WGU C207 Data-Driven Decision Making Exam Guide, Answered (Verified Solution)

WGU C207 Data-Driven Decision Making Exam Guide, Answered (Verified Solution) Define analytics Turning information into knowledge and developing fact based strategies to gain a competitive edge. Descriptive/Diagnostic analytics Encompasses the set of techniques that describes what has happened in the past. 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. Prescriptive analytics Decision models that indicate the best course of action to take. Three characteristic of big data 1. Structured and unstructured large volumes 2. Analytics is used to discover and communicate meaningful patterns 3. Analysis of big data requires a system of organization Davenport-Kim Three-Stage Model* A decision-making model developed by Thomas Davenport and Jinho Kim that consists of three stages: 1. Framing the problem, (why are you trying to do this and what do we already know) 2. Solving the problem (Choosing the model, collecting the data, analyzing the model) 3. Communicating results (Communicating and acting on the results) Categorical or discrete data variables Represents types of data that can be divided into groups or categories. Example: race, sex, age group, and educational level, happy, etc. There are two types of categorical data Nominal and Ordinal Nominal data* Identifies, groups, or categories only. Data cannot be arranged in an ordering scheme. They are used to name or label a series of values. Examples, names, pass/fail, gender, race, the color of the eye Data can not be ordered and makes no sense to calculate means or standard deviations off this. Ordinal data* Data is placed into some order by some quality. They are usually non-numeric captions like happiness or discomfort. It is the ORDER that matters. They provide good information about the order of values, like a customer satisfaction survey. Examples: top ten best cities to live in or top 10 college football teams. (ordinal-order matters) Numerical or continuous variables They can accept any value and can measure nearly anything. Height, weight, BP, etc... Two types of numerical data Interval and Ratio Interval data* It has an order to it and has equal intervals apart. It gives us the order of values with the ability to quantify the difference between each one. Differences between values can be found and meaningful, but they DO NOT have a true 0. ( no temperature, no true 0 time, or no true 0 dress size) These can be added or subtracted but not multiplied (interval means space in between which is the important thing to remember). Ratio data* Data similar to interval data but does have an absolute 0. Ratios are meaningful. (Length, Width, Weight, Distance, age, income, stock prices, repeat customers...) Data can be added, subtracted, multiplied, or divided. Qualitative research Data not characterized by numbers, usually textual, visual, or oral responses. Examples: Hair color, car colors, letter grades, types of coins in a jar. ANYTHING THAT DOES NOT HAVE A NUMER IN IT! Quantitative research Gives you numerical data. Three elements to a quantitative study design