WGU C207 Data Driven Decision Making Module 1 Exam (Answered)

WGU C207 Data Driven Decision Making Module 1 Exam (Answered) Quantitative analysis is another name for what? statistics If a dataset is presented on a scale of hours per week, which measurement scale is used? ratio The use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and add value is called ________. Analytics Analytics makes use of all these things to drive decision-making and add value. First, second, third, fourth... What kind of data does this represent? Ordinal data Ordinal data places data objects into an ordered list according to some quality. This kind of data is also called categorical data. Nominal data Nominal data is used to put data into categories, which is why it is also known as categorical data. Assume you are measuring the various lengths of used pencils, and you collect the following data points (in inches): 1.37, 2.65, 2.78, 3.46, 3.91. What kind of data are these data points? continuous data In a set of continuous data, a point can lay along with any point in a range of data. A data point that can only take on whole values and has boundaries is called what? discrete Discrete data points are always whole integers. If a measure is consistent and repeatable it is said to be __________. reliable A measure must be repeatable in multiple locations under diverse conditions to be considered reliable. A small group from which you make an inference about an entire population is called a __________. sample If chosen properly, a sample can make predictions about an entire population. Cleaning and organizing data that has been collected is known as __________. data management Managing your data is as important as your analysis. The first step in the Davenport-Kim three-stage model is to frame the problem by recognizing what the problem is and then reviewing previous findings to begin to structure the analysis. (t/f) (True) Stage # 1 is to frame the problem by recognizing what the problem is and then reviewing previous findings to begin to structure the analysis. Stage #2 is to solve the problem. Stage #3 is to communicate your findings. The stage that involves the most intense statistics and data work is stage 3, which involves communicating results. True or false? False: The stage that involves the most intense statistics and data work is stage 2, which solves the problem. Stage 2 includes data modeling, data collection, and data analysis. The following are examples of nominal data: T/F male/female red/blue living/deceased True: Nominal data, also called categorical data, places objects into a category. Interval data has an order and all the objects are an equal interval apart. T/F True. Interval data has an order, and all the objects are an equal interval apart. You can not have a natural zero point in interval data. Observational studies are often used when a surveyor wants to adjust different variables and take note of the effects. T/F False. Observational studies are used when it is impractical or impossible to control the conditions of the experiment. Data is valid if it can be repeated by the same person in the same lab each and every time the experiment is executed. T/F F. Data is only valid if more than one individual can execute the experiments in different locations (different conditions are not required; this is part of the experiment ). Big Data a massive amount of data that is difficult to analyze using typical database and software techniques. Blind Study A study performed where the participants are not told if they are in the treatment group or control group. Continuous Data Data that can lie at any point in a range of data Data Management Cleaning and storage, of collected data Decision Tree Analysis The diagramming of possible alternatives and their expected consequences to formulate possible courses of action to make decisions. Discrete Data Data that can only take on whole values and has clear boundaries. Double-Blind Study A study was performed where neither the treatment allocator nor the participant knew which group the participant is in Expected Monetary Value (EMV) Analysis A statistical technique that calculates the average outcome when the future includes scenarios that may or may not happen Information Bias