Calgary Home trends

Impaired Driving Incidents in Canada

World Population Growth

Calgary Population

Calgary Population

I chose this particular project because I find it is interesting to see our city’s increasing overall
population. My goal is to demonstrate the increase of population in Calgary.

Year (L1)	Population (L2)	Difference (compared to previous year)
1990 (0)	692,885
1991 (1)	708,593	15,708
1992 (2)	717,133	8,540
1993 (3)	727,719	10,586
1994	738,184	10,465
1995	749,073	10,889
1996	767,059	17,986
1997	790,498	23,439
1998	819,334	28,836
1999	842,388	23,054
2000	860,749	18,361
2001	876,519	15,770
2002	904,987	28,468
2003	922,315	17,328
2004	933,495	11,180
2005	956,078	22,583
2006	991,759	35,681
2007	1,019,942	28,183
2008	1,042,892	22,950
2009	1,065,455	22,563
2010	1,071,515	6,060
2011	1,090,936	19,421
2012	1,120,225	29,289
2013	1,156,686	36,461
2014	1,195,194	38,508
2015 – estimation	1,211,602	16,408
2016 – estimation	1,239,847	28,155
2017 - estimation	1,268,751	28904

Data acquired from here (http://www.calgary.ca/_layouts/cocis/DirectDownload.aspx?target=http%3a%2f%2fwww.calgary.ca%2fCA%2fcity-clerks%2fDocuments%2fElection-and-information-services%2fCensus2014%2fFinal%202014%20Census%20Results%20book.pdf&noredirect=1&sf=1) at page 20. 

I began with year 1990 and ended with year 2014. Since we are still in year 2015, the population of Calgary on that year will be estimated. The population of Calgary reached one million in year 2007. There were some annexations in year 1996, 2005, and 2008. The annexation slowed down the population growth slightly.

Exponential Regression Graph

After I gathered the data from the link above, I created an exponential regression graph. The exponential regression is as follows:

Y = 681007.50380284(1.0233125727547)^x

This graph is the most accurate compared to other graphs below. The plot points are mostly touching the line. The most beautiful graph as well…

Calgary’s population continues to expand. By using table of values and the regression provided above, in year 2017, Calgary are predicted to have about 1,268,751 residents. In year 2025, 1,525,608 citizens are estimated to live in the city.

Linear Regression Graph

Y = 659736.50153846+20894.001538462x

Linear graph is not ideal because the scatter dots are way off from linear regression.

Quadratic Regression Graph

Y = 266.00613154961x^2+14509.854381271x+684209.06564102

Much better than linear regression graph. However, I still think that scatter dots still are off a bit compared to exponential graph.

Cubic Graph

Y = -10.553894012575x^3+645.94631600231x^2+10936.305868613x+690617.39008546

This graph is not too bad, either. However, I don't think that Calgary's population are decreasing anytime soon, unless some factors caused the population to decrease. It is good idea to use this graph if the population is estimated to decrease after a certain number of years.

Conclusion 

The exponential regression graph is the most accurate graph, followed closely by quadratic regression graph. Cubic regression graph is best suited to project the population decrease. Linear regression graph, however, is the most inaccurate graph above all.

Reference(s)/source(s)

• For Calgary's population growth histor:y http://www.calgary.ca/_layouts/cocis/DirectDownload.aspx?target=http%3a%2f%2fwww.calgary.ca%2fCA%2fcity-clerks%2fDocuments%2fElection-and-information-services%2fCensus2014%2fFinal%202014%20Census%20Results%20book.pdf&noredirect=1&sf=1 at page 20
• Image of Calgary downtown: http://www.getdown.ca/wp-content/uploads/2013/01/calgary-pano2.jpeg

the price of oil

PLEASE CLICK PICTURE TO ENLARGE

UK Population

Candian population

 As for the Google reference, Google Canada Birth/Death Rate, this link will go to the fatality graph and the birth rate will be nine columns above the one you'll be sent to. Also, the reason the Canadian population is increasing by a huge factor, is there are a lot of off-shore permanent, temporary, workers and un-documented visitors give or take. And that would have taken more time that would have needed to give a complete view. For more in-depth information about this presentation feel free to ask me.

Milk Prices

MILK PRICES

Expert Analysis

According to Farmgate milk prices are starting to stabilize after one of the biggest price inflations in years. Thanks to a recent drop in the economy milk will now stabilize to around the $3.00 range.

Data

  

Exponential: y = a (b)^x 

a = 2.192852

b = 1.053050453

Linear: y = a + bx 

a = 166642.70364

b = -8.268 

Prediction

My opinion is that Milk prices will rise and fall with how our agricultural economy is going. If the prices rise then fall like from 2011 to 2014 its doing not so well. If they stabilize like the experts have predicted it will be doing well. but we do not want a inflation rise like in 2005 to 2010. personally i believe it will stabilize and be ok for the next five years.

References

1.) http://inflationdata.com/articles/2013/03/21/food-price-inflation-1913/

2.) http://www.thepeoplehistory.com/pricebasket.html

3.) http://www.thebeefsite.com/news/47743/fao-food-price-index-drops-further-in-march-2015/

Ashley's USA Population Growth Project

US population growth between 1900-2000

1900- 76,212,168

1910-92,228,496

1920-106,021,537

1930-123,202,624

1940-132,164,569

1950-151,325,798

1960-179,323,175

1970-203,211,926

1980-226,545,805

1990-248,709,873

2000-281,421,906
Cubic Graph

 

Cubic Regression equation: ax^3+bx^2+cx+d

a=12.711

b=-65006.145

c=110520945

d=-6.242x10^10

Pros and cons Of Cubic Graph:

- Con: The line from the cubic function goes just above the ploted points.

 

Liner Graph

 

Liner Equation:

2018840.193x-3771250387=y

Pros and Cons of Liner Graph:

- Con: Not all the plot point are on the Liner equations, however more ploted point land on the line compared to any other function.

Quadratic Graph

 

Quadratic equation:

9352.435967x^2-34455660.08x+3.178*10^10=y

Pros and Cons:

Con: the line from the quadratic function misses most of the plot points, if not all of them.

Exponetial Graph

Exponetial Function:

.0021946618*1.012885618^x=y

Pros and Cons:

Pro: Connects to plot point well, better than the other three functions do, and when the line does not connect with a plot point it is relatively close to it.

Conclusion:

The exponetial functions if the function that connects with the ploted point the best, and is the best equation to use for this situation.

 

Resources:

http://www.demographia.com/db-state1900.htm
http://fooplot.com/#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--