Cordeiro,+M

 __INCOME VS CARBON FOOTPRINT__   a) BRAINSTORMING  Environmental     Sports     Other        b) PROPOSED QUESTION Does a North American's yearly income have an effect on their overall carbon footprint from the year 2000 to the present?
 * __ Stage One __**
 * Carbon footprint vs. income
 * greater the income the more products that will hurt the environment
 * lower the income the less products that will hurt the environment
 * greater the income the more environmentally friendly products purchased?
 * price of environmentally friendly products and availability is a huge factor
 * Carbon footprint vs. country of residence
 * the more developed the country, the greater their footprint
 * use more resources due to greater amount of opportunities in country
 * advanced technology
 * Carbon footprint vs. religion
 * belief in different ways of life
 * Involvement in sports vs. income
 * the wealthier the family, the more or less involvment they will have in sports
 * children and the opportunities they are given by their parents to participate in sports
 * cost of playing each individual sport
 * Athletic ability vs. country of residence
 * availability of coaches?
 * specific sports that can be played in certain countries and not others
 * ex. skiing in Australia?
 * Crime rates vs. development of community
 * greater or less of a crime rate in different communities
 * wealthier vs. poorer communitie
 * Youth crime vs. annual income
 * amount of crime depending on a person's income
 * greater income = less crime OR greater income = more crime?
 * Carbon Footprint vs. Income

Independent Variable: Income Dependent Variable: Carbon Footprint c) HYPOTHESIS    I believe that a North American's yearly income will have a direct effect on their environmental footprint because those who have more money are going to spend more on things that will increase their footprint and vise versa, especially in recent years with the vast increase in technology. For example: electronics, cars, vacations etc.

__//** INTRODUCTION  **//__  Does a North American's yearly income have an affect on their overall carbon footprint from the year 2000 to the present?
 * __ Stage Two __**

A carbon footprint is a measure of the impact our activities have on the environment and climate change. It relates to the amount of greenhouse gases produced in our day-to-day lives through burning fossil fuels for electricity, heating, transportation etc. (it is measured in hectares per capita throughout my project) Note: Each person on Earth should only have a carbon footprint of 2.1 hectares per person or else they are using up more resources than the Earth can renew.

 This chart shows what percentage of each activity is used to measure a persons carbon footprint.

I believe that a North American's yearly income will have a direct affect on their carbon footprint because those who have more money are going to spend more on things that will increase their footprint (especially in recent years with the vast increase in technology). However, someone who has less money, is more likely to have a smaller carbon footprint since they have less money to spend on luxury goods that would increase their footprint. For example: electronics, cars, vacations, etc.

I  chose to do this topic because I feel that the   ** dependent **    variable of a  //  carbon     footprint  //  i   s directly related to the    ** independent **    variable of a   persons yearly income  . I feel as though it should be the other way around; that those who have more money should be able to buy products that are more efficient and therefore they should have a smaller footprint. However I don’t believe that that is the case because in todays world, those who have more money tend to spend more on things like larger cars, vacations and other electronics that only increase their footprint. Those with higher incomes also live in larger homes with more technological advances that boost their carbon footprint. 

ANALYSIS OF DATA

The chart above simply shows that a person in the lower income decile (or someone who brings in a very low income) is more likely to have a smaller "ecological" or carbon footprint than someone who is in a higher income decile (or someone who brings in a much higher income). This chart is on average throughout Canada. <span style="font-family: Tahoma,Geneva,sans-serif; font-size: 120%;"> <span style="font-family: Tahoma,Geneva,sans-serif;"> The graph above illustrates the average annual income by province in Canada in the years 2006 and 2007. You can see that Alberta has the highest income level in Canada at $75,300 and New Brunswick has the lowest at $50,600. On average Canada overall has a yearly income level of around $61,800.

If you look at the following table showing Alberta's total expenditures, as well as the table showing New Brunswick's total expenditures, you will see that Alberta's total expenditures exceed New Brunswick's by $9,844. So this shows that Alberta households spend, on average, $9,844 more dollars than New Brunswick households per year. I chose to study these two provinces because they are total opposites; Alberta had one of the highest average incomes while New Brunswick is the lowest. <span style="font-family: Verdana,Arial,Helvetica,sans-serif; font-size: 13px; line-height: normal;"> Last modified: 2009-12-21. ||
 * Average household expenditures, by province and territory <span style="display: block; font-family: Verdana,Arial,Helvetica,sans-serif; text-align: center;">(Saskatchewan, Alberta) ** 2008
 * ~  ||||~ **Sask.** ||||~ **Alta.** ||
 * ~  ||~ **Average expenditure per household** ||~ **Households reporting expenditures** ||~ **Average expenditure per household** ||~ **Households reporting expenditures** ||
 * < **Total expenditures** ||> **68,279** ||> **100.0** ||> **86,911** ||> **100.0** ||
 * < Total current consumption ||> 48,810 ||> 100.0 ||> 60,643 ||> 100.0 ||
 * < Food ||> 6,301 ||> 100.0 ||> 7,713 ||> 100.0 ||
 * < Shelter ||> 11,727 ||> 99.8 ||> 16,525 ||> 100.0 ||
 * < Household operation ||> 3,177 ||> 100.0 ||> 43,977 ||> 100.0 ||
 * < Household furnishings and equipment ||> 2,097 ||> 95.8 ||> 2,328 ||> 94.3 ||
 * < Clothing ||> 2,612 ||> 98.6 ||> 3,301 ||> 99.6 ||
 * < Transportation ||> 10,945 ||> 98.6 ||> 12,182 ||> 99.3 ||
 * < Health care ||> 1,752 ||> 99.4 ||> 2,492 ||> 98.5 ||
 * < Personal care ||> 1,111 ||> 99.6 ||> 1,380 ||> 99.8 ||
 * < Recreation ||> 4,720 ||> 97.5 ||> 5,522 ||> 98.0 ||
 * < Reading materials and other printed matter ||> 245 ||> 78.5 ||> 290 ||> 77.1 ||
 * < Education ||> 963 ||> 34.1 ||> 1,300 ||> 41.3 ||
 * < Tobacco products and alcoholic beverages ||> 1,515 ||> 84.2 ||> 1,867 ||> 82.5 ||
 * < Games of chance (net amount) ||> 360 ||> 75.7 ||> 305 ||> 64.2 ||
 * < Miscellaneous ||> 1,286 ||> 94.0 ||> 1,462 ||> 94.0 ||
 * < Personal income taxes ||> 13,057 ||> 90.7 ||> 19,052 ||> 94.3 ||
 * < Personal insurance payments and pension contributions ||> 4,344 ||> 83.2 ||> 4,579 ||> 89.5 ||
 * < Gifts of money and contributions ||> 2,067 ||> 80.1 ||> 2,636 ||> 77.0 ||
 * **Source:** Statistics Canada, CANSIM, table (for fee) [|203-0001].
 * **Source:** Statistics Canada, CANSIM, table (for fee) [|203-0001].

Last modified: 2009-12-21. ||
 * Average household expenditures, by province and territory <span style="display: block; font-family: Verdana,Arial,Helvetica,sans-serif; text-align: center;">(New Brunswick, Quebec) ** 2008
 * ~  ||||~ **N.B.** ||||~ **Que.** ||
 * ~  ||~ **Average expenditure per household** ||~ **Households reporting expenditures** ||~ **Average expenditure per household** ||~ **Households reporting expenditures** ||
 * < **Total expenditures** ||> **58,435** ||> **100.0** ||> **60,478** ||> **100.0** ||
 * < Total current consumption ||> 43,073 ||> 100.0 ||> 43,108 ||> 100.0 ||
 * < Food ||> 6,548 ||> 100.0 ||> 7,396 ||> 100.0 ||
 * < Shelter ||> 10,073 ||> 99.8 ||> 11,169 ||> 99.9 ||
 * < Household operation ||> 3,163 ||> 99.9 ||> 2,653 ||> 99.9 ||
 * < Household furnishings and equipment ||> 1,623 ||> 93.8 ||> 1,578 ||> 91.2 ||
 * < Clothing ||> 2,071 ||> 98.4 ||> 2,368 ||> 98.7 ||
 * < Transportation ||> 9,925 ||> 97.2 ||> 7,997 ||> 98.2 ||
 * < Health care ||> 1,980 ||> 98.4 ||> 2,084 ||> 98.0 ||
 * < Personal care ||> 978 ||> 99.6 ||> 1,078 ||> 100.0 ||
 * < Recreation ||> 3,407 ||> 97.9 ||> 3,304 ||> 96.2 ||
 * < Reading materials and other printed matter ||> 228 ||> 74.8 ||> 231 ||> 71.5 ||
 * < Education ||> 713 ||> 25.9 ||> 641 ||> 35.3 ||
 * < Tobacco products and alcoholic beverages ||> 1,344 ||> 79.7 ||> 1,416 ||> 87.3 ||
 * < Games of chance (net amount) ||> 255 ||> 72.2 ||> 228 ||> 71.5 ||
 * < Miscellaneous ||> 764 ||> 92.4 ||> 965 ||> 92.1 ||
 * < Personal income taxes ||> 10,419 ||> 90.4 ||> 12,423 ||> 91.2 ||
 * < Personal insurance payments and pension contributions ||> 3,655 ||> 85.2 ||> 3,848 ||> 83.6 ||
 * < Gifts of money and contributions ||> 1,290 ||> 82.5 ||> 1,100 ||> 65.4 ||
 * **Source:** Statistics Canada, CANSIM, table (for fee) [|203-0001].
 * **Source:** Statistics Canada, CANSIM, table (for fee) [|203-0001].

I made this chart below from statistics i found off of StatsCanada. It proves that, in Canada, the higher income decile a person is in, the greater their ecological footprint will be. This chart also outlines the amount spent on food, housing, mobility or transportation, goods and services.
 * || **Poorest 10%** || **Decile 2** || **Decile 3** || **Decile 4** || **Decile 5** || **Decile 6** || **Decile 7** || **Decile 8** || **Decile 9** || **Richest 10%** ||
 * Food || 2.06 || 2.15 || 2.14 || 2.14 || 2.14 || 2.16 || 2.15 || 2.16 || 2.13 || 2.24 ||
 * Housing || 1.51 || 1.82 || 1.79 || 1.73 || 1.88 || 1.98 || 2.06 || 2.19 || 2.31 || 3.40 ||
 * Mobility || 0.36 || 0.62 || 0.88 || 1.04 || 1.20 || 1.43 || 1.55 || 1.74 || 2.17 || 3.23 ||
 * Goods || 0.56 || 0.74 || 0.82 || 0.85 || 0.93 || 1.00 || 1.09 || 1.16 || 1.33 || 2.11 ||
 * Services || 0.55 || 0.68 || 0.71 || 0.74 || 0.79 || 0.82 || 0.83 || 0.89 || 0.95 || 1.48 ||
 * Size Of Ecological Footprint (hectares per capita) || 5.03 || 5.66 || 6.34 || 6.48 || 6.93 || 7.36 || 7.67 || 8.12 || 8.87 || 12.42 ||

The richest 10% of Canadian households has an average ecological footprint of 12.4 hectares per capita. This means that their ecological footprint is 66% higher than the average ecological footprint among all Canadians. And the gap between the ecological footprint of the 10% richest Canadians and the 10% poorest is nearly two and a half times more!

The graph below was created from the information in the chart above. It shows what each income level spends on average on items such as food, housing, mobility, goods and services. It also outlines each income levels overall carbon footprint.

The graph below illustrates the chart above. It shows the carbon footprint by income level for the 10 pieces of data that I am studying. It is a scatterplot that shows that my data has a strong, positive correlation.
 * **X** || **Y** || **X²** || **Y²** || **XY** ||
 * 11,531 || 5.03 || 132,963,961 || 25.3009 || 58,000.93 ||
 * 19,710 || 5.66 || 388,484,100 || 32.0356 || 111,558.6 ||
 * 26,901 || 6.34 || 723,663,801 || 40.1956 || 170,552.34 ||
 * 33,867 || 6.48 || 1,146,973,689 || 41.9904 || 219,458.16 ||
 * 41,113 || 6.93 || 1,690,278,769 || 48.0249 || 284,913.09 ||
 * 48,810 || 7.36 || 2,382,416,100 || 54.1696 || 359,241.6 ||
 * 57,732 || 7.67 || 3,332,983,824 || 58.8289 || 442,804.44 ||
 * 68,804 || 8.12 || 4,733,990,416 || 65.9344 || 558,688.48 ||
 * 85,533 || 8.87 || 7,315,894,089 || 78.6769 || 758,677.71 ||
 * 155,845 || 12.42 || 24,287,664,025 || 154.2564 || 1,935,594.9 ||
 * =549,846 || =74.88 || =46,135,312,774 || =599.4136 || =4,899,490.25 ||



**__ CALCULATIONS

ONE VARIABLE CALCULATIONS __**

Mean= 7.5 Therefore the mean value is a carbon footprint on 7.5. Median=7.145 Therefore the median value is 7.145
 * **Carbon Footprint** || **Midpoint** || **Frequency** || **Cumulative Frequency** || **fimi** ||
 * 5-6 || 5.50 || 2 || 2 || 11 ||
 * 6-7 || 6.50 || 3 || 5 || 19.5 ||
 * 7-8 || 7.50 || 2 || 7 || 15 ||
 * 8-9 || 8.50 || 2 || 9 || 17 ||
 * 9-10 || 9.50 || 0 || 9 || 0 ||
 * 10-11 || 10.50 || 0 || 9 || 0 ||
 * 11-12 || 11.50 || 0 || 9 || 0 ||
 * 12-13 || 12.50 || 1 || 10 || 12.5 ||
 * ||  || **10** || **60** || **75** ||

Standard Deviation
 * **Carbon Footprint** || **Midpoint** || **Frequency** || **Cumulative Frequency** || **fimi** || **mi-u** || **(mi-u)2** || **fi(mi-u)2** ||
 * 5-6 || 5.50 || 2 || 2 || 11 || -2 || 4 || 8 ||
 * 6-7 || 6.50 || 3 || 5 || 19.5 || -1 || 1 || 3 ||
 * 7-8 || 7.50 || 2 || 7 || 15 || 0 || 0 || 0 ||
 * 8-9 || 8.50 || 2 || 9 || 17 || 1 || 1 || 2 ||
 * 9-10 || 9.50 || 0 || 9 || 0 || 2 || 4 || 0 ||
 * 10-11 || 10.50 || 0 || 9 || 0 || 3 || 9 || 0 ||
 * 11-12 || 11.50 || 0 || 9 || 0 || 4 || 16 || 0 ||
 * 12-13 || 12.50 || 1 || 10 || 12.5 || 5 || 25 || 25 ||
 * ||  || **10** || **60** || **75** ||   ||   || **38** ||

= 1.94935886896179 Therefore the standard deviation is approximately 1.949

__**TWO VARIABLE CALCULATIONS**__
 * **X** || **Y** || **X²** || **Y²** || **XY** ||
 * 11,531 || 5.03 || 132,963,961 || 25.3009 || 58,000.93 ||
 * 19,710 || 5.66 || 388,484,100 || 32.0356 || 111,558.6 ||
 * 26,901 || 6.34 || 723,663,801 || 40.1956 || 170,552.34 ||
 * 33,867 || 6.48 || 1,146,973,689 || 41.9904 || 219,458.16 ||
 * 41,113 || 6.93 || 1,690,278,769 || 48.0249 || 284,913.09 ||
 * 48,810 || 7.36 || 2,382,416,100 || 54.1696 || 359,241.6 ||
 * 57,732 || 7.67 || 3,332,983,824 || 58.8289 || 442,804.44 ||
 * 68,804 || 8.12 || 4,733,990,416 || 65.9344 || 558,688.48 ||
 * 85,533 || 8.87 || 7,315,894,089 || 78.6769 || 758,677.71 ||
 * 155,845 || 12.42 || 24,287,664,025 || 154.2564 || 1,935,594.9 ||
 * =549,846 || =74.88 || =46,135,312,774 || =599.4136 || =4,899,490.25 ||

__** Line Of Best Fit **__

y=ax+b


 * a=0.000049 **
 * b=4.79 **

THEREFORE the equation of the line of best fit is ** y=0.000049x+4.79 **


 * __Correlation Coefficient__ **

r=__n(Exy)-(Ex)(Ey)__ [nEx²-(Ex)²] [nEy²-(Ey)²] (square root of value)


 * r=0.25 **

Since the correlation coefficient is 0.25, this means that there is a weak, positive linear correlation. However the outlier in my data may have skewed my results.

The following chart and calculations show the correlation coefficient **without the outlier** in order to make the calculations more accurate.


 * **X** || **Y** || **X²** || **Y²** || **XY** ||
 * 11,531 || 5.03 || 132,963,961 || 25.3009 || 58,000.93 ||
 * 19,710 || 5.66 || 388,484,100 || 32.0356 || 111,558.6 ||
 * 26,901 || 6.34 || 723,663,801 || 40.1956 || 170,552.34 ||
 * 33,867 || 6.48 || 1,146,973,689 || 41.9904 || 219,458.16 ||
 * 41,113 || 6.93 || 1,690,278,769 || 48.0249 || 284,913.09 ||
 * 48,810 || 7.36 || 2,382,416,100 || 54.1696 || 359,241.6 ||
 * 57,732 || 7.67 || 3,332,983,824 || 58.8289 || 442,804.44 ||
 * 68,804 || 8.12 || 4,733,990,416 || 65.9344 || 558,688.48 ||
 * 85,533 || 8.87 || 7,315,894,089 || 78.6769 || 758,677.71 ||
 * =394,001 || =62.46 || =21,847,648,749 || =445.1572 || =2,963,895.35 ||

r= __ n(Exy)-(Ex)(Ey) __ [nEx²-(Ex)²] [nEy²-(Ey)²] (square root of value)
 * r=0.16

Since the correlation coefficient is 0.16 without the outlier, this means that there is a weak, positive correlation meaning that my x value of income does not directly correlate with my y value of carbon footprint. ** <span style="color: #1a1a18; font-family: Times,helvetica,sans-serif;"> ** One Variable Data  **  : I will look at the yearly income of individuals and where they live

** Two Variable Data  **  : I will at the yearly income of individuals (indepenent variable) in relation to their carbon footprint (dependent variable).


 * __ Sampling Bias __ **

** Non Response Bias  **  : This bias occurs when participants choose to not participate in the survey. The income of citizens is determined mainly from a survey that is sent out in the mail and is requested to be filled out. There will be some citizens who choose not to participate in the survey and therefore skew the results. These citizen who either do not participate or who's surveys are lost in the mail, will not be calculated in the survey and therefore will alter what the average income is said to be for that country.

** Response Bias  **  : This bias occurs when participants do fill out the survey, however they give false or misleading answer in order to purposely alter the results of the survey. Some citizens may give misleading answers because they are embarassed or simply because they want to alter the results of the survey. There is no way of telling whether or not a person is telling the truth with their answers, and therefore this type of sampling bias may be relevant.

** __ Sampling Techniques __ **

** Voluntary Response Sample  **  : A survey would be sent out to determine what a person's yearly income is and how much of this income they spend on such things as electronics, vacations, cars etc. The questions would reflect how much a person spends on things that are going to increase their carbon footprint, as well as what their income is. This survey could be sent out in the mail and asked to be mailed back to a specific address.


 * Stratified Sample: ** The strata for this sample would be members from each of the different income decile groups. So there would be the same proportion of members from each different group in order to reduce the bias in the survey. This would give a good idea as to what each different income decile consumes and spends on things that will increase their carbon footprint, and see what each deciles average carbon footprint is.

**__ Conclusion __

From my data I have concluded that my hypothesis was correct, and the greater a persons income, their carbon footprint will also be greater. I have statistics that show that those with a higher income, or who fall into a higher income decile, tend to spend more on goods such a food, housing, vacations, transportation etc. This may be because of the many factors that go into a high carbon footprint. Transportation for example is a leading cause in increasing a persons carbon footprint and the wealthier a person is, the more money they will have to travel and purchase larger vehicles. You can see from the first chart in my introduction, that the main sections that calculating a carbon footprint relies on is recreation/leisure, share of public services, home (oil, coal etc), electricity and private transportation. Wealthier people are going to have have more money to spend on leisure such as vacations and larger houses which will result in a high electricity consumption. When I calculated my correlation coefficient it showed a weak, positive correlation meaning that the two variables I used (income and carbon footprint) are not closely correlated, however when one increases, so does the other. It doesn't necessarily mean that a persons income always affects their carbon footprint, it just shows that on average when income increases so does the carbon footprint. **


 * __ Works Cited __ **

Hermes, J., Jungmeyer, L., & Ross, G. (2008, April 23). Price Sensitivity of Environmentally-Friendly Products. Retrieved November 7, 2009, from http://www.environmentalleader.com/2008/04/23/price-sensitivity-of-environmentally-friendly-products/~

Ma ckenzie, H., Messinger, H., & Smith, R. (2008, June 12). Canada’s Ecological Footprint, By Income. Retrieved November 7, 2009, from http://www.growinggap.ca/files/Ecological%20FootprintFINAL%20June%2024%2008.pdf

Statistics Canada. (2009, December 23). Retrieved November 6,1009, from http://www.statcan.gc.ca/start-debut-eng.html