C207 Concepts and Chapter Quizzes Western Governors University
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Course
WGUC207 (WGUC207)
Institution
Abacus College, Oxford
C207 Concepts and Chapter Quizzes Western Governors University/C207 Concepts and Chapter Quizzes Western Governors University/C207 Concepts and Chapter Quizzes Western Governors University
C207 Concepts and Chapter Quizzes
1. Analytics
Analytics refers to the systematic computational analysis of data or statistics. It is used by companies to make informed
decisions based on data analysis, trends, and statistical models. In the context of developing a complete portrait of
customers, analytics helps in understanding customer behaviors, preferences, and patterns to attract and retain the best
customers.
2. Prescriptive Analytics
Prescriptive analytics is the area of business analytics dedicated to finding the best course of action for a given situation.
It is particularly useful for manufacturers looking to maximize output while minimizing costs, such as labor, because it not
only predicts outcomes but also suggests decision options to achieve these goals.
3. Systematic Error
A systematic error is a type of error that is not determined by chance but is consistent and reproducible. It often results
from a problem that persists throughout the entire experiment, such as faulty equipment or bias, causing data to be
skewed in a particular direction consistently.
4. Measurement Bias
Measurement bias occurs when the data collected for analysis does not accurately reflect the variable being measured
due to some systemic error. This could be a result of the method of collection, the instrument used for measuring, or the
sample not being representative of the population. For instance, using a test developed in one state for a national
assessment without considering regional educational differences might introduce measurement bias.
5. Omission
An omission error in data collection or analysis occurs when certain data points are not included or are missed. This can
happen for various reasons, such as surveying only one language, which might not capture data from non-English
speaking respondents accurately.
6. Can be analyzed with traditional spreadsheets
This statement is not true of big data. Big data is characterized by its volume, velocity, and variety that traditional
databases and spreadsheets cannot efficiently process. It often requires more complex and advanced analytical tools and
methods to process and analyze the data effectively.
7. Determine the scope of the problem and Review of previous findings
Determining the scope of the problem involves defining what the problem is and what it isn't, and setting boundaries for
the analysis. Reviewing previous findings involves looking at past research or data related to the problem to build on
existing knowledge. Both steps are crucial in the "framing the problem" stage of the Davenport-Kim three-stage model
for better understanding and tackling the problem efficiently.
8. Treatment procedures and Experimental response
In any experimental study, treatment procedures refer to the actions or interventions applied to study participants or
subjects to observe their effect. The experimental response is the outcome or reaction measured after applying the
treatment procedures. Together, they help researchers understand the effect of the treatment on the subjects.
9. Ratio - The distance covered in a marathon (26.2 miles) is an example of a ratio level of measurement. Ratio data have
a true zero point and can be compared in terms of how many times larger one value is than another. This level of
measurement allows for the broadest range of mathematical calculations and comparisons.
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,10. Nominal
The type of cars produced in a Ford factory is categorized on a nominal scale. Nominal data classify data into distinct
categories wherein the order or rank of the data is not significant. The focus is on naming or categorizing data based on
attributes or qualities.
11. Ordinal
The "10 best cities in the U.S. to retire in" represents an ordinal level of measurement. Ordinal data is characterized by
the ability to rank the items in order of magnitude or preference, but the differences between ranks are not necessarily
equal or quantifiable. In this case, cities can be ranked from best to least best based on certain criteria for retirement,
but the difference in quality of life between each rank is not precisely measured.
12. Ordinal
Women’s dress sizes (0, 2, 4, 6, etc.) are an example of ordinal measurement. Ordinal data reflects a ranking or order of
items where the distances between ranked positions are not known. Dress sizes indicate a progression in size but do not
quantify the exact difference in measurements between each size.
13. Double-blind study
A double-blind study is a research method used to eliminate bias in studies by ensuring that neither the participants nor
the researchers know who is receiving a particular treatment. This method is especially useful in eliminating bias
introduced by participants' or researchers' expectations in a study, such as examining the impact of testing changes on
math scores.
14. The experimental unit
In an experiment, the experimental unit refers to the smallest unit to which a treatment is applied. In the context of
testing car waxes, the five cars waxed with each type of wax represent the experimental units. The response, or outcome
measured, is the number of washes it takes before the wax begins to deteriorate on these units.
15. Association and causation
The misuse of assuming that a relationship between two variables implies that one causes the other is known as
confusing association with causation. In the case of the online retailer, attributing the increase in sales solely to the
weekly newspaper ad without considering other factors like seasonal buying habits represents this error.
16. Not a representative sample
Using data from a single grade or school to recommend curriculum changes for a broader educational system risks the
error of not having a representative sample. This statistical misuse occurs when the sample does not accurately reflect
the population it's supposed to represent, potentially leading to biased or ungeneralizable conclusions.
17. Median
The median is the best measure to represent the middle value of a data set, especially when the data includes outliers or
is not normally distributed. In the case of household income with two large incomes in the sample, the median provides
a more accurate center of the data distribution than the mean, which could be skewed by these high values.
18. Multiplication Rule
The multiplication rule in probability is used to find the likelihood of two independent events happening at the same
time. For a cable company's customers to have both cable television and internet, the probability of having cable TV and
the probability of having internet would be multiplied if the two events are independent of each other.
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, 19. Bayes Theorem
Bayes Theorem is used to find the probability of an event occurring based on prior knowledge of conditions that might
be related to the event. In the context of finding the likelihood of a customer defaulting on payments given at least two
late payments, the Bayes Theorem allows for updating the probability of default based on the additional information
about payment history.
20. Z-score
A Z-score measures how many standard deviations an element is from the mean of its data set. For assessing the
probability of a customer's test finding bags with the strength of 54.2 or less given the mean and standard deviation, a Z-
score calculation would indicate how unusual or likely that test result is.
21. 4.3 to 7.3
This range represents a 95.4% probability of customer waiting times for teller service, based on the mean and standard
deviation given. It is derived using the concept of standard deviations from the mean in a normal distribution, where
approximately 95.4% of data lies within two standard deviations of the mean in both directions.
22. Variance
Variance measures how far a set of numbers is spread out from their average value. In the context of NBA team
revenues, variance would quantify the degree of dispersion or variability in revenues from the league's mean, indicating
how evenly or unevenly revenue is distributed among teams.
23. Histogram
A histogram is a graphical representation of the distribution of numerical data, showing the frequency of data within
certain ranges or bins. It is particularly useful for analyzing the distribution of time taken for payment receipts, as it can
visually represent the spread and concentration of payment timelines.
24. 32
The median of a data set is the middle value when the numbers are arranged in order. For the given data set: 15, 30, 30,
34, 41, and 60, the median is the average of the two middle numbers (30 and 34), which is 32. This is a direct calculation
without needing further explanation as the numbers are already sorted.
25. The test statistic is greater than the critical value, and The null hypothesis is rejected given the probability of being
wrong.
When using a t-test, if the null hypothesis is rejected, it means the test found a statistically significant difference between
groups. Rejecting the null hypothesis typically involves a test statistic that exceeds a critical value, indicating the
observed data is unlikely under the null hypothesis. This scenario suggests there's a significant difference, and it's done
with an understanding of the probability (alpha level) of being incorrect in rejecting the null hypothesis.
26. Z-score
When an individual data point has a z-score of +1, it means that the data point is one standard deviation greater than the
mean of the data set. The z-score is a measure of how many standard deviations an element is from the mean, allowing
for the comparison of data points across different scales or distributions.
27. Scatterplot - A scatterplot is a type of graph used in statistics to display values for typically two variables for a set of
data. If you're examining the relationship between two variables like car mileage and resale price, a scatterplot can
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