The project is your independent research and data analysis based on either
primary (you collect your own) data or secondary (use some existing database,
from the internet or elsewhere) data. You are to design the study, collect the data
(or use some existing data), analyze the data, draw appropriate conclusions, and
document your study in a carefully written report. Reports or papers are preferred
over power-point presentations, posters or other forms of presentation. See below
for some guidelines on the organization of the final report. One possible plan (but,
not necessarily the only one) Start with some hypothesis, conjecture or claim. The
claim might be your own or someone else's (for example: a manufacturer that
claims their product lasts longer than that of a competitor). Figure out the best
way to test this claim. The first step might be to figure out the correct response to
measure (ordinal, nominal, interval, ratio, discrete, continuous). Decide on a plan
of attack, which might include doing an observational study or a designed
experiment. The data to put your claim to a test might be available somewhere
(on the Internet?) or you may have to collect it yourself. If you collect the data
yourself, decide on an appropriate method of collection (random sample, cluster
sample, etc.) and an appropriate sample size. Justify the method you used and the
sample size selected. Determine the appropriate analysis to test the claim. Use of
descriptive statistics and graphs may be part of the project but will graphs and
numbers alone will rarely be adequate. Use of statistical inference (confidence
intervals, hypothesis testing, etc.) is highly recommended. Carefully complete the
analysis. Draw an appropriate conclusion (if possible) and carefully document
what you did and your conclusions. It may also be appropriate to comment on
lessons learned and potential improvements if the study was to be repeated (what
you might do differently). A project outline or 1 page description of your intended
project is due by Midterm. The project outline is NOT required and will not be
graded. However, it is highly recommended that you submit one. The outline will
be reviewed and returned to you with suggestions and comments for improvement.
Therefore, your outline should be as specific as possible. Instead of submitting a
proposal, you may discuss your project ideas in person with me during office
hours (or by appointment) Projects will be ranked using the criteria shown below.
The instructor reserves the right to grant extra credit for a truly exceptional project
(rarely done) and to give a failing grade to extremely poor projects. Projects
should be done independently. Projects involving collaboration between two or
more people will NOT be accepted this semester.
Criteria for a "good" Project
• Clear statement of the problem (what is the claim? What are you trying to
demonstrate?)
• Relevance of the problem to real life (why should anyone care?)
• Uniqueness/Originality
• Clear statement of conclusions (what's the bottom line? what's the decision?)
• Justification for: o Sample size o Primary of secondary research/experimental
design vs. study of existing data. o Analysis tools used (what did you compute
and why did you choose those "statistics") o Sampling method (random, cluster,
systematic, etc.) - why and how?
• Use of Graphics (if appropriate)
• Use of Computer Aids (like MINITAB), if appropriate
• Accuracy of calculations (No numerical mistakes)
• Neatness
• Organization of Final Report.
There are no requirements on the length of the project write-up. Make it as short
as possible to communicate the important steps in your analysis. Consider writing
for an audience that is statistically literate but not a statistical expert. In other
words, consider the audience for your report to be an average college student or
graduate. Communicate your ideas as clearly as possible. Think Quality, NOT
Quantity. Do not include any irrelevant information. Focus on the main problem.
Do not include histograms, statistics, etc. that are not relevant to your main thesis.
Computer output, equations and detail math calculations rarely adds anything to
a report. If these are included, they should be in an Appendix at the end of the
report.
• Statistical Inference. The emphasis of this course is on statistical inference or
inductive logic. The closer your project comes to using these ideas, the higher it
will be rated. Tables and graphs are nice but try to use the ideas of estimation,
confidence intervals, hypothesis testing (Chapters 6, 7, 8, 9) if you want your
project to rate high. For some projects, the problem you choose may require you
to go beyond the material covered in class in order to provide an appropriate
analysis. If this is the case and you are successful, your project will rank higher.
In past semesters, research reports consisting of critiques of journal articles and
historical aspects of statistics and/or statisticians were allowed as projects in this
course. This type of project will NOT be accepted this semester.
Dedication of Yast Comed to instrumentalists
Introduction
Those who study must know that it is a very competitive field that requires a great deal of time
to be went practicing for performances. That being said, I thought it would be interesting to look
at which group of music majors is more dedicated vocalists or instrumentalists. Based on my
experience as a musie student hypothesized that instrumentalists are more dedicated musicians
than vocalists, and conducted a series of tests to prove this claim true, using hours spent
practicing per week as the measurement of dedication. Below I have highlighted the results that
found, and detailed the exact steps that I took along the way.
Practical Significance or Release
Knowing what musicians are the most dedicated could be important for those who are casting for
a show or hiring for a performance; these individuals want to know that that the musician they
hire will be dedicated to preparing for their performance. This knowledge could also be applied
to salaries of musicians, with more dedicated musicians receiving a higher pay. Aspiring music
students may also find this information useful because it could show them how much they should
expect to practice in order to be successful as a vocalist or instrumentalist
Procedure:
1. Gathering Data - The very first step was to collect the necessary data to be analyzed. I
decided to collect my own data because there was no reliable raw data available that
would serve the purpose that I wished it to. In order to gather the data, I put together a
survey that asked three questions:
a. 1) Are you a vocalist or an instrumentalist?
b. 2) How many days per week do you practice?
c. 3) How many hours do you spend per practice session?
From this I would be able to compute how many hours on average each individual spent
practicing each week. This survey was distributed to thirty-five
music students, twelve of which were vocalists and twenty-five that were
instrumentalists. A survey was used because it was simple to gather the data from my
college campus this way, and an experiment was not required to collect this data. The
sample size of thirty-five was used because there are not many music students on the
River Campus; I gathered just enough data to determine that the sample was normal,
representative, and could be successfully analyzed without having to expand my survey
to include 1
vhich would have compromised the
sample and we study).
2. Numerical Summary and Analysis* - I computed the averages for the individual
survey results and from there calculated summary statistics, which I then compiled and
summarized for later analysis using box-and-whisker plots and comparative tables (which
can be found in the "Reference Tables and Statistics" section of this report). Through this
analysis I determined that the mean number of hours that vocalists practice every week is
2.83 with a standard deviation of 2.42 hours, while for instrumentalists the mean hours of
practice per week is 7.52 with a standard deviation of 6.42 hours. Right away this gives
us a clue that instrumentalists practice more than vocalists, because the difference in
mean practice times is (-)4.69 hours. After computing a 95% confidence interval on this
difference, I can say with 95% confidence that the difference between these means would
lie between (-)1.72 and (-)7.66, which means that this difference of (-)4.69 is most likely
accurate and deserves to be looked at more closely. The box-and-whisker plots that I
have created show that the data is approximately normal, with no outliers or extreme
skewness that could affect analysis, they also show that there is more variability amongst
instrumentalists than there is amongst vocalists. From looking at the spread of the data in
the box-and-whisker plots displayed below, it is clear that my hypothesis has merit and
deserves further testing and analysis.
Vocalists
Series 1
Series 2
1
Series3
Series4
0
2
4
6
00
Series5
LO
12
14
16
18
20
Instrumentalists
Series1
Series2
Series3
Series4
Series5
0
2
4
6
8
10
12
14
16
18
20
*Note: All calculations were made on a TI-84 Plus graphing calculator
fo
wady could be important for viewing
en la promance would why
up with others in the field and ple whose
med voice parte putere
dal parts spend the montering
www tepplied in a real
worem yere they
so you could use this to see how then you should see
you are looking to be calls for a show and
you
non, you may wish to pay them more to come for the end
Reference Table and
RawData
Numerical martes und aatiles
10
25
4
64
25
IGA
175
242
75
3
Test News
225
1
321
015
0.0015
ttestati
2
O
35
D-value
2625
17.5
17.5
4
7.5
17.5
5.25
15
3
1
7.5
Summary Vocalist
Minimum
91
0.25
Median
03
425
Maximum
14.5
175
Hypothese Test and State Significance - The final step in determining whether
instruments are indeed more dedicated the vocalists was to conduct a hypothesis
test. In this case I have used are samples for the difference in two means to
determine if there is a statistically significant difference in the data concocted the
pode estas follows using a T-54 Plus graphing calor
SallAlepothesis: I have signed the sample of vocalists to
wl and the instrumentalisto 2. Given this, my mull and strative hypotheses
to testare and Hard
Sip 2 Yeng Conditions and Coming Test Statistic: This is not a large
samples, but it is approximately normal and random, so once these criteria
were verified as I went ahead and computed a test statisti-3.21
Step Finding the valu have lected to use a level of significance of .05
because that is the generally accepted value. That being said the p-value that I
have calculated for this situation is pm.0015
d. Selising the P.Value to Deemine Slanic and Form Conclusion: The
p-value that I have computed is much smaller than the level of significance a
which means that it is statistically significant. Given this, it may be reasonable to
reject the null hypothesis that the two means are equal in favor of the alternative
hypothesis (that)
Step 5 Reportin Content Based on the results of this hypothesis test, we can
conclude that the mean time spent practicing by instrumentalists is indeed greater
than the mean time spent practicing by vocalists in this sample.
Summa and Conclusion:
The purpose of this study was to see if instrumentalists are more dedicated musicians than
vocalists. I set out to do this through determining if there was a statistically significant difference
between the lengths of time each group of musicians spends practicing each week on average. In
other words, I was trying to prove that the mean practice time for instrumentalists is greater than
the mean practice time for vocalists, and that this difference is unlikely to be present in the data
due to random chance. I gathered a representative sample of data through a survey sent out to
music students at the
generated numerical and visual summaries for
these two sets of data. Then I found the difference between the means and did a 95% confidence
test on that difference to be certain that it was worth further testing. Based on my initial analyses
I concluded that the next step should be to conduct a hypothesis test, so I conducted a two
sample t-test for the difference in two means to determine if the difference I was seeing in the
data was indeed statistically significant. The results of this study showed that the difference in
the two means is statistically significant, and therefore it may be reasonable to conclude that
instrumentalists are indeed the more dedicated musicians compared to vocalists, the average
amount of time that they spend practicing per week is significantly greater than the time spent
practicing by vocalists.
Purchase answer to see full
attachment