Presentation on theme: "Data Management Culminating Project Factors That Affect Income of Canadians By: Jodi Morden & Mike Curridor Sacred Heart High School, Ottawa."— Presentation transcript:
Data Management Culminating Project Factors That Affect Income of Canadians By: Jodi Morden & Mike Curridor Sacred Heart High School, Ottawa
Factors Investigated Location, Sex, Age, and Education Hypothesis All of these factors contribute to the fluctuation of one’s income Therefore living in a big city, being male, between years of age, and having a university level education. Causes one to have a higher income.
Location: City Average Income of the 5 largest cities in Canada: $54,469 (Toronto, Montreal, Vancouver, Ottawa, Calgary) Average Income of the 20 Other Largest Cities in Canada: $47,952
CityAverage Income Toronto$60,110 Montreal$44,593 Vancouver$54,055 Ottawa$56,760 Calgary$56,829 We can conclude that living in a big city affects one’s income. However, your income may also affect one living in a big city. Therefore we can conclude that there is a relationship, but that it is causal. Also the city has a direct correlation with the type of work. Due to a uniform graph, we can see no major difference between the cities.
Measures of Spread Mean:$54,469 Median:Ottawa ($56,670) Standard Deviation:
Location: Province ProvinceAverage Income Newfoundland$41,064 PEI$42,028 Nova Scotia$41,446 New Brunswick$41,090 Quebec$42,229 Ontario$54,291 Manitoba$43,404 Saskatchewan$42,685 Alberta$51,118 British Columbia$50,667 Yukon$54,953 Northwest Territories$60,506
We can see that the Yukon and the Northwest Territories have the highest income. Although this may appear odd at first, if investigated it makes quite a bit of sense. There is essentially no unemployed people in these provinces, because most of the unemployed moved south to find work. The employed people who do live there are given special incentives by their employer to live there ( i.e. more money). Therefore the small number of people that do live there are making a large sum of money.
Measures of Spread Median:$43, Mean:$47, Standard Deviation:
IncomeMalesFemales $0-$19, $20,000-$39, $40,000-$59, $60,000-$79, $80,000-$99,99961 $100, The number of males and females earning a high income ($100,000+) are relatively low, but equal. As we move down the income brackets the division becomes more and more. Eventually leveling back off at the lower income brackets. We can interrupt this in saying, woman either have a very small or very large income. Therefore the majority of the middle earners are male.
Measures of Spread Skewed Right Mean:Males: $33, Females: $23, Median:Males: $0-$19,999 Females: $20,000-$39,999 Mode:Males: $20,000-$39,999 Females: $0-$19,999 Standard Deviation:Males: Females:
Age Source: 1991 Census microdata
The income of people increases from 18, and peaks around 50. Then takes a sharp decline. Except for certain outliers, we can conclude that once a person passes the age of 50 their income declines. This is mainly because of inability to work full days and the retirement factor. All these can contribute to the decline in wages and number of hours worked daily. This graph shows a strong positive correlation, which further substantiates the affect that age has on one’s income.
Measures of Spread Mode:$0-$39,999- Less than University 40% $40,000-$79,999- Less than University 36% $80,000+- Less than High school 59% *Mode is only applicable to this data set. The data was surprising with respect to the high income earners. This was because 56% of the high income earners had education of less than high school. Even within the middle income bracket, the majority of earners had an education of less than university. Therefore we can see no evidence which supports our original prediction.
Summarization To recap- we predicted that the most important factors that affect a person’s income are location, sex, age, and education. We predicted that living in a big city, being male, being between the age of 35 and 45, and having a university level education. The first factor that we looked at was location, we concluded that if you live in a big city, to be more precise Toronto, you are more likely to earn a higher income. Also we determined that this relationship was causal. The second factor that we looked at was sex, we discovered that males earn more on average than females. The third factor that we looked at was age, we found that at the age of 18 a person’s income increased steadily. Peaked around 50 years old, and began to decrease afterwards. The final factor that we looked at was education. From the examination we discovered a surprising twist in the data, it showed that the majority of high income earners had an education less than high school.
Conclusions From the data that we have analyzed, we can predict that being male, living in a big city, being years of age, increase one’s chance of high income. We were incorrect with our initial prediction of education. A possibility to explain the number of high income earners who had a less than high school education could be, taking over a family run company. The person knows they have a well paying job once they leave school, therefore they may see school as unnecessary.
Sources Statistics Canada 2001 Census – Average Income based on location (province, CMA) Statistics Canada Micro data from the 1991 Census – Income based on Sex, Education, and Age