home

[[file:Warrior Dash Results and Analysis 2011.xlsx]]
A blog made from another group!

http://oceanwiselfs252.wordpress.com/

=Brents Suggestions:= He recomended we stick with our original plan and cover many different locations. He also suggested to pehaps broaden our observations/ data to mobility rather than simply runners. Our research question could then be to make a correlation between mobility and something. Or we could just predict who is more mobile, men or women. Celise

=Project=

__Monitoring percent of female vs. male runners/cyclists/speed walkers.__ Variables to record: - Weather Conditions - Time (half hr increments?) - Day - Location - Male, Female Count - Total Tally

Time of day: 4-6pm M-F, Saturday and Sunday suggestions? Does it need to be same time?

SCHEDULE: S - R M - Ja T - Ja W - Ja & Je Th - Ja&M F - Ja & P S - P&C&M

LOCATIONS: P = Richmond Dyke Ja = Seawall (yaletown) R = Seawall (granville Island) C = kits seawall M = Kits Je = Running Group (Langley)

Local: Sun Run, Underwear Affair, any other ideas, Marathons local and other provinces? National: Major Marathons, Iron Man's, Tough Mudder, Warrior Dash [] etc. [] - Harry's Spring Run (525 Males, 496 Females)
 * Reasearch topics:**

[] - utility cycling is a concept of cycling not just for exercise but for transportation. [] - this web site is from victoria about starting up a walking and biking to work program. Includes incentives and analysis on the cost benefits. [] - commuting time and % of commuters walking or cycling.
 * Some Interesting Web Pages:**


 * Questions, Flaws, Variables, Concerns:**

WE ARE DONE! IT'S ALL HANDED IN! WOO

also, we need to have our first and last names on the document, so if everyone could list their full names below this:

Paige Holloway Megan Bontogon Jacqueline O'Neil Celise Bellany Jung Eun (Rebecca) Shim



=Answers to assigment 1=

Canadas grain yeilds (kg/hectare) smallest to largest ..2375..2447..2584..2760..2783..2806..2844..2965..3046..3088..3142..3216..3301..3387.. 1a) Mean=2910 Median=(2844 + 2965)/2=2904 Mode=N/A

Chinas grain yeilds (kg/hectare) smallest to largest ..4756..4802..4826..4878..4890..4897..4947..4954..5190..5226..5319..5320..5460..5524.. 1a)Mean=5070 Median=(4947 + 4954)/2=4950 Mode=N/A

**1B)**
The skew of the cereal yields for Canada is <-0.2345>. The skew characterizes the degree of asymmetry of a distribution around its mean, 2,910. “The negative skew indicates that the ’ //tail// ’ on the left side of the probability density function is //longer// than the right side and the bulk of the values ( including the median=2,568) lie to the right of the mean.”1 Canada’s grain yield skew suggests the median lie to the right of the mean, as seen above this is not the case. The skew for the cereal yields for China, however is 0.51815. “A positive skew indicates that the //tail// on the right side is //longer// than the left side and the bulk of the values lie to the left of the mean,”1 As noted above the mean for China’s cereal yield is 5071 and a median of 4,884, with a positive skew it would be expected for the median to lie to the left of the mean, this is true in this case. Ref:
 * 1) 1. [], accessed 01/30/2012


 * 1C)** The correlation of the cereal yields of Canada and China is 0.8159. From this result, we can conclude that the cereal yields of Canada and China are moderately correlated through 1996-2007. We can also generally conclude, that as Canada’s cereal yields are increasing, China’s will also be increasing.

**1D) posted above as assignment 1 question 1**
===1E) The distribution between the Canada and China scatter plots display a significant difference during the years 1996-2009. Canada's grain yields show a relatively stable range of growth and decrease in grain yields from 1996-2009, yields ranging from 2000-3500. On the other hand, China's grain yields show a substantial growth during 2004-2009, yields ranging from the high 4000's during 1996-2004 and drastically increasing to mid 5000's by 2009. A possible explanation for these results is due to population increase in China during the years of grain yield growth. Another possibility is due to the increased demands of export from China by other countries in the world. According to the information provided by Trading Economics (www.tradingeconomics.com), founded by Anna Fedec and Antonio Sousa, the population of Canada has increased by 6.5 million between the years of 1990-2012, whereas the population of China has increased by 205.8 million. The large population growth in China explains the need to increase cereal yields drastically, as opposed to Canada who can afford a much steadier increase in yields. Also according to Trading Economics (www.tradingeconomics.com), China's economy, which is the second largest in the world, has changed from a closed international trade into one of the largest exporting markets within the past 30 years. However, the Canadian economy is dependent on 45% of Gross Domestic Products (GDP) on foreign trade. In other words China is a provider and Canada is a beneficiary. These observations show effects of cereal yields due to population and economy in Canada and China.===

**2B)**
The distribution of data displayed on this histogram is more of an estimate of each country’s infant mortality rate (IMR) than an accurate statistic. This histogram is only a probability of IMR, due to the methods of gathering data from each country differs. For some countries the IMR is an approximation, for civil registrations, census, or surveys are not completed properly,if at all. The IMR is meant to represent the living conditions of a society (social, economical and environmental conditions), by evaluating child survival. But since the IMR data is not exact, the distribution of data on this histogram actually represents more realistically the vulnerability of each country. Resources: [] Pages 112-116 of 234 Printed : 5/13/2010 4:49:18 PM []
 * 2C)** The World Health Organization (WHO) is striving to promote health development, foster health security, and strengthen health systems (WHO 2012). Many health issues in one place in the world can have a detrimental effect on another part of the world. For example, “ rapid urbanization, environmental mismanagement, the way food is produced and traded, and the way antibiotics are used and misused .”(WHO 2012) Problems such as these can make certain populations more vulnerable to health problems. As previously demonstrated, the distribution of infant mortality rate in 2010 for infants per 1,000 live births amongst various countries is highly skewed. This shows that there are extremely different standards of health services amongst countries. Therefore, in order to achieve their goal of strengthening health systems, the WHO must decrease the skewness of the distribution. By doing this the difference in health systems between countries will be smaller, and they will be better able to achieve their goals and prevent global outbreaks.

**2D)Paige**
Please see Correlation doc. I used the following variables: - physicians per 1000 people - % pop living in slums - % GDP spent on health - Exposure to air pollution

Reference: []