Tuesday, 7 April 2015

Oil Prices





Oil Prices

For this assignment, I decided to talk about oil prices. First of all, I would like to point out the fact that oil is an essential resource in our economy. Also oil is one of the most important industries in Alberta.
 
Oil Prices Chart

In the following table, we can see the oil prices for the last thirteen months including April. By looking at the data we can see that there is a significant difference between last year and this year price. The highest price was on June 1, 2014 (Month 2) at $105.37 per barrel. The lowest price was on March 1, 2015 (Month 11) at $47.60 per barrel. The average price was $76.49 per barrel.

Month
Date
Price
0
2014-04-01
 $   99.74
1
2014-05-01
 $ 102.71
2
2014-06-01
 $ 105.37
3
2014-07-01
 $   98.17
4
2014-08-01
 $   95.96
5
2014-09-01
 $   91.16
6
2014-10-01
 $   80.26
7
2014-11-01
 $   66.64
8
2014-12-01
 $   54.96
9
2015-01-01
 $   50.07
10
2015-02-01
 $   52.14
11
2015-03-01
 $   47.60
12
2015-04-01
 $   49.53

After analyzing the prices on the table for the indicated months it is obvious that the price has dropped by $50.21 per barrel in the last year. This has a negative impact to the world’s economy because most industries relies on this resource.

Oil Prices Equation

Equation Type: Cubic Regression


Y = 0.1565x^3  +(-2.9026)x^2+ 8.3058x + 98.5614



The graph above shows the cubic function f(x) = 0.1565x^3  +(-2.9026)x^2+ 8.3058x + 98.5614. This function shows the decrease in oil price. We can see that the cubic regression is the one that best describes the data. There were many factors that caused the oil price to drop including supply in terms of output, access to future supply, and oil demand. The graph shows that on April 2014 the price per barrel was about $105.37/Barrel. By the end of 2014 the oil price went down to $54.96/Barrel. On March 2015, the price for oil hit its lowest mark in the past 7 years with $47.60/Barrel. Based on the graph, since the price reached the lowest price point we can predict that the price will bounce back up.


Change in price Chart

The following table is the change in dollars of the oil price for the past 13 months including April 2015. In the data, between July 2014 and January 2015 the price gap was -$55.30. This significant change in price worries the oil industry, mainly because profits will decrease and they might have to cut costs by laying off people, shutting down operations, and cancelling projects.   



Month
Date
Change in $
0
2014-04-01

1
2014-05-01
 $            2.97
2
2014-06-01
 $            2.66
3
2014-07-01
-$           7.20
4
2014-08-01
-$          2.21
5
2014-09-01
-$           4.80
6
2014-10-01
-$        10.90
7
2014-11-01
-$           13.62
8
2014-12-01
-$        11.68
9
2015-01-01
-$           4.89
10
2015-02-01
 $            2.07
11
2015-03-01
-$           4.54
12
2015-04-01
 $            1.93

Change in Dollars

Equation type: Cubic regression

Y = 0.0314x^3+(-0.2529)x^2  +(-1.4142)x + 2.5639


The graph above shows the cubic function f(x) =  0.0314x^3+(-0.2529)x^2  +(-1.4142)x + 2.5639 . After June 2014, there was an oil price constant decline until February 2015 where there was a little increase of $2.07. The price went down again for the month of March 2015, then on the beginning of April the price increased by $1.93. It is expected that the oil price should increase within the upcoming months.   


Pros & Cons of Current Oil Price trend




Pros
  • Due to the low barrel price, gas prices will likely tend to go down.
  • Products such as groceries, goods, etc. will tend to be cheaper to transport due to the low price in oil.
  • Low oil prices also help to lower cost of living.

Cons
  • Lay-offs in the oil industry.
  • Weakens economies relying on the oil industry.
  • Research for new energies started by Oil companies might not have enough profits to grant research. 

Conclusion

After reviewing and doing all the required analysis, we can conclude that oil prices have an impact in our economy and our daily lives. The low oil price has affected Alberta in general negatively. People are losing their jobs, local oil companies are losing profit, and the city’s economy is getting weaker. According to the graphs and the analysis made, we can deduct that they oil price will go up hopefully by the end of the year.

References






Thursday, 2 April 2015

Population of China


China

World's Most Populated Country



China's population as of 2015 is 1.4 billion making it the world's most populous country. Using provided data demonstrating growth of China's population since 1960 we will select best equation which describes population growth until present day.

Table 1. Raw Data

Line 1 Years after 1960
Line 2 Population in Millions
0
667
5
715
10
818
15
916
20
981
25
1051
30
1135
35
1204
40
1262
45
1303
50
1337
55
1370


Provided data was loaded into a graphing calculator to perform regression analysis. Four different equation types were tested:

  1. Cubic regression
  2. Quadratic regression
  3. Exponential regression
  4. Linear regression

1. Cubic Regression Graph





Cubic Regression Equation:

y = ax3 + bx2 + cx + d
y = -0.0024107744x3 + 0.0845532246x2 + 15.53260813x + 656.3589744


Y-intercept: 656
Number of X-intercepts: 1


2. Quadratic Regression Graph





Quadratic Regression Equation

y = ax2 + bx + c
y = -0.1143356643x2 + 19.72132867x + 641.4423077

Y-intercept: 641
Number of X-intercepts: 2



3. Exponential Regression Graph





Exponential Regression Equation

y = abx
y = 717.0066508 ◦ 1.013448475x

Y-intercept: 717
Number of X-intercepts: 0




4. Linear Regression Graph




Linear Regression Graph

y = ax + b
y = 13.43286713x + 693.8461538

Y-intercept: 694
Number of X-intercepts: 1


Table 2: Pros and Cons



Pros
Cons
Cubic
Fits most data points
Does not fit 2 data points
Quadratic
Fits 2nd most data points
Does not fit 4 data points
Exponential
None
Only fits 2 data points
Linear
None
Only fits 2 data points



China's Future Population

Predictions and Extrapolations


This data collected from a Wikipedia source shows that China's population is expected to begin declining by 2040.

Table 3: China's future population - data from Wikipedia source


Line 1 Year
Line 2 Population in Millions
2020
1387
2030
1393
2040
1360
2050
1295
2060
1211
2070
1125
2080
1048
2090
984
2100
941


Previously found Cubic Regression equation was applied to same Year points - 60 to 120 year after 1960. Resulting Population Data points can be found in table below.


Table 4: China's future population as predicted by my Cubic Regression equation


Line 1 Years after 1960
Line 2 Population in Millions
60 (2020)
1372
70 (2030)
1331
80 (2040)
1206
90 (2050)
982
100 (2060)
644
110 (2070)
179
120 (2080)
- 428
130 (2090)
- 1192
140 (2100)
- 2127


This data predicts a drastic decline of China's future population which does not fit statistical prediction obtained from Wikipedia source.

Next I applied previously found Quadratic Regression equation in the same manner. Resulting Population Data points can be found in table below:

Table 5: China's future population as predicted by my Quadratic Regression equation


Line 1 Years after 1960
Line 2 Population in Millions
60 (2020)
1413
70 (2030)
1462
80 (2040)
1487
90 (2050)
1490
100 (2060)
1470
110 (2070)
1427
120 (2080)
1362
130 (2090)
1273
140 (2100)
1161


This data indicates that China's future population will continue to increase until a peak in 2050 after which it will start to decline again. This is a reasonable prediction.






Conclusions

In years from 1960 to 2015 Chinese population has been increasing. According to various predictions China's population growth will slow down and reverse in future. Results of my Quadratic Regression (see Table 5) equation predicts this trend to reverse in 2050s with China's population begging to decline slowly. Results of my Cubic Regression (see Table 4) predicts China's population begging to sharply decline in 2020s. Lastly data obtained from Wikipedia source indicates that statistics predicts China's population growth to reverse in 2030s.

References


  • China's population growth (Table 1): http://countrymeters.info/en/china See table: China population history
  • China's future population (Table 3): http://en.wikipedia.org/wiki/Demographics_of_China See: Population Projection, United Nations 2010 estimate.