Hypothesis Testing Definitions: A statistical hypothesis is a guess about a population parameter. The guess may or not be true. The null hypothesis, written H0, is a statistical hypothesis that states that there is no difference between a parameter and a specific value, or that there is no difference between two parameters. The alternative hypothesis, written H1 or HA, is a statistical hypothesis that specifies a specific difference between a parameter and a specific value, or that there is a difference between two parameters. Example 1: A medical researcher is interested in finding out whether a new medication will have undesirable side effects. She is particularly concerned with the pulse rate of patients who take the medication. The research question is, will the pulse rate increase, decrease, or remain the same after a patient takes the medication? Since the researcher knows that the mean pulse rate for the population under study is 82 beats per minute, the hypotheses for this study are: H0: µ = 82 HA: µ ≠ 82 The null hypothesis specifies that the mean will remain unchanged and the alternative hypothesis states that it will be different. This test is called a two-tailed test since the possible side effects could be to raise or lower the pulse rate. Notice that this is a non directional hypothesis. The rejection region lies in both tails. We divide the alpha in two and place half in each tail. Example 2: An entrepreneur invents an additive to increase the life of an automobile battery. If the mean lifetime of the automobile battery is 36 months, then his hypotheses are: H0: µ ≤ 36 HA: µ > 36 Here, the entrepreneur is only interested in increasing the lifetime of the batteries, so his alternative hypothesis is that the mean is greater than 36 months. The null hypothesis is that the mean is less than or equal to 36 months. This test is one-tailed since the interest is only in an increased lifetime. Notice that the direction of the inequality in the alternate hypothesis points to the right, same as the area of the curve that forms the rejection region. Example 3: A landlord who wants to lower heating bills in a large apartment complex is considering using a new type of insulation. If the current average of the monthly heating bills is $78, his hypotheses about heating costs with the new insulation are: H0: µ ≥ 78 HA: µ < 78 This test is also a one-tailed test since the landlord is interested only in lowering heating costs. Notice that the direction of the inequality in the alternate hypothesis points to the left, same as the area of the curve that forms the rejection region. Study Design: After stating the hypotheses, the researcher’s next step is to design the study. In designing the study, the researcher selects an appropriate statistical test, chooses a level of significance, and formulates a plan for conducting the study..

1. Hypothesis Testing
Definitions:
A statistical hypothesis is a guess about a population parameter.
The guess may or not be
true.
The null hypothesis, written H0, is a statistical hypothesis that
states that there is no
difference between a parameter and a specific value, or that
there is no difference between
two parameters.
The alternative hypothesis, written H1 or HA, is a statistical
hypothesis that specifies a
specific difference between a parameter and a specific value, or
that there is a difference
between two parameters.
Example 1:
A medical researcher is interested in finding out whether a new
medication will have
undesirable side effects. She is particularly concerned with the
pulse rate of patients who
take the medication. The research question is, will the pulse
rate increase, decrease, or
remain the same after a patient takes the medication?
Since the researcher knows that the mean pulse rate for the
population under study is 82
beats per minute, the hypotheses for this study are:

2. H0: µ = 82
HA: µ ≠ 82
The null hypothesis specifies that the mean will remain
unchanged and the alternative
hypothesis states that it will be different. This test is called a
two-tailed test since the
possible side effects could be to raise or lower the pulse rate.
Notice that this is a non
directional hypothesis. The rejection region lies in both tails.
We divide the alpha in two
and place half in each tail.
Example 2:
An entrepreneur invents an additive to increase the life of an
automobile battery. If the
mean lifetime of the automobile battery is 36 months, then his
hypotheses are:
H0: µ ≤ 36
HA: µ > 36
Here, the entrepreneur is only interested in increasing the
lifetime of the batteries, so his
alternative hypothesis is that the mean is greater than 36

3. months. The null hypothesis is
that the mean is less than or equal to 36 months. This test is
one-tailed since the interest
is only in an increased lifetime. Notice that the direction of the
inequality in the alternate
hypothesis points to the right, same as the area of the curve that
forms the rejection
region.
Example 3:
A landlord who wants to lower heating bills in a large apartment
complex is considering
using a new type of insulation. If the current average of the
monthly heating bills is $78,
his hypotheses about heating costs with the new insulation are:
H0: µ ≥ 78
HA: µ < 78
This test is also a one-tailed test since the landlord is interested
only in lowering heating
costs. Notice that the direction of the inequality in the alternate
hypothesis points to the
left, same as the area of the curve that forms the rejection
region.
Study Design:
After stating the hypotheses, the researcher’s next step is to

4. design the study. In designing
the study, the researcher selects an appropriate statistical test,
chooses a level of
significance, and formulates a plan for conducting the study. In
the first example, the
medical researcher will select a sample of sample of patients
who will be given the drug.
After the drug takes effect, the researcher will measure each
person’s pulse rate.
Remember that the sample means vary about the population
mean. Thus the mean of the
sample will in most cases, not be exactly equal to 82 (the mean
of the population). This
raises the question, if the mean of the sample is not exactly
equal to the population mean,
how do we know that the medication really does affect the pulse
rate? In other words, is
the difference due to chance or is it due to the effects of the
medication?
If the mean pulse rate of the sample is 83, the researcher would
probably not reject the null
hypothesis. However, if the mean pulse rate is 110, the
researcher would probably
conclude that there really is a difference. The question is,
where does the researcher draw
the line between chance variation and a real difference? The
difference is not made on
feelings or intuition, but is made statistically.
More Definitions:
A statistical test uses the data obtained from a sample to make a
decision about whether or
not the null hypothesis can be rejected.

5. The numerical value obtained from a statistical test is called the
test statistic or test value.
The critical value separates the critical region from the non-
critical region. The critical
region is the region of values that indicates that there is a
significant difference and that the
null hypothesis should be rejected.
A level of significance is the maximum probability of
committing a Type I error.
In a statistical test, the mean is computed for the data obtained
from the sample and is
compared with the population mean. Then, a decision is made
to reject or not reject the
null hypothesis on the basis of the value obtained from the
statistical test. If the difference
is significant, the null is rejected. If it is not significant, the
null is not rejected.
There are 4 possible outcomes:
H0 True H0 False
Reject H0 Type I error Correct decision
Accept H0 Correct decision Type II error
A Type I error occurs when the null hypothesis is rejected when
it is true.

6. A Type II error occurs when the null hypothesis is not rejected
when it is false.
A decision to reject or not reject the null does not really prove
anything. The only way to
prove anything statistically is to use the entire population . . . in
most cases, this is not
possible. Instead, we use probabilities to make a decision.
When there is a large enough
difference between the sample mean and the hypothesized mean,
the null is probably not
true. How large does this difference need to be?
First, we need to set a level of significance. In most cases, this
level will be either .05 or
.01. That means that if the null is rejected, the probability of a
Type I error will either be
5% or 1% and the probability of a correct decision is either 95%
or 99%.
The probability of a Type II error is symbolized by β (Greek
letter beta). The power of a
test is its sensitivity, or the probability that a statistical test will
be able to detect a true
difference. Power is equal to 1-β. Therefore, if β.= .05, power
= .95. Power is directly
related to both sample size and effect size; power increases and
sample size or effect size
increases.
After a significance level is chosen, a critical value is chosen
from a table for the appropriate
test. If a z-test is used, the z-table (Appendix D) is consulted to
find the critical value. The
critical value determines the critical (rejection) and
noncritical(acceptance) regions.

7. For a one-tailed test, the critical value can be on the right or
left side of the mean. Here,
the entire alpha is placed in the appropriate tail.
For a two tailed test, the critical region is split into two equal
parts and the alpha is split
between the tails.
Five step procedure for hypothesis testing:
1. State the null & alternative hypotheses
Tip: It’s often easier to state the alternative hypothesis first.
2. Design the study and select a level of significance (alpha)
Tip: Your alpha level should reflect your level of comfort with
either a Type I or Type II
error.

8. 3. Identify the test statistic
Tip: List all relevant info from your data first, then substitute
into the appropriate
formula. This can include n, s or σ, x-bar or p, µ or π. It’s also
helpful to draw the curve
and rejection area.
4. Formulate a decision rule
Tip: Use the Z-table to find the appropriate table value for your
chosen alpha level,
then decide under what conditions you’ll reject the null
hypothesis.
5. Take a sample and arrive at a decision to reject or not reject
the null.
Tip: Explain your decision in words rather than reporting
“reject” or “accept”. Non-
statisticians will thank you for it.

9. Discussion questions:
Your learning team has just been asked to perform some data
analysis for the same
entrepreneur who invented the battery additive. His newest
product line is a gas
additive that he thinks will significantly improve gas mileage in
older cars. He's
ready to take it to market with a splashy ad campaign but thinks
that it's a good
idea to back up his claims with statistics.
1. What type of sample data will you need?
2. How will you choose and collect your sample data?
3. Set up your null and alternate hypotheses.
4. Did you use a one or two tailed test? Why?
5. Select a level of significance. What level did you use? Why?
6. How likely is it that you'll experience a Type I error? How
can you reduce
this likelihood?
Business Decision Making Project Part 2 Grading Guide

10. QNT/275 Version 5
3
Business Decision Making Project Part 2 Grading Guide
QNT/275 Version 5
Statistics for Decision-Making
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Edited in accordance with University of Phoenix® editorial
standards and practices.
Individual Assignment: Business Decision Making Project Part
2Purpose of Assignment
The purpose of this assignment is to allow students to apply
descriptive and inferential statistics to business situations for
data-driven decision making. This assignment continues the
work students began in Week 3, using the same business
problem or opportunity. Grading Guide
Content
Met
Partially Met
Not Met
Comments:

11. Identify the types of descriptive statistics that might be best for
summarizing the data, if you were to collect a sample.
Apply the types of inferential statistics that might be best for
analyzing the data, if you were to collect a sample.
Analyze the role probability or trend analysis might play in
helping address the business problem.
Analyze the role that linear regression for trend analysis might
play in helping address the business problem.
Analyze the role that a time series might play in helping address
the business problem.
The paper is 1,050 words in length.

12. Total Available
Total Earned
7
#/7
Writing Guidelines
Met
Partially Met
Not Met
Comments:
The paper—including tables and graphs, headings, title page,
and reference page—is consistent with APA formatting
guidelines and meets course-level requirements.
Intellectual property is recognized with in-text citations and a
reference page.
Paragraph and sentence transitions are present, logical, and
maintain the flow throughout the paper.
Sentences are complete, clear, and concise.

13. Rules of grammar and usage are followed including spelling and
punctuation.
Total Available
Total Earned
3
#/3
Assignment Total
#
10
#/10
Additional comments:
Business Decision Making
Leo & Jessica Rodriguez
QNT/275
October 13th, 2016
Eric Godat
1

14. Business Decision Making
4
The Rush Truck Centers is a provider for the commercial
vehicle industry. We provide customers an integrated, one-stop
approach to the service and sales of new and used heavy- and
medium-duty trucks, aftermarket parts, service and collision
repair capabilities, and a range of financial services including
financing, insurance and leasing and rental options. We are
across the country and we have expanded rapidly.
Some of the responsibilities included to pick up and delivers
parts to customers, wholesale accounts, suppliers, and
coordinate any last minute pick-ups or deliveries. Assist with
pulling customer order(s) when requested; verify that invoice
matches purchase order for each pick-up. Check payments
received with the invoices for each delivery. Keep accurate log
of daily deliveries and pick-ups and help with stocking and
posting orders when they arrive to expedite delivery to shop
technicians and retail accounts. A lot of internal responsibilities
were taken in place and it was challenging when you have to be
out on the field at times.
One of the main issues we had at this company was the high
demand in the commercial vehicle industry the allocated time
for a driver to perform all his duties in a satisfactory manner.
The time frame or expectation is not sufficient, potentially
neglecting safety and loss to the company by not being able to
meet customer demand or request. 30% of the times we had to
drive customers to outsource other companies to purchase the
goods need to get the job done and meet our job
responsibilities. It was challenging and most of the time we had

15. to work 12 to 14 hour shifts 5 to 6 days a week. With working
long shifts, the focus was not the same and multitasking was
harder to be able to keep up with. Not to mention, having a
small staff did not make the job easier. Every day was a
different issue or request with the customers and we had to wear
multiple different hats and at all time have a positive and great
customer service.
One of the research variables we believe is affecting the
company is the time frame. We would want to know in detail
what is causing the delay on meeting the expectations or not
meeting the customer’s request. One potential solution could be
looking how much income we are investing in our employees on
the overtime rate and potentially hire more employees and be
able to meet deadlines. Hiring more employees could increase
the productivity level and we will have better quality work.
Customer service is a priority and is what increases our
business and sales. The customer service has not been the
greatest the last 3 years and we have lost sales because of the
lack of time frame. Complains from the customers have
increased and we have discovered this is also affecting our
sales. Larger competitors have promised customers better time
frames and a better rate.
One way we would like to compare the sales if either they have
increased or decreased is by pulling up a report for the last 5
years and from there determine the actual data if we are being
affected in a negative or positive manner. Another idea we have
is the number of accounts each employee is handling and
compare to the number of accounts getting handled the last 5
years. The profit we are making needs to be analyzed weekly
and by the end of the month determine any lost or increased
revenue. Communicating this information to the team members
is crucial and we need to make sure as a team we receive
support from everyone and we try to turn over any lost profit.
Having bi-weekly meetings and making sure this is getting
communicated in a positive matter will make the team members
work in a more productive attitude and will be better assisting

16. the customers and meet the expectations in a positive way. One
last strategy we had is also requesting feedback from the
customers. Knowing what we lack or don’t lack is important to
be communicated and we will better assist the customers if we
know what we need to work on as company. One way of
knowing this is having the customers fill out a survey and
display the answers in a data graph. By having a data graph we
will know exactly what we need to work on and if a pattern is
consistent we would be able to address the issue better.
Pulling reports and having the exact numbers physically will
help us determine if the data is valid. The actual income at the
end of the month will help us know where we stand quarterly
and yearly. We have monthly, quarterly and yearly goals and
with the collective data received we will be able to determine
where we stand from a financial point. Financially we need to
make sure we are on the positive side vs the negative and be
able to make decisions sooner than later to be able to avoid a
negative impact towards the company. If a negative impact is
taking in place towards our company, we want to make sure we
take the correct measures to fix this and be able to increase our
revenue and be able to meet our financial goals. Working as a
team and with every department would help us determine the
correct measures and be able to be proactive on any last minute
scenarios.
Actual Sales for 2013-2016
2013201420152016
2,345,123.00$ 2,896,234.00$ 2,456,789.00$ 1,760,674.00$