WGU C207 Data-Driven Decision Making (Latest 2023\/ 2024 Update) Complete Guide with Terms and Verified Definitions- 100% Correct<\/p>\n\n\n\n
WGU C207 Data-Driven Decision Making
(Latest 2023\/ 2024 Update) Complete Guide
with Terms and Verified Definitions- 100%
Correct
T: Histograms
Answer:
Measures how continuous data is distributed over various ranges. A graph that displays continues
data.
T: Bar Graph
Answer:
Measures data that is distributed over groups or categories.
T: Proportion
Answer:
A type of ratio where the number of observations that are part of a specific group is compared to
the total relevant population.
T: Probability Distribution
Answer:
A list of all of the different probabilities of each outcome that can occur. This is often displayed
as a graph, table, or formula<\/p>\n\n\n\n
T: Probability functions
Answer:
Functions that assign probabilities to values of a random variable, determine the information in a
probability distribution. Needs to have the sum of the probability values equal to one and also
needs each probability value to be greater than zero and less than one.
T: P(X) = { P(2-9) = 0.615
P(10-K) = 0.308
P(A) = 0.077 }
Answer:
A probability distribution is displayed as a function it shows the probability of each outcome.
(This example is drawing a card from a suit
T: Probability density functions are used for\u2026.
Answer:
Continuous random variables. A probability distribution is continuous if there is any number of
outcomes in a range. Like temperature.
T: Probability mass functions are used for\u2026.
Answer:
Discrete random variables. A probability distribution is discrete if there are a defined number of
outcomes. Like working computers or being pregnant; you are or you are not.
T: Cumulative distribution
Answer:<\/p>\n\n\n\n
represents the probability that a variable falls within a certain range. Specifically, the cumulative
distribution of x measures the probability that a variable is less than or equal to x. Cumulative
distributions, as they are progressing through the data should approach 1.0 or 100% of its data.
T: Central Limit Theorem
Answer:
The idea that if a great enough number of samples is taken, the means of those samples will be
normally distributed around the population mean. As more samples are taken, the sample mean
will approach the population mean.
T: Confidence intervals
Answer:
The range around a sample mean that has a specific probability of containing the true population
mean. The “confidence” is the likelihood that a new sample will look like past findings, while the
“interval” is the varying range around the existing mean that allows for the different levels of
confidence.
T: Standard error of the mean
Answer:
Used to give an estimate of the proximity of the sample mean to the population mean.
T: null hypothesis, or H0
Answer:
The statement that there is no relationship. For whatever relationship is being tested, the null
hypothesis is the statement that the relationship does not exist. The null hypothesis is always the
statement that is being tested.<\/p>\n\n\n\n
T: alternative hypothesis, or HA,
Answer:
It is the opposite statement to the null hypothesis. It states that there is a relationship for
whatever relationship is being tested.
T: Statistically significant
Answer:
A result is unlikely to be caused by random variation or errors.
T: Significant difference
Answer:
A difference that is statistically significant
T: The null hypothesis statement can be written as:
Answer:
H0: \u03bc1 = \u03bc2
T: The alternative hypothesis can can be written as:
Answer:
HA: \u03bc1 \u2260 \u03bc2
T: Significance level
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