Dynamic Data Driven Selection Of Sensor Streams

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
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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
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Therefore, two–tail hypothesis testing has applied to test the effect of both variables, the null and alternative hypothesis are below:
H0: ОІ1= 0 –There is no linear relationship between two variables
H1: ОІ1 в‰ 0 – There is a linear relationship between two variables
Excel and a significance level of 5% was used to analyse the hypothesis (О±=0.05).
Result
Chart 1 annual growth rate against net migration rate 2000–2010(all data from sample)
CountryExtreme point (%)
Equatorial Guinea23.19826168 Outlier (%)
Qatar173.05
United Arab Emirates153.31
Bahrain65.38
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 mМ‚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

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

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 $14,640.
Size
Average household size is about 3.4. Based on the graph above, 15 of the 50 sampled have a... Show more content on Helpwriting.net ...
10. For a household size of 10 the confidence interval is (5926.96, 7320.36) which will include the mean credit balance and the prediction interval is
(5195.30, 8052.01) which will include the actual credit balance for a household of that size.
11.
Regression Equation
Credit Balance($) = 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
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

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

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 MuthГ©n 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

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

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,

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

Research Paper On Bmc Performance Assurance
Capacity Management Best Practices using BMC Performance Assurance
August 2015
Table of Contenets
пѓ Abstract.................................................................................. 1
пѓ BMC Performance Assurance – An Overview.....................................2
пѓ If you can't measure it, you can't manage it!!!................................................2
пѓ Investigate Module.....................................................................4
пѓ Analyze Module..........................................................................4
пѓ Performing What–If Analysis With Predict..........................................5
пѓ Trending and Forecasting..............................................................8
пѓ Perceiver..........................................................................................9
пѓ Key Features of BMC Performance Assurance in CPM..................................9
пѓ Conclusion........................................................................................................10 ... Show more content on Helpwriting.net ...
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 projectingcapacity 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

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

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

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

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 Helpwriting.net ...
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 CompetitionFinal 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

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

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
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

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

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

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
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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,

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

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...
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(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 пЃЎ = .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 пЃЎпЂ = .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

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
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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

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

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 Averagefrom 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

Chopin- Raindrop Prelude Analysis
Musical Analysis of... Chopin's Prelude in Dбµ‡ Major Op.28, no. 15 Genre/Style Chopin's prelude in Dбµ‡ 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 content on Helpwriting.net ...
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| Dбµ‡ Major| C# Minor (enharmonic
equivalent)| Dбµ‡ 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– Dбµ‡ and Aбµ‡– with the
repeated quavers being Aбµ‡ – 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 Aбµ‡ 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

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

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

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

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

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...
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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

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 О± = .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

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

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.

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. ... Show more content on Helpwriting.net ...
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

Antarctica And Its Effects On Antarctica
Please note that you will have 4 minutes to recall the вЂЁfacts, and that you will be taking the test immediately." вЂЁThe word Antarctica was shown
in italics to make it clear вЂЁthat they were only to be asked to recall information about вЂЁAntarctica. Participants in the delayed condition were
given вЂЁthe 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 вЂЁthe facts about Antarctica do you think you will be able вЂЁto recall? Please note that
you will have 4 minutes to вЂЁrecall the facts, and that you will be taking the test in 30 вЂЁminutes." вЂЁParticipants were instructed to answer the
prompt by telling вЂЁthe experimenter a number between 0 and 100. вЂЁParticipants in the immediate condition were then given a вЂЁblank sheet of
paper and 4 minutes to recall as many facts вЂЁabout Antarctica as possible. Participants in the 30–min delay вЂЁcondition were given the same test
after a 30–min interval, вЂЁwhich was filled with a series of unrelated distractor tasks вЂЁ(e.g., learning and retrieving category–exemplar pairs). It is
вЂЁworth noting that participants were not informed of the nature вЂЁof the distractor tasks before making their predictions, a factor вЂЁwhich may
have influenced the accuracy of such predictions. вЂЁGiven the purpose of the study, however, we felt it was more вЂЁimportant to focus instructions
on the particular passage that вЂЁwas to–be–tested

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

Dynamic Data Driven Selection Of Sensor Streams

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