## Monday, June 11, 2012

### Ch3 Math Finance Part 2

Ch3 Project Voice Thread Link by Thanh, Min Ying and Lan: Future Value of Annuity

### Interest Compound Continuously

Interest Compounded Continuously

## Friday, June 8, 2012

### Chapter 2 Project

According to zillow.com, the property taxes of my grandparent's house is \$4,778 as of June,2011. The tax was \$4,524  when my family moved in during October 2008.
• To find the model, you go to STAT ---> EDIT---> Plug in X & Y values. The X values would be from 2004-2011 but I type in #1-8 represent years. The Y values would be the property taxes. Then I found Quart Reg fits the best after I tried all four Reg ( Linear, Quad, Cubic, Quart) because R^2 is closet to 1.
• My house property taxes will not be \$100,000 because the maximum in \$4,800.
• The prediction can be changed if the value of the house increases.

### Chapter 2 Project

From Zillow.com, I found out that the house I live in was worth 332,000 in October, 2009, which was when I moved in. To find out the best regression model, i use the data I collected on Zillow and plugged it into my calculator. I went through all the different regression models, like cubic, linear, quadratic, etc. I found out that quartic regression is the most accurate.
The equation is 1.38x^4-24.588x^3+121.630x^2-107.510x+270.720.
In the future, the house will never drop to 100,000 because the minimum value is \$114,500.
The prediction can change if the economy drops even further, which means houses may go down in price.

## Thursday, June 7, 2012

### Ch. 2 Project

From the data that we get from Zillow.com, I find out that the house I live in worth 546,000 at Jun, 2004. To find the best fit model to predict the price of the house at certain time period. So I put all my data into the calculator by pushing STATS and punching in the numbers, then I press STATS again to try out the buttons that make the best formula, like linear regression, Quadratic regression, Cubic regression, Quartic regression, and I found out that Cubic regression is the best fit equation because r^2 = .9427, so this is the only equation that is closest to 1. So the equation is 1.55x^3 + -33.4x^2 +189.82x + 246.6 . In the future, this house will never drop to 100,000 dollars because the minimum value is 357.23k. This equation can be change when  the government change or tax change , and other factors.

### Chapter 2 Project

For the Chapter 2 project, I learned that my house is currently worth \$180,200 on zillow.com but a few years ago, before I moved in, my house was worth up to \$519,000.
I then used my calculator to find a linear regression formula to calculate how long or when my house will reach a certain price and I learned that in 2017, my house can reach \$100,000. Some ways that can change my prediction would be if this community becomes richer and safer, the price of the house would definitely increase.

### Chapter 2 Project

Using the information from zillow.com, my house is worth \$244,400 today, and was worth \$368,000 in 2004 when we first moved in. To create this model, you need to enter the data to the STAT table. X is equal to the years starting from 2002-2011 (represented as 1-10 respectively). Y is equal to the value of the house in thousands during each year. After entering the data and checking through the 4 regression models (linear, quad, cubic,and quart), i found that the quartic regression had fit my model the best with r^2=.8265742, which was closest than all the other regressions models. The quartic regression is Y=1.197898X^4 -25.626748X^3 +168.101253X^2 -345.119463X +573.583334. the value of this house will not reach \$100k. The minimum value is \$251k. My prediction can change if the house gets damaged or if the value of the land goes down, causing the price of the house to drop.

### Chapter 2 project

itled.png" imageanchor="1" style=""> href="http://4.bp.blogspot.com/-8P8UokR7-F8/T9GJReWLQII/AAAAAAAAAEQ/QmoFKQuJ1gw/s1600/Unt According to Zillow, the house my family and I moved into was worth \$509,000 during the year 2004. I used my calculator to find the model by entering my X and Y values into the STATS table. My X are the years and my Y are the value of the house. My linear regression equation is y=54466.4+-26.72x with the Rate of .5319350662. My house will reach the value of \$100,000 until the year 2023. The yearly depreciation of the house is \$17,000. Some things that could happen that could change the value is the increase in violence or the amount of people moving in and out of the neighborhood.

### Ch2 Project

Zillow.com explains that the house I currently live in is worth \$225,100. In 1996, when we moved in it was worth about \$484,000. To find the graph I used X for the number of years and Y for the value. Then I used the quartic regression, because r2 =0.96 which was the closest compared to other regression models. The quartic regression is y= 435.17x4-3491771.47x3+1.05x2-7.04, the house could reach a value of \$100,000 in 2027. The value of the house could change depending on various factors, such as the color of the paint the other houses in the neighborhood has.

### Ch 2 Project

Zillow.com has shown that my house \$513k when we moved in on May, 2010. The price of it now (June 2012) is worth around \$420k. To find what kind of regression model I used for the that, I plugged the X and Y values into the stats table. Then I clicked Stat > Calc > Lin Reg / Quad Reg / Cubic Reg / Cubic Reg. Then I entered one of those regression and clicked Y1 and Y2 and entered again. Then i can see the numbers for each regression. To see which model was best, I chose the one that was closest to 1 for R^2 and it was the Quartic Regression, so I chose that as my equation.

My house value cannot reach \$100k as the minimum house price was \$379k.
My prediction can be changed if prices need to be dropped so it becomes more affordable to the general population.

### Ch2 Project

According to Zillows, my house is now worth \$,242,300 as of June 03, 2012. I moved into this house around June, 2008 and it was worth around \$484,000. To find the regression equation for the data, i plugged them into a graphing calculator and found that the Quartic Regression best fits the data because the coefficient of determination was the highest (r^2 = 0.7419).
The Quartic Regression if got from my data is y = 0.4659x^4 - 10.42094x^3 + 63.9653x^2 - 69.3296 + 353.4242.
My house's value will never reach \$100,000 because the minimum value of my house is \$275,950 and my prediction can be changed if my neighborhood starts to improve and the prices of nearby houses rise so my house would probably rise along with them.

### Chapter 2 Project

Using the information from zillow.com, the house I live in is now worth \$282,000 when I first moved in at March, 2012 . To find the model, I type in #1-10 which represent years 2002-2011; then I put the past values from 2002 through 2011 in thousands of dollars as Y. Next, i tried all four regression models(Linear, Quadratic, Cubic, Quartic) to see which model fits the best, and it turns out that Quartic regression is most accurate because the r^2 is 0.9466850693, which is closer to 1 out of the other three. My Quartic regression equation is: y= .7152x^4 + -15.47^3 + 98.25x^2 + -165.46x + 393.75. This house will never reach the value of \$100,000 because the minimum value is \$247,000.

The prediction of the quartic model can change if the economy, neighborhood, or conditions of the house are bad. This would to lead to downfall in prices.

### Chapter 2

According to the data provided in zillow the house that I moved into in 2002 was worth \$230,000. To find the model you go to stat then edit then plug in your x values and your y values. The x values would be from 2002-2011 then the y values are from \$230,000-\$533,000. The quadratic regression was the best because the R was closer to 1. The quadratic regression model was Y = .47115384615439x^4+ -3779.3265345809x^3+11368322.761959x^2+-15198295709.015x+7619451108445.9. The house that I live in will never be worth 100,000 because the minimum value is 230,000. A factor that can affect the value of my house would be if more people move into the area or if there is another recession.

## Wednesday, June 6, 2012

### Chapter 2 Project.

According to the data provided from Zillow, the house I reside now has the worth of \$499,000 when I moved in during October of 2011. To be able to find the model, I'll first have to enter it on the STATS table. My x is years: 2003-2012, while my y is the value of the house: \$358K-\$467K. My linear regression equation is: y=-2.193.939394x+4840533.333. R=.0809874044. My house would not reach the value of \$100K until 2,161 years. I moved to my house for nearly only a year, and the yearly appreciation would be \$32,000. If more people moved to my neighborhood, then the house prices/value would possibly decrease.