem, solving the problem, and communicating results Omission Error An error because something (for example, data or survey response) is missing. Continuous Data Data that can lay along any point in a range of data Random Errors Errors in measurement caused by unpredictable statistical fluctuations Relational Database A database structured to recognize relations among stored items of information. Benchmarks Standards or points of reference for an industry or sector that can be used for comparison and evaluation. Data Set A collection of related data records on a storage device. 3 Steps of Davenport-Kim Three-Stage Model 1. framing the problem 2. solving the problem 3. communicating results 1. framing the problem -Broad Definition of Business Problem -Review what has happened in the past -Type of Analysis -What Data and How to Collect 2. solving the problem -Specific question to analyze -Type of Data, data collection and data error -Analysis technique and analysis 3. communicating results -Presentation of analysis output -Recommendation 4 Types of Analytics 1. Descriptive Analytics 2. Diagnostic Analytics 3. Predictive Analytics 4. Prescriptive Analytics 1. Descriptive Analytics What happened? (hindsight) 2. Diagnostic Analytics Why did it happen? (insight) 3. Predictive Analytics What will happen? (foresight) 4. Prescriptive Analytics How can we make it happen? (foresight) Analytics chart measures 2 variables (x-y axis) Value and Difficulty (1 & 2) Descriptive/Diagnostic analytics The discovery and communication of meaningful patterns in data. The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes). Graphical analysis examples scatter diagrams, histograms, Praeto Charts, run charts (3) Predictive analytics Consists of techniques that use models constructed from -past data to predict the future or -ascertain the impact of one variable on another Predictive analytics Model • Regression , Time series, Statistical Quality Control Predictive analytics Simulation • It involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision. (4) Prescriptive Analytics Decision 'models' that indicate the 'best course of action' to take. Prescriptive analytics: Optimization Models Models that give the best decision subject to constraints of the situation Prescriptive analytics: Simulation Optimization Combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings. Prescriptive analytics: Decision Analysis -Used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. -It also employs 'utility theory', which assigns values to outcomes based on the decision maker's attitude toward risk, loss, and other factors. 3 questions about analysis techniques should you be able to answer -What is it -Where is it applied (see case studies Module 5) -What is it going to tell me (solve the business problem) A better understanding of consumer behavior through Marketing Analytics leads to: • The better use of advertising budgets • More effective pricing strategies • Improved forecasting of demand • Improved product line management, and • Increased customer satisfaction and loyalty Data Measurement (2 categories)