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BUS 308 Week 2 Problem Set
BUS 308 Week 3 Problem Set (Anova)
BUS 308 Week 4 Problem Set (Regression and Correlation)
BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)
BUS 308 Week 1 DQ 1
BUS 308 Week 1 DQ 2
BUS 308 Week 2 DQ 1
BUS 308 Week 2 DQ 2
BUS 308 Week 3 DQ 1
BUS 308 Week 3 DQ 2
BUS 308 Week 4 DQ 1
BUS 308 Week 4 DQ 2
BUS 308 Week 5 DQ 1
BUS 308 Week 5 DQ 2
BUS 308 Week 1 Quiz (2 Set)
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BUS 308 Week 1 DQ 1
Part Two – Data Characteristics
Read Lecture One on descriptive data and review the Employee Data .
Be sure to familiarize yourself with the different variables shown on the
Data tab. In this course, we will be using the Employee Data and
statistical tools to answer a single research question: In our BUS308
company, are the males and females paid equally for equal work?
Lecture One discusses different ways data values can be classified. In
our data set for the equal pay for equal work assignment, students in the
past have correctly identify the variable gender (coded M and F for male
and female respectively) as nominal level data, but they often see
gender1 (coded 0 and 1 for male and female respectively) as interval or
ratio level data. Why? What could cause this wrong classification? What
data do you use in your personal or professional lives that might suffer
from not being correctly labeled/understood?
Part Three –Descriptive Statistics
Read Lecture Two on describing data sets and view The Role of Data &
Analytics Today video
(https://www.youtube.com/watch?v=fxroi4beKhE). Lecture Two

discusses several different ways of summarizing a data set--central
location, variability, etc. Often, business reports provide a mean or
average value for some measure (such as average number of defects per
production run). Why is the average alone not enough information to
make informed judgements about the result? What other descriptive
statistic should be included? Why? Can you illustrate this with an
example from your personal or professional lives? (This should be
started on Day 3.)
Part Four – Probability
Read Lecture Three on probability. Lecture Three introduces the idea of
probability—a measure of how likely it is to get a particular outcome.
Looking at outcomes as resulting from probabilities (somewhat random
outcomes/selections) rather than fixed constants often changes the way
we see things. How does considering the salary outcomes in our sample
the result of a probabilistic sample rather than a completely accurate and
precise reflection of the population change how we interpret the sample
statistic outcomes? What results in your personal or professional lives
could be viewed this way? What differences would this cause? Why?
Your responses should be separated in the initial post, addressing each
part individually,
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BUS 308 Week 1 DQ 2

DQ #2: Webliography
Post a question that you had related to the material this week. Conduct
research to provide the answer to the question and provide the source.
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BUS 308 Week 1 Quiz
Question 1. Calculating the median requires data of at least what level ?
Question 2. If sales data are reported in dollar values, what is the scale
of the data ?
Question 3. Empirical probability is
Question 4. A probability is found by dividing the number of possible
outcome (0) by the number of successes (e)P = o/e.

Question 5. Which of the following measure central tendency and
includes data from every score?
Question 6. A parameter refers to a sample characteristic.
Question 7. The mean is ?
Question 8. The probability of two independent events occurring
together equals the product of each of the individual event probabilities.
Question 9. Days of the week are considered what level of data ?
Question 10. Data on the ages of customers are ratio scale data.
BUS 308 Week 1 Quiz Set 2
Question 1. The probability of finding 100 defective products in a
sample of 500 is 25%.
Question 2. In statistical notation, M is to µ as s is to σ.
Question 3. Empirical probability is
Question 4. The standard deviation measures the central tendency of the
data set.
Question 5. The mean is?
Question 6. Data on the city from which members of a board of
directors come from represent interval level data?
Question 7. The mean is the most frequently used measure of central
location.
Question 8. Distances are considered an example of which data scale?
Question 9. The standard deviation of normally distributed data is equal
to about 1/6 of the data set’s
Question 10. Calculating the median requires data of at least what level?
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BUS 308 Week 2 DQ 1

DQ #1: Hypothesis Testing / T-tests / F-test
Although the initial post is due on Day 5, you are encouraged to start
working on it early, as it is a three-part discussion that should be
completed in sequential order.
Part One – Hypothesis Testing
Read Lecture Four. Lecture Four starts out with the five-step procedure
for hypothesis testing. What is this? What does it do for us? Why do we
need to follow these steps in making a judgement about the populations
our samples came from? What are the “tricky” parts of developing
appropriate hypotheses to test? What examples can you suggest where
this process might be appropriate in your personal or professional lives?
(This should be started on Day 1.)
Part Two – T-tests
Read Lecture Five. Lecture Five illustrates several t-tests on the data set.
What conclusions can you draw from these tests about our research
question on equal pay for equal work? What is missing from these
results to give us a complete answer to the question? Why? (This should
be started on Day 3.)
Part Three – F-test
Read Lecture Six. Lecture Six introduces you to the F-test for variance
equality. Last week, we discussed how adding a variation measure to
reports of means was a smart thing to do. Why does variation make our
analysis of the equal pay for equal work question more complicated?
What causes of variation impact salary that we have not discussed yet?
How can you relate this issue to measures used in your personal or
professional lives? (This should be completed by Day 5.)

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BUS 308 Week 2 DQ 2
research to provide the answer to the question and provide the source
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BUS 308 Week 2 Problem Set

Before starting this assignment, make sure the the assignment data from
the Employee Salary Data Set file is copied over to this Assignment file.
You can do this either by a copy and paste of all the columns or by
opening the data file, right clicking on the Data tab, selecting Move or
Copy, and copying the entire sheet to this file (Weekly Assignment
Sheet or whatever you are calling your master assignment file).
It is highly recommended that you copy the data columns (with labels)
and paste them to the right so that whatever you do will not disrupt the
original data values and relationships.
To Ensure full credit for each question, you need to show how you got
your results. For example, Question 1 asks for several data values. If you
obtain them using descriptive statistics, then the cells should have an
"=XX" formula in them, where XX is the column and row number
showing the value in the descriptive statistics table. If you choose to
generate each value using fxfunctions, then each function should be
located in the cell and the location of the data values should be shown.
So, Cell D31 - as an example - shoud contain something like "=T6" or
"=average(T2:T26)". Having only a numerical value will not earn full
credit. The reason for this is to allow instructors to provide feedback on
Excel tools if the answers are not correct - we need to see how the
results were obtained.
In starting the analysis on a research question, we focus on overall
descriptive statistics and seeing if differences exist. Probing into reasons
and mitigating factors is a follow-up activity.
1 The first step in analyzing data sets is to find some summary
descriptive statistics for key variables. Since the assignment problems

will focus mostly on the compa-ratios, we need to find the mean,
standard deviations, and range for our groups: Males, Females, and
Overall. Sorting the compa-ratios into male and females will require you
copy and paste the Compa-ratio and Gender1 columns, and then sort on
Gender1.
The values for age, performance rating, and service are provided for you
for future use, and - if desired - to test your approach to the compa-ratio
answers (see if you can replicate the values).
You can use either the Data Analysis Descriptive Statistics tool or the Fx
=average and =stdev functions. The range can be found using the
difference between the =max and =min functions with Fx functions or
from Descriptive Statistics.
Suggestion: Copy and paste the compa-ratio data to the right (Column T)
and gender data in column U. If you use Descriptive statistics, Place the
output table in row 1 of a column to the right. If you did not use
Descriptive Statistics, make sure your cells show the location of the data
(Example: =average(T2:T51)
A key issue in comparing data sets is to see if they are distributed/shaped
the same.
At this point we can do this by looking at the probabilities that males
and females are distributed in the same way for a grade levels.
2 Empirical Probability: What is the probability for a:
a. Randomly selected person being in grade E or above?
b. Randomly selected person being a male in grade E or above?
c. Randomly selected male being in grade E or above?

d. Why are the results different?
3 Normal Curve based probability: For each group (overall, females,
males), what are the values for each question below?:
Make sure your answer cells show the Excel function and cell
location of the data used.
A The probability of being in the top 1/3 of the compa-ratio
distribution.
Note, we can find the cutoff value for the top 1/3 using the fx
Large function: =large(range, value).
Value is the number that identifies the x-largest value. For the
top 1/3 value would be the value that starts the top 1/3 of the
range,
For the overall group, this would be the 50/3 or 17th (rounded),
for the gender groups, it would be the 25/3 = 8th (rounded) value.
i. How nany salaries are in the top 1/3 (rounded to nearest whole
number) for each group?
ii What Compa-ratio value starts the top 1/3 of the range for each group?
iii What is the z-score for this value?
iv. What is the normal curve probability of exceeding this score?
B How do you interpret the relationship between the data sets? What
does this suggest about our equal pay for equal work question?
4 Based on our sample data set, can the male and female compa-ratios in
the population be equal to each other?
A First, we need to determine if these two groups have equal variances,
in order to decide which t-test to use.
What is the data input ranged used for this question:

Step 1:
Ho:
Ha:
Step 2:
Decision Rule:
Step 3:
Statistical test:
Why?
Step 4: C
Conduct the test - place cell B77 in the output location box.
Step 5: Conclusion and Interpretation
What is the p-value:
Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?
What is your decision:
REJ or NOT reject the null?
What does this result say about our question of variance equality?
B Are male and female average compa-ratios equal?

(Regardless of the outcome of the above F-test, assume equal variances
for this test.)
Step 1:
Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test:
Why?
Step 4: Conduct the test - place cell B109 in the output location box.
What is the p-value: Is the P-value < 0.05 (for a one tail test) or 0.025
(for a two tail test)?
What does your decision on rejecting the null hypothesis mean?
If the null hypothesis was rejected, calculate the effect size value:
If the effect size was calculated, what doe the result mean in terms of
why the null hypothesis was rejected?

What does the result of this test tell us about our question on salary
equality?
5 Is the Female average compa-ratio equal to or less than the midpoint
value of 1.00?
This question is the same as:
Does the company, pay its females - on average - at or below the grade
midpoint (which is considered the market rate)?
Suggestion: Use the data column T to the right for your null hypothesis
value.
Step 1:
Ho:
Ha:
Step 3: Statistical test: Why?
Step 4: Conduct the test - place cell B162 in the output location box.
What is the p-value: Is the P-value < 0.05 (for a one tail test) or 0.025
(for a two tail test)?
What, besides the p-value, needs to be considered with a one tail test?

Decision: Reject or do not reject Ho?
What does your decision on rejecting the null hypothesis mean?
If the null hypothesis was rejected, calculate the effect size value:
If the effect size was calculated, what doe the result mean in terms of
why the null hypothesis was rejected?
What does the result of this test tell us about our question on salary
equality?
6 Considering both the salary information in the lectures and your
compa-ratio information, what conclusions can you reach about equal
pay for equal work?
Why - what statistical results support this conclusion?
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BUS 308 Week 2 Quiz
Question 1. To find the z-score of a value, which Excel function could
be used?
Question 2. Which statement is not true?
Question 3. What is the alternate hypothesis in a problem where sales
group two is predicted to be “…significantly less productive than sales
group one?”
Question 4. Using the T-test: Two-sample Assuming Equal Variances
test, the output can provide…
Question 5. To find the normal curve probability of exceeding a specific
z-score, which Excel function could be used?
Question 6. When interpreting the effect size, a high effect is shown by
a value equal to or greater than”.
Question 7. Using the Data Analysis Descriptive Statistics tool, the
output can provide the …
Question 8. Using alpha = .05, what is your decision if the p-value is
0.01 for a one-tail test?
Question 9. Which of the following defines statistical significance
Question 10. If the p-value is greater than (>) our decision criteria,
alpha, then we reject the null hypothesis claim of no difference.
Question 1. Which statement is not true?
Question 2. If the p-value is less than (<) our decision criteria, alpha,
then we reject the null hypothesis claim of no difference.
0.01 for a one-tail test?
Question 4. To use Excel to compare a single sample mean against a
specific value, a Two Sample with unequal variance t-test can be used if
the second “sample” has the same count and consists of only the specific
Ho value (resulting in no variation). This gives us a test outcome that is
the same as a one sample t-test result.

Question 5. What is the alternate hypothesis in a problem where sales
group two is predicted to be “…significantly less productive than sales
group one?”
Question 6. To find the normal curve probability of exceeding a specific
z-score, which Excel function could be used?
Question 7. Which statement is correctly stated?
0.01 for a two-tail test?
be used?
0.04 for a two-tail test?
Question 1. To use Excel to compare a single sample mean against a
specific value, a Two Sample with unequal variance t-test can be used if
the second “sample” has the same count and consists of only the specific
Ho value (resulting in no variation). This gives us a test outcome that is
the same as a one sample t-test result.
Question 2. The sign of =/= means “not equal”.
Question 3. We can use the F-test for variance” to decide if we should
use the equal or unequal variance version of the Two Sample T-test.
Question 4. The arrow head in the null hypothesis shows which tail the
result needs to be in to reject the null.
Question 5. If the p-value is less than (<) our decision criteria, alpha,
then we reject the null hypothesis claim of no difference.
Question 6. Using the Data Analysis Descriptive Statistics tool, the
output can provide the …
Question 7. Which of the following defines statistical significance
Question 8. In a one-tail test, which of the following statements is true?
Question 9. When interpreting the effect size, a high effect is shown by
a value equal to or greater than”.

be used?
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BUS 308 Week 3 DQ 1
Part One – Multiple Testing
Read Lecture Seven. The lectures from last week and Lecture Seven
discuss issues around using a single test versus multiple uses of the same
tests to answer questions about mean equality between groups. This
suggests that we need to master—or at least understand—a number of
statistical tests. Why can’t we just master a single statistical test—such
as the t-test—and use it in situations calling for mean equality decisions?
(This should be started on Day 1.)
Part Two – ANOVA
Read Lecture Eight. Lecture Eight provides an ANOVA test showing
that the mean salary for each job grade significantly differed. It then
shows a technique to allow us to determine which pair or pairs of means

actually differ. What other factors would you be interested in knowing if
means differed by grade level? Why? Can you provide an ANOVA table
showing these results? (Do not bother with which means differ.) How
does this help answer our research question of equal pay for equal work?
What kinds of results in your personal or professional lives could use the
ANOVA test? Why? (This should be started on Day 3.)
Part Three – Effect Size
Read Lecture Nine. Lecture Nine introduces you to Effect size measure.
There are two reasons we reject a null hypothesis. One is that the
interaction of the variables causes significant differences to occur – our
typical understanding of a rejected null hypothesis. The other is having
a large sample size – virtually any difference can be made to appear
significant if the sample is large enough. What is the Effect size
measure? How does it help us decide what caused us reject the null
hypothesis?
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BUS 308 Week 3 DQ 2

DQ #2: Webliography
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BUS 308 Week 3 Problem Set (Anova)
During this week, we will look at ways of testing multiple (more than
two) data samples at the same time.
We will continue to use the data and assignment file that we opened in
Week 2, we just move on to the Week 3 tab.
The first question asks us to determine if the average compa-ratio is
equal across 10K salary groups (20 – 29K. 30 – 39K, etc.). The second
question asks us to identify which of the salary groups have different
averages. The final question asks us to interpret the new information
presented in the lecture and assignment; how does the new information
we analyzed help us answer our equal pay for equal work question.

The data and assignment file can be found in the Course Materials link,
at the bottom in the Multi-Media section. If you save the files from last
week, you do not need to open them again.
Week 3 ANOVA Three Questions
Remember to show how you got your results in the appropriate cells.
For questions using functions, show the input range when asked.
1 One interesting question is are the average compa-ratios equal across
salary ranges of 10K each. While compa-ratios remove the impact of
grade on salaries, are they different for different pay levels, that is are
people at different levels paid differently relative to the midpoint? (Put
data values at right.)
Step 1:
Ho:
Ha:
Step 3: Statistical test:
Why?
Step 4: Conduct the test - place cell b16 in the output location box.
Step 5: Conclusions and Interpretation
What is the p-value?
Is P-value < 0.05?

What is your decision: REJ or NOT reject the null?
If the null hypothesis was rejected, what is the effect size value (eta
squared)?
If calculated, what does the effect size value tell us about why the null
hypothesis was rejected?
What does that decision mean in terms of our equal pay question?
2 If the null hypothesis in question 1 was rejected, which pairs of
means differ? Why?
Groups Compared Diff T +/- Term
Low to High Difference Significant? Why?
G1 G2
G1 G3
G1 G4
G1 G5
G1 G6
G2 G3
G2 G4
G2 G5
G2 G6
G3 G4
G3 G5
G3 G6
G4 G5
G4 G6

G5 G6
3 Since compa is already a measure of pay for equal work, do these
results impact your conclusion on equal pay for equal work? Why or
why not?
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BUS 308 Week 3 Quiz
Question 1. A single factor ANOVA output includes information on
Question 2. ANOVA tests for variance differences
Question 3. Excel’s single factor ANOVA does not have a related
Effect Size measure associated with it.
Question 4. The effect size measure for the single factor ANVOA is
called eta squared and equals the SS Between/SS Total.
Question 5. The Two Factor ANOVA with Replication primarily tests
for interactions between the variables.

Question 6. The null hypothesis for the Single Factor ANOVA states
that all means are equal.
Question 7. ANOVA’s SS within is an estimate of the average variance
of the data samples.
Question 8. The mean difference calculation involves using
Question 9. The single factor ANOVA tests for mean differences
between 3 or more groups by comparing
Question 10. ANOVA’s SS within is an estimate of the overall variance
in the data set.
Question 1. Question 1.1. ANOVA’s SS within is an estimate of the
overall variance in the data set.
Question 2. The null hypothesis for the Single Factor ANOVA states
that all means are equal.
Question 3. A single factor ANOVA output includes information on
Question 4. The alternate hypothesis for the single factor ANOVA
states that all means differ.
Question 5. A significance of F value equaling 3.5E-03 means
Question 6. The single factor ANOVA tests for mean differences
between 3 or more groups by comparing
Question 7. Excel’s single factor ANOVA output includes the effect
size measure.
Question 8. In calculating which means differ, each pair of means needs
a unique range.
Question 9. Setting up data entry for the single factor ANOVA in Excel
involves
Question 10. What is the best reason to perform an ANOVA test rather
than multiple t-tests?

Question 1. The single factor ANOVA mean difference calculation
involves
Question 2. Excel’s ANOVA output
Question 3. A significance of F value equaling 3.5E-03 means
Question 4. Excel’s options for performing an ANOVA include
for interactions between the variables.
Question 6. Excel’s single factor ANOVA does not have a related
Effect Size measure associated with it.
Question 7. ANOAV uses which statistical distribution to determine the
significance of the results?
Question 8. What is the best reason to perform an ANOVA test rather
than multiple t-tests?
Question 9. The alternate hypothesis for the single factor ANOVA
states that all means differ.
for mean
differences.
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BUS 308 Week 4 DQ 1

Part One – Correlation
Read Lecture Ten. Lecture Ten introduces the idea that different
variables may move together—sometimes due to causation and at other
times due to an unknown influence. An example involves the perfect
(+1.0) correlation between annual number of rum barrels imported into
the New England region of the U.S. between the years 1790 and 1820
and the number of churches built each of those years (citation lost).
Discuss this correlation: What does it tell us? Does rum drinking cause
church building? Does church building cause rum drinking? Or what
else could it tell us? If this correlation shows a cause and effect
relationship, what drives what? If not, why does it exist? What could this
correlation be used for? (This should be started on Day 1.)
Part Two – Linear Regression
Read Lecture Eleven. Lecture Eleven provides information showing a
strong positive correlation and a significant linear regression existed
between the individual’s salary and midpoint (used as a substitute for
grade). This is not an unexpected outcome in a company. How useful are
these in understanding what drives salary differences? Why? What
examples of a linear regression might be useful in your personal or
professional lives? Why? (This should be started on Day 3.)
Part Three – Multiple Regression
Read Lecture Twelve. In Lecture Twelve, a multiple-regression equation
was developed that showed the factors that influenced a person’s salary
and—almost as important—factors that did not influence salary. How do
we interpret a multiple-regression equation? Pick one of the factors—
whether statistically significant or not—used in the analysis, and

describe its impact on salary, what the coefficient is and what it means,
what its significance is, and whether you expected this outcome or not.
(This should be completed by Day 5.)
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BUS 308 Week 4 DQ 2
DQ #2: Webliography
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BUS 308 Week 4 Problem Set (Regression and
Correlation)

Problem Set Week Four
This week we get to answer our equal pay for equal work question by
looking at relationships between and among the different variables.
The first question this week looks at correlations and the creation of a
correlation table for our variables. The second question asks for a
regression equation showing how the different variables impact the
compa-ratio measure. The third questions asks you to discuss the
benefits of using a regression equation approach over the single variable
tests we have been doing.
The forth question asks for what other information you would have liked
to have analyzed in our research. The fifth question asks for your answer
to the equal pay for equal work question of: Is the company paying fairly
or not? If not, who benefits and why?
Regression and Corellation
Remember to show how you got your results in the appropriate cells.
For questions using functions, show the input range when asked.
1. Create a correlation table using Compa-ratio and the other interval
level variables, except for Salary.
Suggestion, place data in columns T - Y
a What range was placed in the Correlation input range box: Place C9 in
output box.
b What are the statistically significant correlations related to Compa-
ratio? T = Significant r =
c Are there any surprises - correlations you though would be significant
and are not, or non significant correlations you thought would be?

d Why does or does not this information help answer our equal pay
question?
2 Perform a regression analysis using compa as the dependent variable
and the variables used in Q1 along with including the dummy variables.
Show the result, and interpret your findings by answering the following
questions. Suggestion: Place the dummy variables values to the right of
column Y. What range was placed in the Regression input range box:
Note: be sure to include the appropriate hypothesis statements.
Regression hypotheses
Ho:
Ha:
Coefficient hyhpotheses (one to stand for all the separate variables)
Ho:
Ha:
Place B36 in output box.
Interpretation: For the Regression as a whole:
What is the value of the F statistic:
What is the p-value associated with this value:
Is the p-value < 0.05?
What does this decision mean?
For each of the coefficients: Midpoint Age Perf. Rat. Service Gender
Degree
What is the coefficient's p-value for each of the variables: Is the p-value
< 0.05?
Do you reject or not reject each null hypothesis:
What are the coefficients for the significant variables?
Using the intercept coefficient and only the significant variables, what is
the equation?
Compa-ratio =
Is gender a significant factor in compa-ratio?
Regardless of statistical significance, who gets paid more with all other
things being equal?
How do we know?

3 What does regression analysis show us about analyzing complex
measures?
4 Between the lecture results and your results, what else would you like
to know before answering our question on equal pay? Why?
5 Between the lecture results and your results, what is your answer to the
question of equal pay for equal work for males and females? Why?
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BUS 308 Week 4 Quiz
Question 1. The t Stat value is used to determine the statistical
significance of each of the variables listed in a regression analysis.
Question 2. A correlation of .90 and above is generally considered too
strong to be of any practical significance.
Question 3. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less than .05

Question 4. If two variables are known to be correlated, it is possible to
predict the value of y (dependent variable) from an x (independent)
variable.
Question 5. When determining statistical significance of correlations, (as
a rule of thumb), variable pairs with coefficients greater than (>) 70%
are generally not very valuable for prediction purposes.
Question 6. Which statement does not belong?
Question 7. Pearson Correlation Coefficient is a mathematical value that
shows the strength of the linear (straight line) relationship between two
variables.
Question 8. A regression analysis uses two distinct types of data. The
first are variables that are at least nominal level.
Question 9. The ANOVA table provides the Significance of F to use to
see if we reject or fail to reject the null hypothesis of no significance.
The Significance of F is also known as the P-value.
Question 10. When performing a regression analysis using the
Regression option in Data Analysis, the input for the Y range is the
independent variable (can generally control) and the input X range is for
the dependent variables.
Question 1. When determining statistical significance of correlations,
(as a rule of thumb), variable pairs with coefficients greater than (>)
70% are generally not very valuable for prediction purposes.
0.00000000000000000000000000000000000922 and is less than .05
Question 3. Pearson Correlation Coefficient is a mathematical value that
shows the strength of the linear (straight line) relationship between two
variables.
Question 4. A Pearson correlation of +1.00 is considered a “perfect
positive correlation”. This means….
Question 5. Spearman’s rank order correlation (rho) can be performed
on ordinal or any ranked data.

Question 7. Pearson’s Correlation requires at least interval level data.
variable.
Question 10. When looking at a regression statistics table, Multiple R
displays the percent of variation in common between the dependent and
all of the independent variables.
Question 1. Pearson’s Correlation requires at least interval level data.
0.00000000000000000000000000000000000922 and is less than .05
Question 3. When plotting variables on a scatter diagram, the variables
plotted on the Y-axis is the horizontal axis and the X-axis is the vertical
axis.
variable.
Question 5. When determining statistical significance of correlations,
(as a rule of thumb), variable pairs with coefficients greater than (>)
70% are generally not very valuable for prediction purposes.
Question 7. A Pearson correlation of +1.00 is considered a “perfect
positive correlation”. This means….
Question 8. When looking at a regression statistics table, Multiple R
displays the percent of variation in common between the dependent and
all of the independent variables.
Question 9. Which statement does not belong?

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BUS 308 Week 5 DQ 1
Part One – Confidence Intervals
Read Lecture Thirteen. Lecture Thirteen introduces you to confidence
intervals. What is a confidence interval, and why do some prefer them to
single point estimates? Ask your manager what is preferred and why?
What are the strengths and weaknesses of using confidence intervals in
making decisions? (This should be started on Day 1.)
Part Two – Chi Square
Read Lecture Fourteen. As Lecture Fourteen notes, the chi-square test
is—in some ways—fundamentally different than the previous tests we
have looked at. In what ways and why is this approach important?
Examples were shown of gender-degree distributions and employees per
grade. How do these tests help with understanding our equal pay for
equal work question? Do they change or reinforce our decision from last
week? What situations in your personal or professional lives could use a
chi-square approach?
Part Three – Overall Reactions

Has your opinion about statistics changed? How can statistical analysis
help your professional career?
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BUS 308 Week 5 DQ 2
What are common mistakes in linear regression analysis?
BUS 308 Week 5 Final Paper Statistics
Reflection (2 Papers)

This tutorial contains 2 Different Papers
The final paper provides you with an opportunity to integrate and reflect
on what you have learned during the class.
The question to address is: “What have you learned about statistics?” In
developing your responses, consider – at a minimum – and discuss the
application of each of the course elements in analyzing and making
decisions about data (counts and/or measurements).
The course elements include:
• Descriptive statistics
• Inferential statistics
• Hypothesis development and testing
• Selection of appropriate statistical tests
• Evaluating statistical results.
Writing the Final Paper
The Final Paper:
1. Must be three to- five double-spaced pages in length, and formatted
according to APA style as outlined in the Ashford Writing Center.
2. Must include a title page with the following:
a. Title of paper
b. Student’s name
c. Course name and number
d. Instructor’s name
e. Date submitted
3. Must begin with an introductory paragraph that has a succinct thesis
statement.
4. Must address the topic of the paper with critical thought.
5. Must end with a conclusion that reaffirms your thesis.
6. Must use at least three scholarly sources, in addition to the text.
7. Must document all sources in APA style, as outlined in the Ashford
Writing Center.

8. Must include a separate reference page, formatted according to APA
style as outlined in the Ashford Writing Center.
**************************************************
BUS 308 Week 5 Quiz
Question 1. Compared to the ANOVA test, Chi Square procedures
are not powerful (able to detect small differences).
Question 2. In confidence intervals, the width of the interval depends
only on the variation within the data set.
Question 3. The percent confidence interval is the range having the
percent probability of containing the actual population parameter.
Question 4. The Chi Square test can be performed on categorical
(nominal) level data.
Question 5. For a one sample confidence interval, the interval is
calculated around the estimated population or standard.

Question 6. The chi square test is very sensitive to small differences
in frequency distributions.
Question 7. The probability that the actual population mean will be
outside of a 98% confidence interval is
Question 8. A confidence interval is generally created when statistical
tests fail to reject the null hypothesis – that is, when results are not
statistically significant.
Question 9. A contingency table is a multiple row and multiple
column table showing counts in each cell.
Question 10. For a one sample confidence interval, if the interval
contains the population mean, the corresponding t-test will have a
statistically significant result – rejecting the null hypothesis.
Question 2. The Chi Square test for independence needs a known
(rather than calculated) expected frequency distribution.
Question 3. For a two-sample confidence interval, the interval shows
the difference between the means.
Question 4. Statistical significance in the Chi Square test means the
population distribution (expected) is not the source of the sample
(observed) data.
Question 6. The chi square test measures differences in frequency
counts rather than measures differences (such as done in the t and
ANOVA tests).
Question 7. The Chi Square test can be performed on categorical
(nominal) level data.
Question 8. The degrees of freedom for both forms of the Chi Square
test are calculated the same way.
Question 9. In confidence intervals, the width of the interval depends
only on the variation within the data set.

Question 10. Compared to the ANOVA test, Chi Square procedures
are not powerful (able to detect small differences).
Question 1. For a one sample confidence interval, if the interval
contains the population mean, the corresponding t-test will have a
statistically significant result – rejecting the null hypothesis.
Question 2. While rejecting the null hypothesis for the goodness of fit
test indicates that distributions differ, rejecting the null for the test of
independence means the variables interact.
calculated around the calculated sample mean.
Question 5. Having expected frequencies of 5 or less in a Chi Square
test can increase the likelihood of a type I error – wrongly rejecting the
null hypothesis.
Question 6. The degrees of freedom for the goodness of fit test equals
calculated around the estimated population or standard.
Question 8. The null hypothesis for the test of independence states
that no correlation exists between the variables.
Question 10. The chi square test measures differences in frequency
counts rather than measures differences (such as done in the t and
ANOVA tests).
**************************************************

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