WGU Data Driven Decision Making - C207 Exam Questions
with Correct Answers, Graded A+
Activities (RBM stage) ✔✔ second step involves the process that converts inputs to
outputs (actions necessary to produces results -
...
WGU Data Driven Decision Making - C207 Exam Questions
with Correct Answers, Graded A+
Activities (RBM stage) ✔✔ second step involves the process that converts inputs to
outputs (actions necessary to produces results - training, evaluating, developing)
Alternative hypothesis ✔✔ The argument that either a sample is not equal to, greater
than, or less than the hypothesized null sample
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
Analytics ✔✔ The discovery, analysis, and communication of meaningful patterns in
data.
Autocorrelation ✔✔ A relationship between two variables that is inherently non-linear
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.
Bar chart ✔✔ A graph that measures the distribution of data over discrete groups or
categories.
Benchmarks ✔✔ Standards or points of reference for an industry or sector that can be
used for comparison and evaluation.
Big Data ✔✔ very large amounts of data; an all-encompassing term for any collection of
data sets so large and complex that it becomes difficult to process them using traditional
data processing applications
Blind Study ✔✔ A study performed where the participants are not told if they are in the
treatment group or control group
body mass index (BMI) ✔✔ A measure, based on a person's weight and height, that is
used to classify people as underweight or overweight.
Business process ✔✔ A sequence of logically related and time based work activities to
provide a specific output for a customer.
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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