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"WGU Data-Driven Decision Making (C207): Key Concepts, Techniques, and Tools"
1. Activities (RBM stage): second step involves the process that converts inputs to outputs (actions necessary to produces results - training, evaluating,
developing)
2. Alternative hypothesis: The argument that either a sample is not equal to, greater than, or less than the hypothesized null sample
3. Analysis of Variance (ANOVA): a technique used to determine if there is a sufficient evidence from sample data of three or more populations to
conclude that the means of the population are not all equal
4. Analytics: The discovery, analysis, and communication of meaningful patterns in data.
5. Autocorrelation: A relationship between two variables that is inherently non-linear
6. Balanced Scorecard: An approach using multiple measures to evaluate performance, including financial measures, and the non-financial measures of
customers, internal business processes, and learning and growth.
7. Bar chart: A graph that measures the distribution of data over discrete groups or categories.
8. Benchmarks: Standards or points of reference for an industry or sector that can be used for comparison and evaluation.
9. Big Data: very large amounts of data; an all-encompassing term for any collection of data sets so large and complex that it becomes diflcult to process them
using traditional data processing applications
10. Blind Study: A study performed where the participants are not told if they are in the treatment group or control group
11. body mass index (BMI): A measure, based on a person's weight and height, that is used to classify people as underweight or overweight.
12. Business process: A sequence of logically related and time based work activities to provide a specific output for a customer.
13. Central Limit Theorem: A theorem that states that, the greater the sample, the closer the mean of the sample is to the entire population and the
more the results will look like a normal distribution
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14. Cluster Analysis: The process of arranging terms or values based on ditterent variables into "natural" groups
15. Cointegration: Occurs when two time series are moving with a common pattern due to a connection between the two time series
16. Combination: The number of ditterent unordered possibilities for a certain situation.
17. Complement: The occurrence of an event not happening, the opposite
18. Confidence interval: An interval estimate used to indicate reliability
19. Continuous Data: Data that can lay along any point in a range of data
20. Control chart: A graphic display of process data over time and against established control limits, and that has a centerline that assists in detecting a
trend of plotted values toward either control limit.
21. Control limits: The area composed of three standard deviations on either side of the centerline, or mean, of a normal distribution of data plotted on a
control chart that reflects the expected variation in the data
22. Criterion-reference test: compare an individual to certain defined standards
23. Critical Success Factors: The important things an entity must do to be successful, such as quality measures, customer service, or efficiency.
24. Cumulative Average-Time Learning Model: A learning curve model in which the cumulative average time per unit declines by a
constant percentage each time the cumulative quantity of units produced is doubled
25. Cumulative distributions: The probability that a random variable will be found at a value less than or equal to a given number
26. Customer satisfaction: A measure of the extent to which customers are satisfied with the products and related services they received from a supplier.
27. Cycle time: The total elapsed time to move a unit of work from the beginning to the end of a physical process, as defined by the producer and the customer.
28. Cyclicality: Repetition of up (peaks) or down movements (troughs) that follow or counteract a business cycle that can last several years
29. Data Management: The management, including cleaning and storage, of collected data.
30. Data Mining: the process of discovering patterns in large data sets; performed on big data to decipher patterns from these large databases
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47. Incremental Unit-Time Learning Model: A learning curve model in which the incremental unit time (the time needed to produce the
last unit) declines by a constant percentage each time the cumulative quantity of units produced is doubled
48. Independent Variable: The variable presumed to influence another variable (dependent variable);
typically it is the level of activity or cost driver
49. Information 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
50. Input (RBM stage): the first step of RBM is to define the resources, human or financial, used by the RBM system (people, funds, information)
51. Interquartile range: The difference, in value, between the bottom and top 25 percent of the sample or population
52. Interval Data: Data that is ordered within a range and with each data point being an equal interval apart
53. Irregularity: One-time deviations from expectations caused by unforeseen circumstances such as war, natural disasters, poor weather, labor strikes, single-
occurrence company- specific surprises or macroeconomic shocks
54. Item Response Theory (IRT): model of designing, analyzing and scoring tests
55. Key Performance Indicator (KPI): A performance measurement that organizations use to quantify their level of success.
56. Laspeyres Index: a comparison of the same quantity of goods with the same weight over a period of time
57. Line graph: A graph that illustrates relationships between two changing variables with a line or curve that connects a series of successive data points
58. Lower limit control: The minimum value on a control chart that a process should not exceed
59. Mean: An average, calculated by adding a series of elements in a data set together and dividing by the total number in the series
60. Measurement Bias: A prejudice in the data that results when the sample is not representative of the population being tested
61. Median: The value or quantity lying at the midpoint of a frequency distribution
62. Multicollinearity: A multiple regression equation is flawed because two variables thought to be indepen- dent are actually correlated to be
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63. Multiple Linear Regression: A statistical method used to model the relationship between one depen- dent (or response) variable and two or
more independent (or explanatory) variables by fitting a linear equation to observed data
64. Multiplication Principle: When the probabilities of multiple events are multiplied together to determine
the likelihood of all of those events occurring
65. Mutually exclusive events: When two or more events are not able to occur at the same time
66. Net Promoter Score: A management tool designed to collect data indicating the relative loyalty of customers and their willingness to
recommend a company's products or services.
67. Nominal Data: Sometimes called categorical data or qualitative data, this data type is used to label subjects or data by name
68. Non parametric test: A test that does not assume there to be a structure (may be a normal distribution) to the population.
69. Norm-referenced test: compare an individual to other individuals
70. Normal distribution: data tending to occur around a central value with no bias right or left
71. Null hypothesis: The argument that there is no difference between two samples or that a sample has not changed over time
72. Omission Error: An error because something (for example, data or survey response) is missing.
73. Operating Income: Earnings before Interest and Taxes.
74. Ordinal Data: Data that places data objects into an order according to some quality with higher order indicating more of that quality
75. Outcome (RBM stage): the short-term effect that the outputs will have (greater efficiency, more viability, better decision making, social action, or
changed public opinion)
76. Outlier: An observation point that is significantly distant from the other observations in the dataset
77. Output (RBM stage): third step when the outputs have been created by the RBM activities (goods and services, publications, systems,
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93. Range: The difference between the minimum and maximum value in a given measurable set
94. Rate: measure of an event occurring over a period of time
95. Ratio: measures one quantity in relation to another quantity
96. 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.
97. Regression Analysis: A statistical analysis tool that quantifies the relationship between a dependent variable and one or more independent
variables
98. Relational Database: A database structured to recognize relations among stored items of information.
99. Reliable Data: Data that is consistent and repeatable
100. Results-base Management (RBM): a management strategy that uses results as the central mea- surement of performance
101. Return on Investment (ROI): The ratio of income earned on the investment to the investment made to earn that income.
102. Run chart: A line chart that shows performance measurements over time; run charts help to uncover trends or aberrations in processes
103. Sampling with replacement: When a piece of the population can be selected more than once
104. Sampling without replacement: When a piece of the population cannot be selected more than once
105. Scatter diagram: A graphic that uses dots to show relationships or correlations between variables
106. Significance level: A number that is used as the cutoff for how statistically meaningful a probability, equal to or more extreme than what was
observed, is
107. Simple Composite Index: created when a researcher gathers data from many ditterent sources without weighing any data more
significantly than any other data
108. Simple Index Number: shows the change in price or quantity of a single good or service over time
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109. Simple Linear Regression: A form of regression analysis with only one independent variable
110. Specification limits: The area, on either side of the centerline, or mean, of data plotted on a control chart that meets the customer's requirements for
a product or service. This area may be greater than or less than the area defined by the control limits
111. Standard deviation: The square root of the variance, a measure of how spread out the numbers are
112. Standard Error (SE) of Estimate: The "average" deviation of the data points from the regression line or curve
113. Standard score: Also Z-scores, measure the distance from a piece of data from the mean compared to the entire population; method to compare two
data sets together with ditterent scales.
114. Statistics: The science that deals with the interpretation of numerical facts or data through theories of probability. Also, the numerical facts or data
themselves.
115. Systematic Errors: Errors in measurement that are constant within a data set, sometimes caused by faulty equipment or bias
116. test statistic: One value used to test the hypothesis, it is a numerical summary of the data set
117. The Result Chain: 1) Resources - inputs and activities 2) Results - outputs then outcomes then impact
118. Time Series Analysis: Regression analysis that uses time as the independent variable
119. Trend: In data analysis, a general slope upward or downward over a long period of time
120. Trial: An experiment, a test of the performance or qualities of something or someone
121. 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
122. True Score Model: average score an individual would achieve if he or she were to take the test infinite times; observed score is the true score
plus random error
123. Upper control limit: The maximum value on a control chart that a process should not exceed
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137. conscious bias: occurs when the surveyor is actively seeking a certain response to support his or her theory or cause
138. Bayes' Theorem: A formula that calculates conditional probabilities, important in understanding how new information affects the probabilities of ditterent
outcomes.
139. conditional probability: the probability of an event occurring given that another event has occurred
140. Chi-square test: any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null
hypothesis is true
141. Plan Do Check Act Cycle: A four-step method that practitioners use to create plans to solve a problem (Plan), run an experiment to see if the plan
will work (Do), check the experiment results (Check), and implement changes to processes or policies (Act)
142. SIPOC diagram: A diagram that defines the boundaries of a process and shows how its Suppliers, Inputs,
Processes, Outputs, and Customers affect process quality.
143. Ishikawa - 7 Basic Tools of Quality: 1) Run Chart
2) Check sheet
3) Cause and effect diagram (fishbone diagram)
4) Histogram
5) Flow Chart
6) Scatter Diagram
7) Pareto chart
144. Six Sigma: A highly disciplined, data-driven approach that uses statistical analysis to measure and improve a company's operational performance by
identifying and eliminating defects in manufacturing and service processes; the term itself is commonly defined as 3.4 defects per million opportunities.
145. lean operations: Popularized by Six Sigma, business practices that use as little time, inventory, supplies
11 / and work as possible to create a dependable product or service. The less that is used, the less waste occurs, and the more money the business saves. Accuracy is also very important in POS (Point of Sale) systems, and the most accurate systems produce products and services without flaws, so nothing needs to be thrown away.
146. International Organization for Standardization (ISO): Established a certification pro-
gram that guarantees that an organization is dedicated to quality concepts and is continually working to ensure that it is producing highest level of quality possible. The certification shows that an organization has a quality management system in place to monitor and control quality issues and is continuing to meet the needs of customers and stakeholders with high-quality products and services.
