In many investigations, two or more variables are observed for each experimental unit in order to determine: 1. Whether the variables are related.
2. How strong the relationships appear to be.
3. Whether one variable of primary interest can be predicted from observations on the others. Regression analysis concerns the study of relationships between quantitative variables with the object of identifying, estimating, and validating the relationship. The estimated relationship can then be used to predict one variable from the value of the other variable(s). In this article, we introduce the subject with specific reference to the straight-line model. Here, we take the additional step of including the omnipresent random variation as an error term in the model. Then, on the basis of the model, we can test whether one variable actually influences the other. Further, we produce confidence interval answers when using the estimated straight line for prediction. The correlation coefficient is shown to measure the strength of the linear relationship. One may be curious about why the study of relationships of variables has been given the rather unusual name “regression.” Historically, the word regression was first used in its present technical context by a British scientist, Sir Francis Galton, who analyzed the heights of sons and the average heights of their parents. From his observations, Galton concluded that sons of very tall (short) parents were generally taller (shorter) than the average but not as tall (short) as their parents. This result was published in 1885 under the title “Regression Toward Mediocrity in Hereditary Stature.” In this context, “regression toward mediocrity” meant that the sons’ heights tended to revert toward the average rather than progress to more extremes. However, in the course of time, the word regression became synonymous with the statistical study of relation among variables. Studies of relation among variables...

...StraightLine Equations and Inequalities
A: Linear Equations - Straightlines
Please remember that when you are drawing graphs you should always label your axes and that y is always shown on the vertical axis. A linear equation between two variables x and y can be represented by y = a + bx where “a” and “b” are any two constants. For example, suppose we wish to plot the straightline If x = -2, say, then y = 3 + 2(-2) = 3 - 4 = -1 If x= -2 -1 -1 1 0 3 1 5 2 7 As you can see, we have plotted the five points on the graph. They do indeed all lie on a straightline and we have joined them together to show the line. Of course, you could draw the line by just plotting any two points on it and then joining and extending those two points. y = 3 + 2x ..... and so on (see table below)
Then y =
y
x
The equation simply represents the relationship between two variables x and y. For example: suppose our basic salary is £4000 and we add commission to that at the rate of 5% of our total sales. Call y our total salary and call x our sales (both in £) then we could represent this relationship as y = 4000 + 0.05x (5% is five hundredths i.e. 0.05) Then, if we knew that total sales were 6000, we could work out total salary: y = 4000+0.05(6000) or £4300
For our next example, we will draw the equation y = 6 - x on a graph (using just two...

...company chooses to incorporate should be one that most effectively matches expenses with the revenues produced. The method that most select is that of straight-line depreciation, which "spreads the depreciable value evenly over the useful life of an asset." (Horngren, Sundem, Elliott, & Philbrick 2006, p.342) Depreciation schedules reflect how much depreciation will be allocated for each year of the assets useful life. In order to calculate depreciation expense we take the cost of the acquisition minus the estimated residual value divided by the years of estimated useful life. The depreciation schedule using the straight-line method for Balls and Bats, Inc. would be as follows:
Total Acquisition cost= $100,000
Salvage value= $10,000
Estimated useful life= 4 years
Depreciation expense= 100,000 - 10,000 = 22,500
4
D= $22,500 per year
Single-line Depreciation
Balances at End of Year
1 2 3 4
Acquisition cost of
Equipment $ 100,000 $100,000 $100,000 $100,000
Accumulated Depreciation $ 22,500 $ 45,000 $ 67,500 $ 90,000
Net book value $ 77,500 $ 55,000 $ 32,500 $ 10,000
Balance at the end of year 4 equals salvage value of $10,000
Assets that shows a pattern of depreciation that is written off more quickly than regular straight-line method is referred to as accelerated...

...Waiting Lines & Queuing Models
American Military University
Business 312
For my project on other operations research techniques I have decided to research waiting lines and queuing models. My interest in this application stems from my personal dislike for standing in lines and waiting on hold while on the phone. This is virtually my only pet peeve; nothing aggravates me faster than standing in aline or waiting on hold. Like most people I go out of my way to avoid lines, using strategies such as arriving early or visiting during non-peak times. However, before investigating this topic, I had no idea there was a specific science behind the madness.
Queuing models are important applications for predicting congestion in a system. This can encompass everything from a waiting line at pharmacy to traffic flow at a busy intersection. This is important because it can impact businesses in unforeseen ways. Customers may begin to believe that they are wasting their time when they are forced to wait in line for service and continued delays may begin to negatively influence their shopping preferences.
Organizations design their waiting line systems by weighing the consequences of having a customer wait in line, versus the costs of providing more service capacity. Queuing theory provides a variety...

...method? Assume a depreciation rate of twice the straight – line method.
Straightline method =- (cost-residual value) = 25000-5000
10 10
Depreciation per year = 2000
Depreciation after year 2 = 2000+2000=$4000
Book value at year 2 = $25,000-4000=$21,000
Answer: $21,000
16.
Q= Pete’s Warehouse has a market value of $5,000,000. The property in Pete’s area is assessed at 40% of the market value. The tax rate is $105.10 per $1,999 of assessed value. What is Pete’s property tax?
$5,000,000*40%=$2,000,000 assessed value
105.10*1.999=210094.90
$2,000,000/1999*105.10=105,152.576
Answer:Property tax =$ 105,152.58
17.
Q= Jim Smith, who lives in Territory 5 carries 10/20/5 compulsory liability insurance along with optional collision that has a $200 deductible. Jim was at fault in an accident that caused $1,800 damage to the other auto and $600 damage to his own. Also, the courts awarded $13,000 and $8,000 respectively, to the two passengers in the other car for personal injuries. How much does the insurance company pay, and what is Jim’s share of responsibility?
18. -
Q= Jeff Sellers bought 200 shares of Radio Shack stock at $22.35. Eight months later, he sold the stock at $31.76. Assuming a 2% charge, what is the bottom line for Jeff?
Shares bought = 200
Unit cost per share = $22.35
Cost = $200*22.35=$4,470
8 months later sale price = 200*$31.76= $6,352
2%charge =$...

...take a ball as close to the meter stick as possible and measure a) the height dropped and b) the resulting height bounces. Repeat this for three different heights dropped and records all data. Then you will make a graph of the data.
Make at least three more measurements for each of the previous three height dropped levels. Find the average height bounced for each level and record the data and the average values.
Make a new graph of the average height bounced for each level that the ball was dropped . Draw a straightline best fit that includes the origin by considering the general trend of the data points measurement data. Draw that straightline as close as possible to as many data points as you can so that you have about the number of data points on both sides of the straightline.
Compare how well both graph predicts the heights that the ball will bounce for height dropped that were not tried previously. Located an untried height dropped distance on the straightline then use the corresponding height bounced from the graph as a prediction.
Graphing
Feb. 1, 2011
TR
9:30-10:50
Shantia Stringfellow
Physical Science Survey...

...main khote chale gaye.
Door jakar bhi hum door jaa na sakenge,
Kitna royenge hum bata na sakenge,
Gham iska nahi ki aap mil na sakoge,
Dard is baat ka hoga ki hum aapko bhula na sakenge…
apne seene se laga kar rakh mujhe,
mere saare gum dur kar de,
tujhse judaa na ho paau,
itna apne pyar se mujhe choor kar de,
meri nas nas main bas jaaye tera pyar,
main kisi aur ko na dekhu,
itna mujhe majboor kar de
Just loved these lines about LIFE..
When I got enough confidence, the
stage was gone..
When I was sure of losing, I won..
When I needed people the most, they
left me..
When I learnt to dry my tears, I
found a shoulder to cry on..
When I mastered the skill of hating,
Someone started loving me from the
core of one's heart &
While waiting for light for hours when
I fell asleep the sun came out..
That's LIFE! No matter what u plan u
never know what life has planned for
u..
"Success introduces you to the
world..Failure introduces the world to
you.."
Nice Lines By Gulzaar..
Paani se Tasveer kaha banti hai,
khwabo se taqdeer kaha banti hai.
kisi bhi rishte ko sachche Dil se
nibhao, kyunki ye zindagi phir wapas
kaha milti hai ..
Koun kisi se chah kar dur hota hai,
har koi apne halatose majbur hota
hai
hum to bas itna jante hai har rishta
MOTI aur har dost KOHINOOR hota
hai..
Life has never promised you
anything... Nor has Destiny.. But
people...

...Chapter 2
Regression Analysis and Forecasting Models
A forecast is merely a prediction about the future values of data. However, most
extrapolative model forecasts assume that the past is a proxy for the future. That is,
the economic data for the 2012–2020 period will be driven by the same variables as
was the case for the 2000–2011 period, or the 2007–2011 period. There are many
traditional models for forecasting: exponential smoothing, regression, time series,
and composite model forecasts, often involving expert forecasts. Regression analysis is a statistical technique to analyze quantitative data to estimate model
parameters and make forecasts. We introduce the reader to regression analysis in
this chapter.
The horizontal line is called the X-axis and the vertical line the Y-axis. Regression analysis looks for a relationship between the X variable (sometimes called the
“independent” or “explanatory” variable) and the Y variable (the “dependent”
variable).
For example, X might be the aggregate level of personal disposable income in
the United States and Y would represent personal consumption expenditures in the
United States, an example used in Guerard and Schwartz (2007). By looking up
these numbers for a number of years in the past, we can plot points on the graph.
More speciﬁcally, regression analysis seeks to ﬁnd the “line of best...