A template report file can be found in the course shell: mnmprojectreport.doc. Before your write your report, watch the video titled “mnmunwrapped.wmv” located in the course shell. It is a 3:30-minute video segment from the TV show “Unwrapped,” showing many parts of the production process, which might give you some ideas. Ignore the color percentages quoted in the segment.

Imagine you are a quality control manager at the Masterfoods plant. Write a two to three (2–3) page report on all the parts of the project. Structure your paper using the following headers: oIntroduction: Purpose of Report

oProject Part 1: Sampling Method
oProject Part 2: Method, Analysis, Results
oProject Part 3: Method, Analysis, Results
oProject Part 4: Method, Analysis, Results
oProject Part 5: Method, Analysis, Results
oQuality Control: Assume that at least one of the tests from Part 4 was rejected (proportion not equal to targeted amount set by Masterfoods). Discuss how you would investigate the operations of the plant to determine why the proportions were off the targeted values. Speculate on three or more possible conditions in plant and bagging process that could have caused the observed results. oConclusion

You should explain what was done as well as the results. Tables can be used to present results and information. Your audience is a supervisor or manager who is unfamiliar with this project and may or may not be familiar with statistical terms. As a result, you will either need to explain/define statistical terms or write them in a way that a layman can understand.

You will be graded on the following criteria:
1.Present the methods, analysis, and results for the five parts of the project. See above Project Parts 1 through 5. 2.Explain how to investigate unexpected results (failed test(s) in Part 4) and speculate on at least three plausible causes for the observed results. 3.Clarity in explaining all...

...STAT 600 Statistics and Quantitative Analysis
PROJECT: Stock return estimation
The project must be done by 6-15 a.m. October, 16th. You should submit your projects before the class begins. This is a group project. Read the course outline for general guidelines. Good luck!
The project is closely related to Lectures 1-5 of the class.
Today is September 15, 2013 and you have just started your new job with a financial planning firm. In addition to studying for all your license exams, you have been asked to review a portion of a client’s stock portfolio to determine the risk/return profiles of 12 stocks in the portfolio. Unfortunately, your small firm cannot afford the expensive databases that would provide all this information with a few simple keystrokes, but that’s why they hired you. Specifically, you have been asked to determine the monthly average returns and standard deviations for the 12 stocks for the past five years.
The stocks (with their symbols in parentheses) are:
Apple Computer (AAPL) Hershey (HSY)
Archer Daniels Midland (ADM) Motorola (MOT)
Boeing (BA) Procter and Gamble (PG)
Citigroup (C) Sirius XM radio (SIRI)
Caterpilar (CAT) Wal-Mart (WMT)
Deere&Co. (DE)...

...
MBA 501A – [STATISTICS]
ASSIGNMENT 4
INSTRUCTIONS: You are to work independently on this assignment. The total number of points possible is 50. Please note that point allocation varies per question. Use the Help feature in MINITAB 16 to read descriptions for the data sets so that you can make meaningful comments.
[10 pts] 1. Use the data set OPENHOUSE.MTW in the Student14 folder. Perform the Chi
Square test for independence to determine whether style of home and location are are related. Use α = 0.05. Explain your results.
Pearson Chi-Square = 37.159, DF = 3, P-Value = 0.000
Likelihood Ratio Chi-Square = 40.039, DF = 3, P-Value = 0.000
The P value associated with out chi square is 0.00 and the Alpha level is 0.05 so we reject the null hypothesis. The P- value is less than the alpha level. So, we conclude that style of homes and locations are not related.
[10 pts] 2. Use the data set TEMCO.MTW in the Student14 folder. Perform the Chi
Square test for independence to determine whether department and gender are related. Use α = 0.05. Explain your results.
Pearson Chi-Square = 1.005, DF = 3, P-Value = 0.800
Likelihood Ratio Chi-Square = 1.012, DF = 3, P-Value = 0.798
The P-value associated with out chi square is 0.800 and the Alpha level is 0.05 we can see that we are unable to reject the null hypothesis. The P- value is greater than the alpha level. So, we conclude that departments and gender are related..
[30 pts] 3. Use the data set...

...criterion we get the best model: y = -124.382 + 0.296X1 + 0.048X2 + 1.306X3 + 0.5198X4. This model contains all four predictor variables X1, X2, X3 and X4. This model is selected as best model by the MaxR criterion because it has the largest R-Square 0.9629, which is larger than 0.9615(model containing 3 variables), 0.9330(model containing 2 variables) and 0.8047(model containing 1 variable).
Below is a SAS output of the MaxR criterion.
Obviously, the “best” model obtained from MaxR criterion differs from that obtained from Stepwise and Backward Elimination Method. It is not hard to understand this phenomenon: Since for the Stepwise/Backward Elimination method, F-statistic plays an important role in selecting a variable: the F-statistic for a variable to be added must be significant at the SLENTRY level, the F-statistic for a variable to be removed must be significant at the SLSTAY level. While the MaxR method selects variables depending on which variable or variable combination can produce the largest R square. MaxR makes the switch that produces the largest increase in R square.
Appendix |
Code:
data job;
infile "C:\Users\sandra\Desktop\CH09PR10.txt";
input y x1 x2 x3 x4;
run;
proc reg data=job;
model y=x1 x2 x3 x4/selection=stepwise slstay=.10 slentry=.05;
title "Stepwise Selection";
run;
proc reg data=job;
model y=x1 x2 x3 x4/selection=adjrsq;
run;
proc reg data=job;
model y=x1 x2 x3...

...Lecture Notes on Introductory Statistics, I
(P.P. Leung)
Lecture notes are based on the following textbook:
N.A. Weiss (2012), Introductory Statistics, 9th edition, Pearson.
Chapter 1 The Nature of Statistics 統計本質
§1.1 Two kinds of Statistics
§1.4 Other Sampling Designs (其他抽樣方法)
Chapter 1 The Nature of Statistics 統計本質
What is Statistics? 何謂統計?
From Wikipedia, the free encyclopaedia:
Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the natural and social sciences to the humanities. Statistics is also used for making informed decisions in government and business.
Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both descriptive and inferential statistics comprise applied statistics. There is also a discipline called mathematical statistics, which is concerned with the theoretical basis of the subject....

...INTRODUCTION
A. Importance of Statistics
Statistical methods have been applied to problems ranging from business to medicine to agriculture. A review of the professional literature in almost any field will substantiate the extent of statistical analysis.
Accounting: Public accounting firms use statistical sampling procedures when conducting audits for their clients.
Economics: Economists use statistical information in making forecasts about the future of the economy or some aspect of it.
Marketing: Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications.
Finance: Financial managers have routine contact with information in numerical form. Financial forecasts, break-even analyses, and investment decisions under uncertainty are but part of their activities.
Production: A variety of statistical quality control charts are used to monitor the output of a production process.
Statistics
the collection, organization, presentation, analysis, or interpretation of numerical data, especially as a branch of mathematics in which deductions are made on the assumption that the relationship between a sufficient sample of numerical data are characteristic of those between all such data.
it is a science which deals with the collection, organization, presentation, analysis, and interpretation of data.
B. Fields of Statistics
Descriptive...

...
Statistical Analysis
BU 510 601
2 Credit Hours
Fall 2013
Instructor: Shrikant Panwalkar Office phone: (410) 234 9456
Office Hours: By appointment panwalkar@jhu.edu
Required Text and Learning Materials
Business Statistics in Practice; 6th Edition, McGraw-Hill Higher Education,
ISBN-13 978-0-07-340183-6 (There are other ISBN numbers)
Authors: Bowerman, Bruce; O'Connell, Richard. (the cover shows a third author – Murphree)
Please note: 7th edition is available, however, we will NOT be using the 7th edition – please purchase the 6th edition
Additional learning material may be posted from time to time
Blackboard Site
A Blackboard course site is set up for this course. Each student is expected to check the site throughout the semester as Blackboard will be the primary venue for outside classroom communications between the instructors and the students. Students can access the course site at https://blackboard.jhu.edu. Support for Blackboard is available at 1-866-669-6138.
Course Evaluation
As a research and learning community, the Carey Business School is committed to continuous improvement. The faculty strongly encourages students to provide complete and honest feedback for this course. Please take this activity seriously because we depend on your feedback to help us improve so you and your colleagues will benefit. Information on how to complete the evaluation will be provided towards the end of the course....

...typically have? You take a random sample of 51 reduced-fat cookies and test them in a lab, finding a mean fat content of 4.2 grams. You calculate a 95% confidence interval and find that the margin of error is ±0.8 grams. A) You are 95% confident that the mean fat in reduced fat cookies is between 3.4 and 5 grams of fat. B) We are 95% confident that the mean fat in all cookies is between 3.4 and 5 grams. C) We are 95% sure that the average amount of fat in the cookies in this study was between 3.4 and 5 grams. D) 95% of reduced fat cookies have between 3.4 and 5 grams of fat. E) 95% of the cookies in the sample had between 3.4 and 5 grams of fat. Determine the margin of error in estimating the population parameter. 12) How tall is your average statistics classmate? To determine this, you measure the height of a random sample of 15 of your 100 fellow students, finding a 95% confidence interval for the mean height of 67.25 to 69.75 inches. A) 1.5 inches B) 0.25 inches C) 1.06 inches D) 1.25 inches E) Not enough information is given. 12) 11) 10)
3
Construct the indicated confidence interval for the difference between the two population means. Assume that the assumptions and conditions for inference have been met. 13) The table below gives information concerning the gasoline mileage for random samples of trucks of two different types. Find a 95% confidence interval for the difference in the means m X - m Y. Brand X Brand Y 50 50 20.1 24.3 2.3 1.8 13)...

...1. A radio station that plays classical music has a “By Request” program each Saturday night. The percentage of requests for composers on a particular night are listed below:
Composers Percentage of Requests
Bach 5
Beethoven 26
Brahms 9
Dvorak 2
Mendelssohn 3
Mozart 21
Schubert 12
Schumann 7
Tchaikovsky 14
Wagner 1
a. Does the data listed above comprise a valid probability distribution? Explain.
The individual probabilities are all between 0 & 1 and the sum = 100%
b. What is the probability that a randomly selected request is for one of the three B’s?
P(one of the B’s) = P(Bach) + P(Beethoven) + P(Brahms) = 5 + 26 + 9 = 40%
c. What is the probability that a randomly selected request is for a Mozart piece?
P(Mozart) = 21%
d. What is the probability that a randomly selected request is not for one of the two S’s?
P(not Schubert or Schumann) = 1 – P(Schubert or Schumann)
= 1 – (12 + 7)
= 81%
e. Neither Bach nor Wagner wrote any symphonies. What is the probability that a randomly selected request is for a composer who wrote at least one symphony?
P(Symphony) = 1 – P(Bach or Wagner)
= 1 – (5 + 1)
= 94%
f. What is the probability that a randomly selected request is for a composer other...