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 1. Units, this is the subject or object that is being observed. (Example: Students are broken up into three different groups) 2. Treatments, the procedures being applied to each subject. (Examples: which learning is best, lecture, simulation, or no treatment. Each unit gets a different treatment in this example) 3. Responses, the effect of the experimental design. (What were the results of each group, who got the best grades based off the different treatments.) Data managements responsibility Clean and organize data sets, make sure clean good data is provided. 5 questions asked about data management 1. Is the data relevant to your business 2. Is the data complete, do you have all the data 3. Is the data accurate 4. Is the data available and assessable 5. Is the data timely and up to date Do all data measurements have some degree of error? Yes 4 common errors in data 1. Random errors, caused by unknown and unpredictable errors. The environmental conditions or the measuring instruments cause these errors. These can be minimized by increasing the sample size. 2. Systematic errors, error in the data or measuring instrument but it is a constant error. Like a measuring instrument not returning to zero. 3. Omission errors, when something has been left out or when an action has not been taken. 4. Outlier errors, observation points that are really far from other observations. You need to figure out if it is wrong or if it is actually correct and should be included in the data set. Skewness How much the data "leans" to one side. What is measurement bias? The bias that occurs from not selecting a true random sample. When the sample is not representative of the population, the sample test must be sufficiently random. What is Information bias? When the respondent or interviewer has an agenda and is not truly unbiased. What is response bias? When respondent says what they believe the questioner wants to hear What is conscious bias? When the surveyor is actively seeking a certain response. Maybe you only choose to ask women, or children a question. That is consciously choosing to be biased because of a preference. What is a triple blind study? When the data Analyzer, data gather and participant are not told What are Random errors? 1. Random errors, caused by unknown and unpredictable errors. The environmental conditions or measuring instruments cause these errors. These can be minimized by increasing the sample size. What are systematic errors? Systematic errors, error in the data or measuring instrument but it is a constant error. Like a measuring instrument not returning to zero. For example: a plastic tape measure gets stretched out over time. The error repeats itself. What are omission errors? Omission errors, when something has been left out, there is missing data, or when an action has not been taken. What are outlier errors? Outlier errors, observation points that are really far from other observations. You need to figure out if it is wrong or if it is actually correct and should be included in the data set. T-test* Is used to compare the average between two groups. Keyword comparison between 2 groups and AVERAGE. Or, to compare the average to a standard (1 sample T-test. Example: Pizza delivery times between car and bicycle. ANOVA*