MA2611, Applied Statistics I Term B, 2012 Lab Report 6 Dec. 7, 2012
Name:
Objectives
The purpose of this lab intends to explain the process and impacts of confidence and prediction interval techniques and procedures by learning through online tutorials, examples, and quizzes. Procedures

This lab was conducted in a controlled, computer environment with access to SAS software and applications. Instructions for the lab were provided in .pdf form and included procedures for accessing private applications in SAS. Additionally, tutorials and quizzes were administered using an applet available at http://www.wpi.edu/Academics/ATC/Collaboratory/LOs/Gagnon/PItutor/index.html and http://www.wpi.edu/Academics/ATC/Collaboratory/LOs/Gagnon/CIpQuiz/index.html. Prediction Intervals

A tutorial was given to help explain the process and purpose of prediction intervals. Following the tutorial, a quiz was taken. The results from the quiz are shown in Figure 1.

Figure 1: Results from online quiz #1.
The purpose of a prediction interval is to tell you where you can expect the next data point to be. SAS computes the prediction interval using equation 5.11, shown below. Ynew-σYnew-Ynewtn-1,1+L2,Ynew+σ(Ynew-Ynew)tn-1,1+L2,

The assumptions of this formula are that the {∈i} are independent N(0,σ2) random variables. Following the instructions from the lab report, SAS was used to build a data containing the measurements of the speed of light. Parameters for the application are C = 0.90. The results of this application are shown in Figure 2.

Figure 2: Prediction Interval using SAS with application SASDATA.SOL. C = 0.90 The width of the prediction interval is 2.9996 - 2.9972 = .0024. The width of the confidence interval is 2.9985 – 2.9983 = .0012. The width of the prediction interval is exactly twice as large (wide) as the confidence interval. If the confidence interval is increased to C = 0.95 (95%), the result is an increase in...

...Statics and Uncertainty
Marilyn Esthappan
Lab Partner: Nisha Sunny
TA: Sajjad Tahir
Physics Lab 106
May 29, 2011
1-3pm
THEORY:
Statistical variation and measurement uncertainty are unavoidable. A theory is consistent if the measurement is 2m +/- 1m. Uncertainty rises from statistical variation, measurement precision, or systematic error.
EXPERIMENTAL OVERVIEW:
Part 1. Coin Toss: Statistical Variation:
Sixteen coins were tossed nine times and the number of heads was counted to determine variation associated with random events.
Part 2. Measurement Precision:
The measurements (length, width, and height) of the wood block were taken with a ruler to estimate the preciseness of using a ruler. The uncertainty of each measurement was measured because this is how much how much the preciseness of the ruler is off. The mass of the block was taken using a scale. All measurements have an uncertainty that effects its calculations.
DATA:
Part 1. Coin Toss: Statistical Variation:
Number of Times for Heads | The Range of Heads Between 6 and 10 | Average for Heads | 7 +/- 8 |
6 | Y | Sigma | 1 +/- 5 |
5 | N | Average per Penny with 16 Pennies | 0 +/- 5 |
7 | Y | Sigma per Penny | 0 +/- 1 |
7 | Y | Percentage Uncertainty | 20% |
8 | Y | Sum of Number of Heads for 144 Pennies | 70 |
9 | Y | Average per Penny for the Whole 144 Set of Coins | 0 +/- 51 |
9 | Y | Sigma per Penny Using Sigma of 6 | 0 +/- .4 |
10 | Y |...

...of 1000 flights and proportions of three routes in the sample. He divides them into different sub-groups such as satisfaction, refreshments and departure time and then selects proportionally to highlight specific subgroup within the population. The reasons why Mr Kwok used this sampling method are that the cost per observation in the survey may be reduced and it also enables to increase the accuracy at a given cost.
TABLE 1: Data Summaries of Three Routes
Route 1
Route 2
Route 3
Normal(88.532,5.07943)
Normal(97.1033,5.04488)
Normal(107.15,5.15367)
Summary Statistics
Mean
88.532
Std Dev
5.0794269
Std Err Mean
0.2271589
Upper 95% Mean
88.978306
Lower 95% Mean
88.085694
N
500
Sum
44266
Summary Statistics
Mean
97.103333
Std Dev
5.0448811
Std Err Mean
0.2912663
Upper 95% Mean
97.676525
Lower 95% Mean
96.530142
N
300
Sum
29131
Summary Statistics
Mean
107.15
Std Dev
5.1536687
Std Err Mean
0.3644194
Upper 95% Mean
107.86862
Lower 95% Mean
106.43138
N
200
Sum
21430
From the table above, the total number of passengers for route 1 is 44,266, route 2 is 29,131 and route 3 is 21,430 and the total numbers of passengers for 3 routes are 94,827.
Although route 1 has the highest number of passengers and flights but it has the lowest means of passengers among the 3 routes. From...

...Table of Contents
Abstract……………………………………………………………………………………………2
Introduction………………………………………………………………………………………..2
Background………………………………………………………………………………..2
Objectives…………………………………………………………………………………2
Scope………………………………………………………………………………………3
Theory review……………………………………………………………………………………..3
Design of report…………………………………………………………………………………...5
Procedures…………………………………………………………………………………………5
Results……………………………………………………………………………………………..6
Discussion…………………………………………………………………………………………6
Conclusion………………………………………………………………………………………...7
Reference……………………………………………………………………………………….....7
Appendix…………………………………………………………………………………………..7
ABSTRACT
This experiment introduces the use of dimensionless analysis and conventionally analytical method to survey the performance of centrifugal pump. The end of this experiment points out the benefit of using the “new” method to the conventional in most practical problem, especially in the survey of turbo-machine. Also, through this experiment, students know some basic indexes to assess the efficiency of pumps used. We will that for the specific fan conducting this experiment, the best efficiency point occurs at CQ = 0.2, the specific speed NS ~1.23.
INTRODUCTION
Background
A fan is a turbo-machine in which work is done to increase the total pressure of the fluid leaving the device. This is achieved by a rotor or impeller, which is driven by an external source of power to move a row of blades so as to...

...progressing. The company provided a data sample from the past 12 months with 200 entries, each with 6 variables. The aim of this report is to evaluate the success of CCResorts in fulfilling their key performance indicators as outlined in their business plan, determines the clientele that are attracted to CCResorts and analyses the effect of different variables on the expected expenditure of the customers. The statistical analysis yielded several significant conclusions discussed in terms of their implications for CCResorts. The sample meets with key performance indicator 1 with over 40% of guests staying the full week. There is sufficient evidence to suggest that over 40% of the total population also stay 7 days at CCResorts. On average, majority of customers do not spend more than $255 per day at the resort. Despite this, there are certain demographics that are more likely to achieve a higher expenditure per day. Firstly, the age of the guest impacted their daily expenditure with customers who were older tending to spend slightly more than their younger counterparts. Furthermore, guests who stayed in large groups had a greater likelihood of fulfilling the second key performance indicator. Customers with an income over $100 000 p.a were more inclined to spend more money in excess of accommodation costs.
introduction
The central focus of this statistical report is to determine the success of CCResorts in achieving their key performance...

...Daphny Maldonado
Bio Lab 2107
Kiah Britton
W 10-12:30
Is H20 Bad for You?
Abstract:
In the village of Gopher Hollow there’s a cluster of Blue Baby Syndrome. There were
four infants affected by this cluster. The families from the infants would collect their
water from wells. We have to determine what’s the source of the high levels of nitrites in
the water. The four sources that could be the point of contamination are a new
subdivision, textile plant, an organic farm, and a mountain lake. We had to ﬁnd the
concentration of each known standard and unknown standard. We did this by using a
spectrophotometer. The results were the following, the organic farm with a herd of 50
cows and a 10 acre ﬁeld of zucchini had the highest levels of nitrites.
Introduction:
Blue Baby Syndrome is a condition that affects many infants. This condition makes
the baby’s skin turn blue because of the lack of oxygen. This condition can exhibit
lethargy, vomiting and not being able to breathe. It can even lead to death in rare cases.
This condition is caused by the excess amount of nitrate that is then converted into
nitrite by the digestive system. The hemoglobin then reacts with the nitrites to form
Methemoglobin. Methemoglobin is not a problem in adults since they have an enzyme
that converts methemoglobin back to hemoglobin. Infants don’t have many of the
enzyme to convert methemoglobin to hemoglobin, resulting in Blue Baby Syndrome. For
example in Gopher...

...1. Introduction
This report is about the case study of PAR, INC. From the following book: Statistics for Business an Economics, 8th edition by D.R. Anderson, D.J. Sweeney and Th.A. Williams, publisher: Dave Shaut. The case is described at page 416, chapter 10.
2. Problem statement
Par, Inc. has produced a new type of golf ball. The company wants to know if this new type of golf ball is comparable to the old ones. Therefore they did a test, which consists out of 40 trials with the current and 40 trials with the new golf balls. The testing was performed with a mechanical fitting machine so that any difference between the mean distances for the two models could be attributed to a difference in the design. The outcomes are given in the table of appendix 1.
3. Hypothesis testing
The first thing to do is to formulate and present the rationale for a hypothesis test that Par, Inc. could use to compare the driving distance of the current and new golf balls. By formulation of these hypothesis there is assumed that the new and current golf balls show no significant difference to each other. The hypothesis and alternative hypothesis are formulated as follow:
Question 1
H0 : µ1 - µ2 = 0 (they are the same)
Ha : µ1 - µ2 ≠ 0 (the are not the same)
4. P-value
Secondly; analyze the data to provide the hypothesis testing conclusion. The p-value for the test is:
Question 2
Note: the statistical data is provide in § 5.
-one...

...STATISTICS - Lab #6
Statistical Concepts:
Data Simulation
Discrete Probability Distribution
Confidence Intervals
Calculations for a set of variables
Open the class survey results that were entered into the MINITAB worksheet.
We want to calculate the mean for the 10 rolls of the die for each student in the class. Label the column next to die10 in the Worksheet with the word mean. Pull up Calc > Row Statistics and select the radio-button corresponding to Mean. For Input variables: enter all 10 rows of the die data. Go to the Store result in: and select the mean column. Click OK and the mean for each observation will show up in the Worksheet.
We also want to calculate the median for the 10 rolls of the die. Label the next column in the Worksheet with the word median. Repeat the above steps but select the radio-button that corresponds to Median and in the Store results in: text area, place the median column.
Calculating Descriptive Statistics
Calculate descriptive statistics for the mean and median columns that where created above. Pull up Stat > Basic Statistics > Display Descriptive Statistics and set Variables: to mean and median. The output will show up in your Session Window. Print this information.
Calculating Confidence Intervals for one Variable
Open the class survey results that were entered into the MINITAB worksheet.
We are...

...
Banana Oil LabReport
Jesse Bradford
7/10/14
MTWR Section
Introduction
In the banana oil lab we began with isopentyl alcohol + acetic acid isopentyl acetate + Water. We needed for this experiment a hot plate, clamps, pipette, 5mL vial, caps, hoses and a thermometer. Upon starting, our group set up an open system experiment that allowed gases to be released to avoid pressure build up. We mixed together to molecules, 1.0mL of isopentyl alcohol, 1.5mL of acetic acid and added three drops of sulphuric acid. The acetic acid was used as a catalyst to speed up the reaction. Once all the needed chemicals were added we waited for about 70-75mintues for the reaction to take place. The desired temperature for the reaction was 150oC. We also had the solution at a constant stir.
After the reaction was done taking place, we began to purification process. We used a pipette to remove the excess water and impurities that were underneath the banana oil. We removed all that was available and then began to add sodium carbonate to help wash and dry the mixture. Slowly shaking the banana oil inside the 5mL side to side, allowing CO2 to escape the 5mL vial. We did this twice making sure all the excess impurities were removed. As we had our final solution of banana oil, we used the I.R. spectra to conclude our results. The I.R. spectra showed us that the compound we produced had no peak at 3300cm-1. The...