Online Failure Forecast For Fault Tolerant Data Stream...

Online Failure Forecast For Fault Tolerant Data Stream...
Chapter 5 Online Failure Forecast for Fault–Tolerant Data Stream Processing 5.1 Introduction 5.1.1
Historical background of Failure management in Data Stream Processing In the recent times Data
Stream Management Systems (DSMSs) have been developed to support critical applications that
must quickly and continuously process data as soon as it becomes available. A few of the example
applications include financial stream analysis and network intrusion detection. Fault tolerance and
high availability are the most important for these applications because faults can lead to losses that
are quantifiable. Therefore a DSMS must be equipped with fault tolerance techniques to handle both
node and network failures in order to support such applications. All of the basic techniques that help
to cope with failures involve some kind of replication. Typically the state of a system's computation
is replicated onto independently failing nodes. The system should then coordinate with the replicas
for accurate recovery from failures. Fault–tolerant techniques are most usually designed to tolerate
up to a pre–defined number, say k, of simultaneous failures. If such methods are used, the system is
then said to be k–fault tolerant. Replication and coordination have 2 general approaches. Both
approaches rely on the assumption that computation can be modeled as a deterministic state
machine. The implication of this assumption is that two non–faulty computations that receive the
same input in the
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Essay on Math 533 Final Exm
MATH 533 Final Exm
Click Link Below To Buy: http://hwcampus.com/shop/math–533–final–exm/ Or Visit
www.hwcampus.com
1. (TCO A)Consider the following sample data on the age of the 30 employees that were laid off
recently from DVC Inc.
21 38 20 26 37 52 37 24 45 20
50 49 44 30 29 42 56 46 60 30
32 25 47 55 38 25 20 29 32 30 a. Compute the mean, median, mode, and standard deviation, Q1,
Q3, Min, and Max for the above sample data on age of employees being laid off.
b. In the context of this situation, interpret ... Show more content on Helpwriting.net ...
b. Find the probability that the monthly customer expenditure is between $300 and $600 for a
randomly selected customer.
c. The management of a supermarket wants to adopt a new promotional policy giving a free gift to
every customer who spends more than a certain amount per month at this supermarket. Management
plans to give free gifts to the top 8% of its customers (in terms of their expenditures). How much
must a customer spend in a month to qualify for the free gift? (Points : 18)
5. (TCO C) A tool manufacturing company wants to estimate the mean number of bolts produced
per hour by a specific machine. A simple random sample of 9 hours of performance by this machine
is selected and the number of bolts produced each hour is noted. This leads to the following results.
Sample Size = 9
Sample Mean = 62.3 bolts/hr
Sample Standard Deviation = 6.3 bolts/hr
a. Compute the 90% confidence interval for the average number bolts produced per hour.
b. Interpret this interval.
c. How many hours of performance by this machine should be selected in order to be 90% confident
of being within 1 bolt/hr of the population mean number of bolts per hour by this specific machine?
(Points : 18)
6. (TCO C) A clock company is concerned about errors in assembly in their custom made clocks. A
simple random sample of 120 clocks yields nine clocks with errors in assembly. a. Compute the 99%
confidence interval for the proportion of clocks with errors in assembly.

Student 's Choice Of How They Spend Their Leisure Time...
Introduction: For many Elon students, the pressure and stress caused by a heavy course load can be
very detrimental to their health. Whether it be a lab report, final project, or cumulative test, Elon
students at one point or another will find themselves stressing out and in need to unwind. In today's
world, there are many options that allow students to unwind, taking a walk, playing video games, or
napping to name a few. These activities have nothing to do with your school work. However, will
they make an impact on your performance in the classroom? For my paper topic, I decided to
investigate how a student's choice of how they spend their leisure time will affect their collegiate
GPA. This topic is important because we teach ... Show more content on Helpwriting.net ...
Since the rise of streaming services like Netflix, children and young adults today find themselves
picking up books less and looking at screens more. Watching TV at a young age has often been
connected to poor performance on standardized tests. This includes one study which found that
Californian third graders who had televisions in their bedroom, watched more TV and in turn,
performed worse on standardized tests than those who did not (Gosline).
Some believe students who spend more time on video games instead of reading perform poorly in
school. This includes a group of schools in Michigan. Their after–school program named, "Kids
Learning in Computer Klubhouses!" gave students a place to go after school to surf the web and
play video games. The program attracted students who, "[miss] a lot of school and who [aren't]
particularly successful" according to one adult coordinator (Girod). However, both a 2000 and 1997
study found a small correlation between amount of time spent playing video games and GPA
suggesting that while the program may have attracted those who do not perform well, their
performance may have been caused by other factors (Craton).
Napping is also an option for some students. Given that many find themselves up until the odd hours
in the morning, catching up on sleep can become a priority when given free time. Taking a nap
immediately after a lecture was shown to improve cognitive memory and in turn, standardized test
scores, according to one study

How to Predict the Selling Price of a House
Executive Summary
An analysis was done to find an equation that predicts the selling price of a house. The data used in
this research analysis to predict the selling price of a house is shown in the Bryant/Smith Case 28
(See Appendix 1).
The null hypothesis stated that there is not a relationship between the selling price of a house and its
characteristics. The alternate hypothesis stated that there is a relationship between the selling price
of a house and its characteristics. A 95% confidence level was chosen and a prediction interval
which is a confidence interval estimate of a predicted value of the selling price used. The MegaStat
output of a Regression Analysis of the Bryant/Smith Case 28 data was used as the basis to calculate
the ... Show more content on Helpwriting.net ...
Even houses on the same road and in identical condition can sell for different prices due to the
negotiations that take place between buyer and seller on the price and the property valuations that
vary from agent to agent.
In a case study presented by Cain and Janssen, their approach was illustrated with an actual situation
in the pricing of townhouses. According to Cain and Janssen "A serviced parcel of land in a
suburban location is proposed for a 16–unit townhouse development. The developer has acquired
the land, prepared plans and drawings, secured zoning approval, applied for bank financing and
commenced excavation. Construction is set to begin. A major decision is the setting of prices for the
units. This is of critical importance to the success of the project. In particular, if the prices are
perceived by the market as too high, sales will be slow, and profits eroded. If on the other hand
prices are too low, profits may be foregone. Overpricing, however, is more serious than under
pricing in the present instance".
One key starting point for a House Price Index is a fixed housing stock. However, the quality of the
housing stock is likely to rise as a result of newly built homes. This, in turn, causes the mean or
median price to continue to rise even when individual properties are not appreciating (Bailey et al,
1963). For example, if a disproportionate number of high priced homes were sold in a given period,
the mean or median price would

If I had it my way, I would tone down the zero tolerance...
If I had it my way, I would tone down the zero tolerance act. It just punishes too harshly for some
things. I think that it needs to distinguish the difference between minor problems, like playing with a
toy gun, and major problems, like having drugs.
Zero Tolerance
The zero tolerance policies call for students to receive automatic suspensions or expulsions as
punishment for certain offenses, primarily those involving weapons, threats, or drugs. I agree that
safety is an important concern in schools, but sometimes the zero tolerance policy is taken too far. It
is as if administrators don't distinguish between minor and major discretions.
I do believe that students who get in fights or who bring drugs into school should ... Show more
content on Helpwriting.net ...
If anything, he/she might have gotten the idea from television or video games. I understand that
those who are disruptive to the learning process should be removed from school. It is not fair to
other students who actually want to learn. If they aren't very disruptive, I think other punishments
should be implemented, such as in school suspensions.
In the movie we watched, it discussed an eight year old boy who was suspended for playing with a
paper gun. I see no harm in this. He obviously wasn't trying to hurt anyone; he just wanted to play
cowboys and Indians like a normal young boy. However, due to the zero tolerance act, he was
suspended. I see no fairness. I would understand if he would have purposely hurt someone with his
paper gun, but he was just pretending. Also in another school, a school mascot wasn't allowed to
carry a cardboard sword around. It's not as if he wanted to attack someone with this fake sword; it is
just part of his costume.
Again, I don't think this is at all fair.
If I had it my way, I would tone down the zero tolerance act. It just punishes too harshly for some
things. I think that it needs to distinguish the difference between minor problems, like playing with a
toy gun, and major problems, like having drugs. I completely understand that schools just want to
make the environment violence free, but this just can't be done by punishing light offenders. The
world is a violent place, so until everyone can

Statistical Tools And Methods Of The And An Online...
Statistical Tools & Methods: The main statistical tools that will be used to perform my analysis are
the Microsoft Excel data analysis Tool Pak and an online calculator for a t–test. I will run a
regression analysis and an ANOVA test. Excel is the best tool to use because the data that has been
given is quantitative not qualitative. The data that the teams are given in the case study appears to be
units used/sold and manufactured and this is good data to import into Excel. The only variable that
is present is the year, quarter, or month that the units were sold/produced in. In this case study the
given data would fall in the category of a time series because the sales figures increased over the
time (years). Also in reviewing the data there is seasonal variation in the sales figures from 2006 to
2008. This category of data can be analyzed using time series methodologies. The next year's data or
projected data is dependent on the previous year's data, so time series analysis is appropriate. When
looking at the problem that A–CAT is facing we see either overstocking or under stocking
transformers. Over stocking causes capital to be tied up in the inventory. Under stocking causes A–
CAT to be unable to deliver order quickly. I chose to use ANOVA and a regression analysis so that I
could establish definitively if the transformers required is dependent or independent on the sales of
refrigerators and by how much does the sales of refrigerators influence the number of

Stats Final
* The Final Exam is worth a possible 250 points. * There are 10 essay questions. Question 1
addresses TCO A and is worth 33 points. Questions 2, 3, and 4 address TCO B and are worth 18
points. Questions 5 and 6 address TCO C and are worth 18 points. Questions 7 and 8 address TCO
D and are worth 24 points. Question 9 addresses TCO E and is worth 48 points. Question 10 also
addresses TCO E and is worth 31 points. * You will have 3 hours and 30 minutes to take the exam.
Keep an eye on the remaining time and SAVE your work often, because when the time limit is
reached, you will be exited from the exam. * Please also note that during the exam time, the
copy/paste and printing functions are disabled. * 1. (TCO A) At an ... Show more content on
Helpwriting.net ...
(Points : 18) | 7. (TCO D) An article in a trade journal reports that nationwide 28% of liquor
purchases are made by women. If B & B Liquor's proportion of sales to women is significantly
different from the national norm, the owners are considering redesigning B & B's advertising. A
random sample of 100 customers is selected resulting in 24 women and 76 men. Does the sample
data provide evidence to conclude that less than 28% of B&B's customers are women (using 
= .01)? Use the hypothesis testing procedure outlined below. a. Formulate the null and alternative
hypotheses. b. State the level of significance. c. Find the critical value (or values), and clearly show
the rejection and non–rejection regions. d. Compute the test statistic. e. Decide whether you can
reject Ho and accept Ha or not. f. Explain and interpret your conclusion in part e. What does this
mean? g. Determine the observed p–value for the hypothesis test and interpret this value. What does
this mean? h. Does this sample data provide evidence (with = .01), that less than 28% of B
& B's customers are women? (Points : 24) | 8. (TCO D) Carleton Chemical claims that they can
produce more than 800 tons of meladone on average per week. A random sample of 36 weeks of
production yields the following results. Sample Size = 36

Research Paper On Bmc Performance Assurance
Capacity Management Best Practices using BMC Performance Assurance
August 2015
Table of Contenets
 Abstract.................................................................................. 1
 BMC Performance Assurance – An Overview.....................................2
 If you can't measure it, you can't manage it!!!................................................2
 Investigate Module.....................................................................4
 Analyze Module..........................................................................4
 Performing What–If Analysis With Predict..........................................5
 Trending and Forecasting..............................................................8
 Perceiver..........................................................................................9
 Key Features of BMC Performance Assurance in CPM..................................9
 Conclusion........................................................................................................10 ... Show more
Increases the visibility and success of the performance organization by providing a consumer
viewing tool for internal customers.
Protects your performance investment by providing a performance viewing tool available across
multiple platforms.
Conclusion
Today's business needs continuous application availability in order to guarantee sustained
profitability. It requires solutions that can improve their existing performance to ensure superior
system and application performance in cost effective way. The continuous and projecting capacity
management methodology can be leveraged to deliver advanced capabilities such as timely tracking,
measurement and analysis of performance models before migrating to the production environment.
Such a proactive approach enables business to accurately predict and adjust system performance in
the actually environment with minimum risk of errors, thereby ensuring a better experience for the
end user. This ensures consistent business application availability, enhancing the end–user
experience, and ensuring superior system

Demand And Demand Literature Review
LITERATURE REVIEW 2.1 Introduction
This chapter defines demand and demand forecasting . it describes the demand determinants and
demand forecasting methods , techniques and process . 2.2 Demand and forecast concept
A forecast is a decision making tool used to predict or estimate what will occur in future by past or
present data . it is helping managers and businesses develop meaningful plans and reduce
uncertainty of events in the future .
Demand can be defined as the quantity of a product or service which people are willing and able to
buy during a given period of time . (Archer, B ) 2.2.1 The determinants of demand
The determinants of demand can causes some effects on demand curve . if demand increases this
leads ... Show more content on Helpwriting.net ...
The most common are listed below . 1. Price of the good : Generally if the price of a product gets
higher the quantity demanded becomes smaller so people buy less of that good and more of its
substitutes . And as the price of product decreases the quantity demanded increases. 2. prices of the
related goods : A substitute is a good that can be used in place of another good. For example, a bus
ride is a substitute for a train ride and a compact disc is substitute for a tape. If the price of a
substitute for a tape increases, people buy less of the substitute and more tapes. (Michael Patlan,
1998)
3. Income : As income increases the quantity of goods and services demanded. So there is a positive
relationship between income and quantity demanded . 4. Tastes or preferences of consumers : This is
the desire, emotion, or preference for a good or service. When tastes rise, so does the quantity
demanded. Likewise, when tastes fall, it will depress the amount demanded. Brand advertising tries
to increase the desire for consumer goods. ( Kimberly Amadeo, 2016 ) 5. Expectations : When
people believe that the price of something will rise in future , then they demand more of it

Relationship Between Age And Confidence
Furthermore, the relationship between age and confidence in God's existence is positive. This is
supported by the Pearson's R value of .107, which is above 0 making it positive, found in Table 2.
However, because a value of 1 represents a perfectly positive linear line, the low R value suggests
that our line is nearly horizontal and will have a small slope. In order to find how confident we are
this relationship exists we can run a t–test for R, which is included at the bottom of Table 1. The test
shows there is confidence that the relationship is evident, because the p–value is < .01, so the null
hypothesis can be rejected, that there is no relationship between the variables. Rather, there is
confidence there is in fact a relationship between age and one's faith, which supports the hypothesis
that the two variables are connected. Again this only shows the relationship is positive, but not how
great of an affect age has on faith. The relationship that faith increases as age increase is more
evident in a graph. Figure 2 compares age to the average amount of belief for its respective age
group. The data has some variation from what was expected. For example, the average belief for 18
year olds is somewhere between "Knows God Exists, But Has Doubts," and "Knows God Exists,"
which is higher than the hypothesis, which predicted they would be much more skeptical. However,
this could be because at this age kids are just now starting to break away from the influence their
parents

Understanding the Realtion Between Math and Music Essay
Math and Music: An Introduction and Mathematical Analysis
Galileo Galilei once said that the entire universe is "written in the language of mathematics". Then,
it is not surprising to learn that music is closely related to math. The mathematical application in
music will be discussed in this essay.
Rhythm and Frequency To understand the relation between math and music, the primary step is to
study the nature of rhythm, frequency and amplitude. Everything around us has its own pattern of
rhythm, from the motion of protons and neutrons, to the beats in rock music. According to Garland
(1995), rhythm is determined by the periodicity of vibration of certain object in its surrounding
substance, or medium (p. 28). The vibration is ... Show more content on Helpwriting.net ...
Amplitude refers to the distance from crest or trough of the wave to its equilibrium position.
Garland (1995) stated, frequency is a term that describes the number of waves, or vibrations, that
pass a given point per second (p. 28). It is inversely proportional to the wavelength, which is the
distance of one wave cycle. Frequency can be evaluated in the pitch of a tone: a higher pitch has a
higher frequency. A simple tone has constant frequency and amplitude, and its graph is similar to a
sine curve. Tones that are more complicated result from combinations of several simple curves. The
smallest pure tone frequency in complex tunes is called fundamental frequency. Integer multiples of
the fundamental frequency make the resultant tone musical.
The graph below shows the frequency data from A3 to A4 key. Note that A4 key has twice as much
frequency as A3 key, because any two keys that are one octave apart have the frequency ratio of 2:1.
Further inspection shows that two adjacent notes are in the ratio of 1.059...
For example:
Freq. of A#3 / freq. of A3 = 233.1 Hz / 220 Hz = 1.059...
Freq. of C4 / freq. of B3 = 261.6 Hz/ 246.9 Hz = 1.059...
Freq. of G4 / freq. of F#4 = 392.0 Hz / 370.0 Hz = 1.059...
The examples show that the frequency of any note is a product of the frequency of the adjacent note
before it and the constant number 1.059.
Proof:
Let an octave start from key A. The ratio of frequency between adjacent keys is h.
Then,

Chopin- Raindrop Prelude Analysis
Musical Analysis of... Chopin's Prelude in Dᵇ Major Op.28, no. 15 Genre/Style Chopin's prelude in
Dᵇ is from the romantic period, which began in the late 18th/early 19th century. Composers like
Chopin were at the forefront of this change in music, developing the writing and playing of solo
piano, orchestral and opera works and how music was performed. There were many composers from
this era that were very well known, especially in their later years, or after their deaths: Liszt,
Beethoven, Schubert, Schumann, Chopin, Verdi, Brahms, Tchaikovsky and Strauss are just a few of
the better known ones. There was a lot of experimentation at the start of the period therefore
changes in how composers wrote and played melodies, harmonies and rhythms ... Show more
The romantic pianos on which Chopin was composing were not too different from the modern
pianos. The cast–iron frame was developed in his time allowing powerful sounds to be created–
there were two pedals– the damper and soft pedals– creating the sustained and dynamic qualities
wanted by composers. The keyboard would have been the full length, or close to the full length of
the modern piano. Structure, Tonality, Modulations and General Musicianship TERNARY FORM |
A | B | A (2)/Coda | Bars | 1–27 | 28–75 | 76–89 | Key Signature/Modulations | Dᵇ Major | C# Minor
(enharmonic equivalent) | Dᵇ Major | The piece has a time signature of 4/4 (C=common time) and is
primarily in Db Major, modulating to C# Minor (the enharmonic equivalent). The accompaniment
(left hand) through section A is based around the tonic and dominant chords– Dᵇ and Aᵇ– with the
repeated quavers being Aᵇ – the dominant. In the B section, the repeated quavers played both as
singular notes and octaves are on the dominant G#, which is the enharmonic equivalent to Aᵇ and
therefore is the repeated quaver as in section A. A) A long flowing melody that rises and falls gently.
Two distinct sections– the first introductory melody, followed by another melody which has calls
and responses in a minor key. These phrases are joined with a small rising then falling melody and
repeated. Left hand octaves are followed by arpeggios of chords/inversions of the chords. The first
section is then

Relationship Between Body Mass Index And Gender
The first Chi Square analysis I ran is to determine a relationship between Body Mass Index(BMI)
and Gender. The P–Value is 0.202744 while the level of significance is 0.05. This led to non–
significance. The second chi– square analysis I ran is to determine a relationship between being on a
sports team or with BMI. The P– Value is 0.574058 while the level of significance is 0.574. This
means the data was non–significant. The third chi square analysis I ran is to determine if there is a
relationship between having friends who exercise and the college students feeling towards running.
The Chi– Square number is 5.43 with a P– value of 0.019. This led to significance between the two
data points. I ran a regression analysis to see if gender, being on a sports team, or the amount of time
you exercise per week influences GPA. The P– Value for gender is 0.072, while the P– Value for
being on a sports team is 0.020. With a level of significance of 0.05 these two data points were
significant. The amount of times exercised per week P– Value is 0.64, while I am looking for a level
of significance of 0.05. This led the results to be non– significant for times exercised per week and
GPA. In the introduction, I stated that I expect those who exercise to have a lower GPA than those
who don't. I felt this because I think if you work out you will have less time to study or focus on
school work. After running a regression analysis, I found that there was no relationship between the
two. The

Crusty Pizza Restaurant: Forecasting Using Regression
Crusty Pizza Restaurant: Forecasting using Regressions
Group One: Jenna Baseler and Zachary Kain
MBA 610–T304
Introduction
The purpose of this case is to determine which key variables drive Crusty Pizza Restaurant's
monthly profit and then forecast what the monthly profit would be for potential stores. Based off of
this information we will be able to make a recommendation to Crusty Dough Pizza Restaurant on
which stores they should open and which they avoid. The group was provided 60 restaurants' data
that included monthly profit, student population, advertising expenditures, parking spots, population
within 20 miles, pizza varieties, and competitors within 15 miles. For the potential stores we were
given all of this ... Show more content on Helpwriting.net ...
In a regression analysis we found that the correlation coefficient is .5862 with a very high level of
confidence (see Appendix A for detailed Regression Analysis Data). We also found that the
coefficient of determination was .3437. This shows that since there is a correlation between student
population and profits, the impact student population has on the profits is approximately 34%. We
also found from our regression equation that an increase in the student population by 1 student can
increase profit by $1.07. You can see the positive trend in the scatter plot below.
Competitors within 15 Miles Competition is a strong driver in the free enterprise of America. Crusty
Dough Pizza Company is no exception. While Crusty Dough Pizza Company varies in its number of
competitors within 15 miles by store, this variance does not seem to correlate to higher or lower
profits. When looking at the regression analysis completed using competitors within 15 miles as the
independent variable and monthly profit as the dependent variable, we find that the correlation
coefficient is a very low .133. It's safe to say there is it not a correlation between these two figures
(see Appendix A for detailed Regression Analysis Data).
Multiple Regression
As you can see from our findings, two main correlations we found that impacted monthly profit are
monthly advertising expenditures and student population. We can use

The Effect Of Gmms On Study Medication Adherence Has...
Our systematic review demonstrates that the use of GMMs to study medication adherence has
increased considerably in the past few years, with no studies having been conducted prior to 2010.
This is likely a reflection of the fact that GMMs are a relatively newer statistical technique. For
instance, Nagin first introduced GBTMs in 1999, and Muthén introduced GMMs in 2001.23, 24,
According to a literature search conducted by Nagin and Odgers, a small, but rapidly developing
clinical literature base has emerged since these models were first proposed – from 2000 to 2008,
applications of these methods to clinical studies increased from 8 to 80 publications per year.25
Thus, it is perhaps unsurprising, that increased familiarity through application in other clinical areas,
led to eventual adoption and popularization of the technique in studying medication adherence.
GMMs (and their GBTM counterparts) provide a sophisticated statistical method by which to
capture the heterogeneity within a given population, and often map onto how many researchers
conceptualize growth, in that, different types of individuals likely exhibit different trajectories of
behavior. Given the rapid adoption and application of such models within the clinical literature, and
the fact that this modeling technique is relatively novel, it stands to reason that there may be certain
methodological nuances which have been overlooked or ignored by researchers. Our review reveals
several problematic issues related to

Reflection On Acuity As An English II Predictor
Reflection 27: Acuity as an English II Predictor Methods Linear regression allows researchers to
analyze cause and effect or predictive relationships among variables (Creighton, 2007). For this
assignment, I set out to conduct a regression analysis in hopes of answering two questions: (1) is
there a relationship between student scores on Acuity, our school–wide interim testing program, and
their performance on the English II state assessment?, and (2) if the relationship is significant, can
Acuity results be used to predict student performance on the state test? To conduct this analysis I
used the 2015 Partnership for Assessment of Readiness for College and Careers (PARCC) English II
assessment results as well as the Acuity data from the first semester screener which was given in
December 2014. This test was the last comprehensive Acuity exam given to English II students
during the 2014–2015 school year (a PARCC–issued practice test was used during third nine–weeks
exams). My student sample included all students who had both a December Acuity score in the
Acuity online system and a Spring 2015 PARCC score in School Status, the school's database for
teacher and student information. With these requirements, the total sample size included 64 students.
To run my statistical analysis, I created a Google Sheet document with three columns including the
student name (1), their percent correct on the December 2014 Acuity Diagnostic test (2), and their
scale score for the Spring

Regression and Hypothesis Testing
As discussed in the Module 5 DQ 1, the most vital function of the hypothesis testing is in researches
where the needs to be a conclusion drawn from a logical approach of making a claim and proving
that the claim is rejected or not with respect to samples and respective statistical approaches. The
claim could be for the effect on blood pressure with certain hypertensive drugs as Beta–blockers, the
adverse effects of certain anti–cancer drug in comparison to another anti– cancer drug, the claim that
fast foods cause heart diseases in contrast to healthy, the claim that alcohol and drunk driving are the
major cause of accidents, etc. The hypothesis testing is taken from a normally distributed population
with the known mean and known or unknown standard deviations; the claim could also be
conducted about the standard deviations or variances. It is normally concerned with drawing
sensible inferences and are not associated with making prediction from the values drawn from the
variables.
The regression on the other hand is probably the most used statistical procedure in public health and
beyond (example, business, law, administrative area, banks, etc). Regression normally utilizes more
than one variable to predict the value of one variable in regards to the other. It uses the related
variables to construct the behavior of the taken variable. The linear correlation which is represented
as r is a number that can be achieved by using a scatter plot to draw a graph and an equation

Evaluation Of 30 Day Hospital Readmission Using A Dataset...
II. Literature Review
Silverstein et al. in [24] were to develop and validate predictors of 30 day hospital readmission
using a dataset of more than 29,000 patient's record over the age of 65 and to compare prediction
models that used alternate comorbidity classifications. In these paper they were capable to identify
the risk factors of hospital readmission and calculated the risk of all the attributes by using
prediction model. An important limitation of their study was that it did not directly include
information on patients' abilities to perform activities of daily living or other measures of physical
function.
Strack et al. in [25] studied the impact of HbA1c on readmissions. They used Multivariable logistic
regression to fit the relationship between the measurement of HbA1c and early readmission while
controlling for covariates such as demographics, severity and type of the disease, and type of
admission. The results showed that the measurement of HbA1c was performed infrequently (18.4%)
in the inpatient setting. Their analyzed that the profile of readmission differed significantly in
patients where Hba1c checked in the setting of a primary diabetes diagnosis when compared to
those with a primary circulatory disorder. But cannot addressed cause and effect although the data
provide strong support for development of protocols to examine this hypothesis directly.
Hosseinzadeh et al. in [17] conducted analysis on predictability of hospital readmissions in general

Otto's Consulting Report Essay
Otto's Auto Parts Statistical Report
Hansen Consulting proudly presents the following statistical information for Otto's Auto parts.
Statistics is a branch of mathematics that makes it possible for you to gain an edge over your
competitors by providing a method for collection of data, and a way to summarize and quantify it to
represent real world observations from which predictions can be made. Statistics also includes
stochastic modeling, which is a powerful tool that incorporates random variables to predict future
outcomes. For decades insurance companies have successfully profited from using stochastic
modeling for making predictions of unknown entities. Stochastic models can be run hundreds or
even thousands of times to show the ... Show more content on Helpwriting.net ...
The long run average, or law of large numbers, makes it possible for owners and managers to better
visualize how optimal strategies can be formulated in order to lower their costs and increase their
systems' performance and effectiveness.
We recently ran a 500–week simulation to determine the optimal order quantity and reorder point for
the titanium covers your company purchases and holds, for use in the internal assembly of your
catalytic converters. In creating your titanium cover simulation we used random variables to
simulate different titanium part demand values and supplier lead times for your manufacturing
facility and analyzed the affect they had on your inventory, holding costs, shortage costs, and
ordering costs for each week. Our analysis shows that your optimal strategy for order quantity to be
1,000 units, and your optimal reorder point to be 800 units. A 500–week simulation plot for your
optimal strategy was generated (located on the next page) to demonstrate that the long run average
begins to reveal itself at ~270 weeks. As the plot starts to "flatten out" near ~270 weeks, the long run
average shows that your optimal strategy's expected value is estimated at ~$307.
500 –Week Simulation Plot for Optimal Strategy
Employee profile – Gender
An employee profile was created using descriptive statistics and statistical estimation. Descriptive
statistics is a procedure used

Move Your Feet By Junior Senior: Song Analysis
Nostalgic Consonance
Music, for generations, has influenced everyone's lives in some way. Friends may turn to recent
dance songs to get them motivated, or family will turn on a familiar tune during a road trip. These
road trip songs are unique due to the connection the song will have with the people in the car.
Everyone in my family has their own song. My road trip song is the early 2000s dance hit Move
Your Feet, by Junior Senior. This track is close to me due to the balance between its charming
chorus, catchy beat, and nostalgic value. By listening to the song so many times, it has reminded me
how family bonds can easily form around music.
The track eases the listener in by starting off very quiet, as if one is hearing it from a distance. ...
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One might discern that the simple melody is nice, but becomes boring after a few repetitions. On the
other hand, a child could hear this same simple melody without being bored. As children tend to
listen to these simple melodies more often, those same melodies become nostalgic later in life. Some
studies Levitin has made has shown that people's "musical preferences are also influenced by what
[they've] experienced before, and whether the outcome of that experience was positive or negative."
(Levitin 242) These studies have merit as I am a prime example. Move Your Feet has been a favorite
of mine since a young age and the nostalgia I hold for the song has affected my taste in music as a
whole today. The nostalgia this song brings forth for me is mainly from when my sisters and I used
to play a video game named Dance Dance Revolution. The game included a huge selection of music
in which the player would dance and earn points based on how accurately timed the movements
were. One might extrapolate which song was my personal favorite. Therefore, Move Your Feet has
always been known as "my song" between my sisters and I. After reading British neurologist,
naturalist, and author, Oliver Sack's article "Music on the Brain: Imagery and Imagination," I
realized that the concept of "musical imagery" exemplifies what I feel when I think of Dance Dance
Revolution. He described musical imagery as "intense and repeated exposure to a particular piece or
sort of music." (Sacks 37) I possess such an exposure to the song due to the quotidian nature of my
gaming habits. Consequently, whenever Dance Dance Revolution comes to mind, I immediately
think of Move Your Feet by Junior Senior. Additionally, at my sister's wedding, she played this song
on the dance floor. Ergo, whenever I think about that time, this song plays in my

The Importance Of Medieval Music
We ask ourselves, does medieval music need to be protected against certain types of scholarly
approaches? In the 14th century to 15th century it was important for the people to follow certain
approaches and interpretations with music. Many men and women withdrew from the materialistic
world, and devoted themselves to church; they devoted themselves to lives of prayer or theology. In,
Elizabeth Eva Leach's article, "Gendering the Semitone, Sexing the Leading Tone: Fourteenth–
Century Music Theory and the Directed Progression, "Reading and Theorizing Medieval Music
Theory: Interpretation and Its Contexts," and in Sarah Fuller's article, "Concerning Gendered
Discourse in Medieval Music Theory: Was the Semitone 'Gendered Feminine?" these summaries
consist a contrast why the authors argument why medieval music was an integral part of everyday
life for people living in the 14th century through 15th century . These authors both have a different
viewpoint of how masculinity and feminine were influenced under the power of the church and
composers who exemplified these ideals, and brought the compositions out of church and spread it
across Western Europe. They both agreed that music and semitones were influenced by gender, but
disagreed how feminine and masculine affected people in a certain approach. Music of the medieval
era included liturgical music used for church and chants, and consisted a lot of voice exchanges
from instruments and choral music. Composers had a strict way

Onset Rime Case Study
1. The two major independent constructs compared by the authors were onset–rime and phoneme
awareness. The dependent constructs were early reading ability and general academic ability. The
independent constructs were operationalised using twelve trials of a segmenting and a blending task;
which was given at age five. Half of the trials involved blending phonemes and the other half
involved blending onset–rime and rhymes; scores for both the tests were combined.
Child reading ability at age five was operationalised through the combined scores of three readings
tasks: non–sense word reading task, spelling task and a reading task. Academic ability at age seven
was operationalised through the implementation of a national test. The test ... Show more content on
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i) Randomly partitioning children into two groups and measuring reading achievement before and
after intensive training in phoneme–level phonological skills for one group and normal schooling for
another group is superior to the measure variable design used in this experiment as it would be able
to infer more causation. As the current study used measured variables, phonological skills cannot
conclusively be suggested to have a causal effect on reading development as the relationship can
only suggest the strength of the correlation between the two variables. However, if pre–test reading
skills were measured at age five, then the intervention was implemented for half the students with
the remainder being the control, then follow–up tests were conducted at age seven more causation
could be inferred as there is a control group present to compare to and if confounds are controlled
for (random assignment to reduce PAV confounds) there will be only one IV controlling the DV and
thus causation can be inferred. ii) One practical advantage of training studies for reading
development is that if confounds are controlled for the pre and post test scores can suggest if an
effect was observed for the treatment group compared to the control group. With reference to the
experimental study a practical advantage would be that schools could place emphasis on phoneme
awareness in order to improve reading

Dynamic Data Driven Selection Of Sensor Streams
On Dynamic Data–Driven Selection of Sensor Streams
This paper covers the online sensor selection in which the sensor network and relations between
sensors dynamically change by time and a model which is working for selection of sensors at time
may not work for the sensor selection at time , . The selection model of this paper is similar to
model discussed in section 3 (i.e. Online distributed sensor selection) but it is based on a well
defined special centralized algorithm (central server calculates the utility function and decides which
sensors should be selected).
The main goal of the sensor selection is to preserve battery by selecting a subset of sensors for
prediction. After the sensors have been selected, their value will be read in multiple turns (i.e. active
turns) and it will be used to predict the unselected sensors, then the value of all sensors will be read
(i.e. passive turn) and based on that the active set of the next interval will be selected. The art of this
paper is to find a way to switch between active and passive selection modes in some specific update
intervals (number of active turns before a passive turn) to find the optimized selection of sensors for
the next interval of data collection, this way it preserves the power requirement by activating only
few sensors for multiple turns and then trying to maintain the active set using the collected data of
the passive turns. It accomplishes this task by doing evaluation regression model over multiple

Property Crimes
CASE 49: PROPERTY CRIMES
I. Executive summary
The focus of this study is the examination of the data provided by U.S government agencies. Our
analysis revealed that of the eight possible contributing factors, only three variables (namely,
urbanization rate, high school dropout rate, and population density) affected property crime rates.
Our data analysis model accounted for approximately 66% of the factors contributing to property
crimes. The model is generally considered to be statistically strong, however, if we need to account
for the remaining 34% of factors contributing to property crime rates in the U.S., further data and
evaluation of other possible factors would be necessary.
II. Introduction According to the US ... Show more content on Helpwriting.net ...
Error | 754.255 | Dep. Var. | CRIMES (Y) | | ... | | | | | | | Regression output | | | | confidence interval |
variables | coefficients | std. error | t (df=41) | p–value | 95% lower | 95% upper | Intercept | –
1,008.0855 | 1,003.2571 | –1.005 | .3209 | –3,034.2043 | 1,018.0334 | PINCOME (X1) | 0.0156 |
0.0731 | 0.213 | .8323 | –0.1320 | 0.1632 | DROPOUT (X2) | 73.3997 | 21.5165 | 3.411 | .0015 |
29.9463 | 116.8532 | PUBAID (X3) | –49.3649 | 39.8547 | –1.239 | .2225 | –129.8531 | 31.1233 |
DENSITY (X4) | –2.2108 | 0.7018 | –3.150 | .0030 | –3.6281 | –0.7934 | KIDS (X5) | 0.4108 | 1.3363
| 0.307 | .7601 | –2.2878 | 3.1095 | PRECIP (X6) | –0.5357 | 10.9622 | –0.049 | .9613 | –22.6744 |
21.6030 | UNEMPLOY (X7) | –57.4497 | 78.7026 | –0.730 | .4696 | –216.3928 | 101.4933 | URBAN
(X8) | 65.8552 | 11.0268 | 5.972 | 4.74E–07 | 43.5862 | 88.1242 | | | | | | | |
The summary of this analysis: 1. R^2 = 68.6%: This is the proportion of variation in the dependent
variable Y that is explained by variation in the independent variables Xi. In other words, using this
model, almost 67% of the variation in the crime rate can be attributed to the independent variables
X1 – X8. 2. To determine how much effect each of the independent variables has on the dependent
variable, we examine the correlation coefficient for each of the independent variables. The higher

Antarctica And Its Effects On Antarctica
Please note that you will have 4 minutes to recall the facts, and that you will be taking the test
immediately." The word Antarctica was shown in italics to make it clear that they were only to be
asked to recall information about Antarctica. Participants in the delayed condition were given the
same prompt except they were asked to predict their performance after a 30–min delay: "In thirty
minutes you will be asked to recall as many facts about Antarctica as you can. What percentage of
the facts about Antarctica do you think you will be able to recall? Please note that you will have 4
minutes to recall the facts, and that you will be taking the test in 30 minutes." Participants were
instructed to answer the prompt by telling the experimenter a number between 0 and 100.
Participants in the immediate condition were then given a blank sheet of paper and 4 minutes to
recall as many facts about Antarctica as possible. Participants in the 30–min delay condition were
given the same test after a 30–min interval, which was filled with a series of unrelated distractor
tasks (e.g., learning and retrieving category–exemplar pairs). It is worth noting that participants
were not informed of the nature of the distractor tasks before making their predictions, a factor
which may have influenced the accuracy of such predictions. Given the purpose of the study,
however, we felt it was more important to focus instructions on the particular passage that was to–
be–tested

The Sports Equipment Company ( Tdsec )
The Doortodoor Sports Equipment Company (TDSEC) is a unique organization that has found a
way to cut out the middle man, they supply sporting good the everyday sports enthuses right on the
there doorstep. Being that they are the only organization to start this trend in the nation they want to
take a closer look at their sales associates, map out their current patterns and treads, and well as
predict the future trends of the organization. The Markov analysis is a great tool for TDSEC to use
to predict the future trends of their organization, most because it doesn't rely on the behavior or state
of the organization when the analysis is completed.
Internal Labor Market The state of the internal labor market can give the organization incites into
many different elements which help predict the pattern of early mobility, the possibility of higher–
level positions being filled from within, firm–specific human capital, incentive structures, or pure
institutional factors (NBER, 2003). As stated in NBER (2003) the state of the internal labor market
must be fluid in order for the organization to flourish. A clear sign of a strong internal labor market
can be indicated through the state of how the organization employees; the high–level positions are
filled almost exclusively through internal promotion and low levels will be filled through external
hiring (NBER, 2003). Taking a look at TDSC's promotion path, one can conclude that their internal
market is in a fairly good condition,

Analyzing and Summarizing Math Data
Introduction | | |AJ DAVIS is a department store chain, which has many credit customers and wants
to find out more information about these customers. A sample of 50 credit customers is selected with
data collected on the following five variables: 1. LOCATION (Rural, Urban, Suburban) 2. INCOME
(in $1,000 's – be careful with this) 3. SIZE (Household Size, meaning number of people living in
the household) 4. YEARS (the number of years that the customer has lived in the current location) 5.
CREDIT BALANCE (the customers current credit card balance on the store 's credit card, in $).
|PROJECT PART A: Exploratory Data Analysis | | ... Show more content on Helpwriting.net ...
Discuss your 3rd pairing of variables, using graphical, numerical summary and interpretation H.
Conclusion
|Project Part B: Hypothesis Testing and Confidence Intervals | |
Your manager has speculated the following: a. the average (mean) annual income was less than
$50,000, b. the true population proportion of customers who live in an urban area exceeds 40%, c.
the average (mean) number of years lived in the current home is less than 13 years, d. the average
(mean) credit balance for suburban customers is more than $4300. 1. Using the data set currently
posted inside DocSharing, perform the hypothesis test for each of the above situations in order to
see if there is evidence to support your manager's belief in each case a.–d. In each case use the
Seven Elements of a Test of Hypothesis, in Section 6.2 of your text book with α = .05, and explain
your conclusion in simple terms. Also be sure to compute the p–value and interpret. 2. Follow this
up with computing 95% confidence intervals for each of the variables described in a.–d., and again
interpreting these intervals. 3. Write a report to your manager about the results, distilling down the
results in a way that would be understandable to someone who does not know statistics. Clear
explanations and interpretations are critical. 4. All

Regression Analysis
Confidence intervals and prediction intervals from simple linear regression
The managers of an outdoor coffee stand in Coast City are examining the relationship between
coffee sales and daily temperature. They have bivariate data detailing the stand 's coffee sales
(denoted by [pic], in dollars) and the maximum temperature (denoted by [pic], in degrees
Fahrenheit) for each of [pic] randomly selected days during the past year. The least–squares
regression equation computed from their data is [pic].
Tommorrow 's forecast high is [pic] degrees Fahrenheit. The managers have used the regression
equation to predict the stand 's coffee sales for tomorrow. They now are interested in both a
prediction interval for tomorrow 's ... Show more content on Helpwriting.net ...
The next term in the prediction interval formula is the standard error of the estimate, [pic]. It can be
computed from the mean square error (MSE), which is given to be [pic]:
[pic].
The last part of the prediction interval formula consists of the square root of the sum of [pic] and a
fairly long expression. We do not need to compute the long expression, though, because we were
given its value: [pic]. We have
With this information, we can compute the [pic] prediction interval for the coffee sales given a
maximum temperature of [pic] degrees Fahrenheit:
[pic].
Upon simplification, this is the interval whose lower limit is approximately [pic] and whose upper
limit is approximately [pic]
2. Because there 's more precision involved in estimating the mean of a distribution than in
predicting a particular observation from that distribution, we would expect the confidence interval to
be narrower than the prediction interval. We can verify this by comparing the formulas for
computing the intervals (shown near the top). As noted previously, the only difference between the
prediction interval formula and the confidence interval formula is that the prediction interval
formula has a [pic] in the sum underneath the square root, while the confidence interval formula

does not. This makes the margin of error (the term following the "[pic]") greater in the prediction
interval formula than in the confidence interval formula, which means that the

Colonial Broadcasting Essays
Colonial Broadcasting Company Executive Summary Colonial Broadcasting Co (CBC), a major
American television network, must determine which of the different factors plays a key role in
optimizing the ratings of its movie. The following report contains statistical analysis on the different
relationships between the factors influencing ratings. The Regression Model For a detailed
description of the variables and the defined statistical terms used in this report, see [ Annex 1 ].
Based on the sample data provided and the statistical analysis, the following regression equation has
been derived: Ratings = 13.729 – 1.540*BBS + 1.281*Winter + 1.164*Sunday +1.593*Monday +
1.854*Fact + 0.910*(SQRT)Stars + 8.413*Log (Previous Rating) – 10.206 ... Show more content on
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In the 1st trial (see Annex 8), Autumn was the lowest among the four variables falling off the
acceptance region and subsequently was removed. All three noticeably improved their t–test, p–
value and confidence interval test afterwards but ABN was far from an acceptable range. The 2nd
transformation (see Annex 9) will show that the ABN variable was still considerably far from an
acceptable region and later had to be taken out. Looking at the final model (see Annex 3), all
variables have solid t–test, p–value and confidence interval results. Choose the best model Aside
from the models mentioned, an assortment of models was produced with a variation of non–linear
transformations. I rejected them even though they produced higher adjusted R2 values, as I could
not adequately explain the reasoning for the non–linear transformations involved and the increase
was not large enough to warrant further investigation in the time available. However, I recommend
collecting additional data to further examine these variations. The final model chosen consisted of
the following transformations: Initial Model: BBS, ABN, Winter, Autumn, Sun, Mon, Fact,
SQRT(Stars), Log Previous Rating, Log Competition Final Model: BBS, Winter, Sun, Mon, Fact,
SQRT(Stars), Log Previous Rating, Log Competition Good Predictors of Movie Ratings Factors that
will greatly help and have a linear impact on the ratings of the show are the

Asclepius: A Narrative Fiction
Asclepius' hands, which now glowed the usual pinkish color, and any visual presence of the vibrant
blue color quickly disappears. The cold that was brought along with it, was quickly replaced by
Axel's normal heated feeling. Relaxing, he sat back down beside her, keeping one hand still gently
laid atop Rogue's whilst the other waves off her apology. "No... No please, don't... Don't be."
Asclepius starts in a tired, kind tone, before his eyes meet the other's. Giving her a confident smile,
Asclepius voice slowly echoes back into her head. "You couldn't of... Of known, and... And I was
unaware that... That seeing you would trigger... Trigger him to attempt to... To take control, so I... I
could not of warned you.." Axel reassured her in a knowing tone, patting her hand lightly with his
own had located on top. ... Show more content on Helpwriting.net ...
It was inevitable that it would. Listening to her confession, Axel's head shifted to a different
position, one that made him look towards an old, dying tree. The only one in the outline of rather
healthy trees that boxed–in this odd clearing. Looking on, to that particular tree with an absent mind,
Asclepius nods simply. It takes him a minute, staring at the dead piece of vegetation, to think of the
words he was to use to describe what it is that he could do for her. But eventually, the words quickly
poured out of his mind, and into

What Is Multiple Variables?
The real value in testing for a relationship between scale variables is not in knowing the strength of
the correlation, but rather in being able to forecast (Mirabella, 2011). In a multiple regression model,
we can choose to evaluate several variables at the same time; however, there is still only one
dependent scale variable. When calculating multiple variables, we keep just the variables which are
0.05 significance level. However, we only eliminate one at a time. Ironically, removing two
variables at a time may result in removing a significant variable by mistake. In the context of testing
hypothesis on any arbitrary subset of regression parameters, one may use the non–sample prior
information on the explanatory variables to ... Show more content on Helpwriting.net ...
The prediction interval is to forecast the MBA GPA of a 40–year–old student who studies six hours
per week, works full time, and has a BS GPA of 3.0. With changing the variables from the initial
regression model with a 95% confidence level which forecast the MBA GPA of 2.96 makes the
forecast irrelevant. Presumably, after removing the gender variable from the calculations, I moved
the other two columns of data over to columns three and four respectively. Therefore the p–value for
the BS GPA remained 0.0000 which is less than the significance level of 0.05 determining this
variable to remain as significant. The next variable was hours the student studies per week, and the
p–value changed to 0.0018, which resulted in this variable is a significant variable as well. As a
result, the multiple regression model is MBA GPA = 0.38381 + 0.77785 (BS GPA) + 0.0444 (Hours
Studying) + 0.012 (Works Full–time) + –0.0004 (Age). The BS GPA is the student's undergraduate
grade point average, hours studying is the average hours the student spent studying each week,
works full time is if the student worked full time or not, and the age is the age of the MBA student.
Therefore, we can conclude with 95% confidence level that a 40–year–old graduate student who had
an undergraduate grade point average of 3.0, spends six hours studying each week, and works full
time will have an MBA GPA of 2.978. Given individual differences

Aj Davis Department Stores
AJ Davis Department Stores – Project Part A, B, and C
Stacie Borowicz
June 14, 2013
Math 533
Project Part A – Exploratory Data Analysis
Credit Balance ($)
Based on a sample of 50 customers, the credit balance for customers of Davis Department stores is
on average $3970.00. Based on the graph, 18 of the 50 sampled fall below and 17 fell above the
average. The standard deviation for credit balance is 931.9.
Income
Annual Income of Davis Department Stores customers range anywhere from $22,000 to $67,000.
Majority of their customers have an annual income of about $43,740. The standard deviation for
income is $14,640.
Size
Average household size is about 3.4. Based on the graph above, 15 of the 50 sampled have a ...
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10. For a household size of 10 the confidence interval is (5926.96, 7320.36) which will include the
mean credit balance and the prediction interval is (5195.30, 8052.01) which will include the actual
credit balance for a household of that size.
11.
Regression Equation
Credit Balance($) = 1276.02 + 32.2719 Income ($1000) + 346.852 Size + 7.88209 Years
(Appendix C)
APPENDIX A
General Regression Analysis: Credit Balance($) versus Size

Regression Equation
Credit Balance($) = 2591.44 + 403.221 Size
Coefficients
Term Coef SE Coef T P
Constant 2591.44 195.064 13.2851 0.000
Size 403.22 50.946 7.9147 0.000
Summary of Model
S = 620.162 R–Sq = 56.62% R–Sq(adj) = 55.71%
PRESS = 19992921 R–Sq(pred) = 53.02%
Analysis of Variance
Source DF Seq SS Adj SS Adj MS F P
Regression 1 24092210 24092210 24092210 62.6421 0.000000 Size 1 24092210 24092210
24092210 62.6421 0.000000
Error 48 18460853 18460853 384601 Lack–of–Fit 5 2499467 2499467 499893 1.3467 0.263274
Pure Error 43 15961386 15961386 371195
Total 49 42553062
Fits and Diagnostics for Unusual Observations
Credit
Obs Balance($) Fit SE Fit Residual St Resid 5 1864 3397.89 113.691 –1533.89 –2.51600 R
R denotes an observation with a large standardized residual.
APPENDIX B

Case Study "No Bats in the Belfry: The origin of...
Biology 124 Lab Case Study "No Bats in the Belfry: The origin of White–Nose Syndrome in Little
Brown Bats" Part 1 Questions 1. What is the basic question of the study and why is it interesting?
The basic question of thIs study is "Is the European strand of Pseudogymnoascus destructans
causing White–noise syndrome (WNS) in the United States?" This question is interesting because if
it is the European strand of P. destructans causing WNS in North America then why is it not
occurring in as high of levels and why are bats not dying in as big of a capacity in Europe as the bats
in North America. 2. What specific testable hypotheses can you develop to explain the observations
and answer the basic question of this study? Write at ... Show more content on Helpwriting.net ...
2. Describe an experiment you could use to differentiate between the null and alternative
hypotheses. An experiment could be devised in which bats are taken in a large quantity and one
group in injected with the North American P. destructans strand, a second group is injected with the
European P. destructans strand and a third group is injected with a placebo. The bats should be
placed in a cave like setting and the effects of the injections should be examined. Part 3 Questions 1.
Use the graph below to predict what the results will look like if the null hypothesis is supported. The
total arousal counts in the control treatment at each interval is graphed for you. Justify your
predictions. 2. Use the graph below to predict what the results will look like if the null hypothesis is
rejected. The total arousal counts in the control treatment at each interval is graphed for you. Justify
your predictions. Part 4 Questions Study Results 1. How do your predictions compare with the
experimental results? Be specific. My predictions for the acceptance of the null hypothesis were
very similar to the actual results with a few variations. My prediction of total arousal times for the
first interval were the same however, on the second interval I had 60 total arousal times for the bats
injected with the European P. destructans strand and the actual results had a total arousal of 80 bats
injected with the European strand at interval

Mat 510 Week 11 Final Exam
MAT 510 Week 11 Final Exam Click Link Below To Buy: http://hwcampus.com/shop/mat–510–
week–11–final–exam/ Or Visit www.hwcampus.com
Question 1: The principal drivers of the rapid change in the global economy are:
Question 2: What is tool that can be used to detect the structure variation?
Question 3: Improving the quality of process measurements is:
Question 4: Sub–optimization occurs when:
Question 5: Which type of variation was critical to resolving the realized revenue case study?
Question 6: In evaluating data on our process outputs, four characteristics we might investigate are:
central tendency, variation, shape of distribution, and stability. Which of the following tools would
be most helpful to determine the ... Show more content on Helpwriting.net ...
Displaying cross–function activities in simple terms
Helping maintain the big business picture
Question 27: The fact that processes tend to be dynamic, rather than static, is a key principle of
statistical thinking. Which of the following is a natural consequence of this fact?
Question 28: Suppose the Process Improvement Framework were used to attack a problem where
the process in question was inherently unstable. Which of the following would be a likely result of
this effort?
Question 29: A manufacturing process has been experiencing problems. The operators charting the
process data have identified the cause to be due to an unanticipated change in incoming raw
materials. This problem should be considered:
Question 30: Figure 2.16 shows a chart of the Dow Jones Industrial Average from 1990 through
2011. This plot reveals some obvious change points, such as the 2008 financial collapse, and some
unexplainable short–term variation. Assuming that these are the only sources of variation in this
plot, what source of variation is NOT visible in the plot?

Question 31: In the advertising case study, the unexpected "V" shaped pattern in the plot of
advertising dollars versus sales indicated

Internship At National Load Despatch Centre ( Nldc ),...
ABSTRACT
Internship at National Load Despatch Centre (NLDC), Power System Operation Corporation Ltd
can be divided into two periods. In starting weeks, I get the overview of Indian power sector,
understood the Indian power grid, functions of NLDC and many technical terms but later the project
"Short Term Load Forecasting(STLF) using ANN Techniques" was assigned to me. I have to
forecast the load for Delhi using ANN Technique.
Load forecasting is the technique for prediction of electrical load. It is much need for a generating
company to know about the market load demand in a deregulated market for generating near to
accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of
irregular supply and in case of excess generation the generating company will have to bear the loss.
There are various factors which influence the behavior of the consumer load. These factors can be
categorized as Time factor, weather, economy and random disturbances. Accurate forecasting can
maximize the profit of producers and for consumers, it can maximize their utilities. STLF plays an
important role in electric power system operation and planning. An accurate load forecasting not
only reduces the generation cost in a power system, but also provides a good principle of effective
operation. It is trained using back propagation algorithm and tested. The results obtained from
neural network are presented and the results show that the neural network based

Why Demographic Trends Spell Trouble For China And Russia
Introduction All around us there are forecasts predicting the future. Whether it's an article in Forbes
entitled "Why Demographic Trends Spell Trouble For China And Russia – And Prosperity For US"
predicting a prosperous economy for the United State and Canada in 2035 (Preston, 2015) or the
CEO of Facebook Mark Zuckerberg at the F8 2016 Conference unveiling his roadmap on what
Facebook would look like in 10 years (Buxton, 2016). Both are examples of prediction that use
different forecast techniques to predict a future outcome. The article from Forbes, predicts a possible
future due to demographic trends, whereas, Mark Zuckerberg forecasts a preferably or probable
future due to advancements in technology. However, there are number of different forecasting
techniques that are used today in business, government, and military to predict future outcomes.
This paper looks at the different techniques for forecasting the future and how some futurists use
these different techniques for predicting future trends, technologies, and events.
Forecasting Methods First it must be noted, that no forecasting technique can be certain, however,
they can come close to a possible, preferable, or probable future. Second, the further into the future
the predictions the higher the uncertainty of the forecast (Gordon, 2009). The reason for greater
uncertainty is the possible of a wild card or black swam event happening that would be a total
surprise and have a major effect on the long term

Correlation Between Customer Satisfaction And The Total...
Correlation and regression analysis allows us to determine the strength of a linear relationship, the
direction of a linear movement, and if a relationship exists between two variables (Donnelly, 2013).
In this scenario, a student intern noticed a possible correlation between the customer's satisfaction
and the total amount of the bill. The intern decided to collect 100 customer satisfaction surveys and
match each of them with the corresponding total bill. The intern wants to use the collected data to
develop a hypothesis, conduct a test for correlation, and develop a prediction equation for customer
satisfaction. I will assist the student intern by developing an appropriate hypothesis for the data
collected, conducting a test for correlation, developing a prediction equation for customer
satisfaction, and analyzing the results to better inform the owners. Correlation and regression
analysis will tell us if a relationship exists between customer satisfaction and the total amount of the
bill and it will tell us the strength of the relationship. Currently we are unsure if there is a
relationship between the two variables, so we need to develop a hypothesis and determine the
correlation coefficient. Since we are only looking to see if there is a relationship between the two
variables, we will create a hypothesis that answers this question. The null hypothesis will be that the
population correlation coefficient equals 0 and the alternative hypothesis will be that the

Relationship Between Net Migration And Economic Growth
Introduction
According to the Solow model, the higher the country's rate of population growth, the lower its
income (GDP per capita) and therefore cause a decrease in economic growth. Therefore, net
migration plays an important role in effect of economic growth. But, immigrants may have higher
average human capital than natives and emigrants which might increase the economic growth if this
is sufficient to offset the dilution of physical capital. The aim of this report is to investigate the
relationship between net migration and economic growth and in particular to know whether there
was a positive or negative effect of net migration (defined as the number of immigrants deduct the
number of emigrants) between year 2000 and 2010, relative ... Show more content on
Helpwriting.net ...
Therefore, two–tail hypothesis testing has applied to test the effect of both variables, the null and
alternative hypothesis are below:
H0: β1= 0 –There is no linear relationship between two variables
H1: β1 ≠ 0 – There is a linear relationship between two variables
Excel and a significance level of 5% was used to analyse the hypothesis (α=0.05).
Result
Chart 1 annual growth rate against net migration rate 2000–2010(all data from sample)
Country Extreme point (%)
Equatorial Guinea 23.19826168 Outlier (%)
Qatar 173.05
United Arab Emirates 153.31
Bahrain 65.38
Kuwait
33.64
Table 1: Extreme point and outlier of countries on the scatter plot
Table above shows the extreme point and outlier of countries. As both extreme point and outlier
exists, they may have strong influence on the fitted regression line since the linear regression model
is based on minimizing the sum of squared errors. Chart 2: annual growth rate against net migration
net 20000–2010 (extreme point and outliers excluded)
As there is not much difference for chart 1 and 2, therefore, results below used chart 1 analyse.
The regression equation is: net m̂ igration rate= 7.958 – 0.593 annual growth rate b0=0.07958 means
that 7.958% is the portion of net migration not explained by the dependent variable (annual growth
rate). B1 = –0.00593 indicates

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