3. Education in the Past Over the past 25 years, technological advancement has increased the need for highly educated workers. Women saw their employment rates increase as more of them moved into the labour market. However, for men, rates have decreased. This decline is visible due to their lower levels of education.

4. From the years 1996-2005, has the enrolments in to undergraduate and graduate programs been a steady increase? Who is enrolling at a greater pace, males or females? I predict that there will be a steady increase in enrolments for under- and graduate degrees. I also predict that females are enrolling at a greater pace than males.

5. Central Tendency MEAN Undergrad: Male: 286,529 Female: 395,275 Graduate: Male: 61,583 Female: 62,026 MEDIAN Undergrad: Male: 276,461 Female: 379,634 Graduate: Male: 58,250 Female: 59,297 MODE Undergrad: Male: none Female: none Graduate: Male: none Female: none One can see that the mean number of females entering undergraduate programs is substantially higher than the mean number of males enrolling in undergraduate programs.

6. Males: y = 1478.1x 2 - 6E+06x + 6E+09 r 2 = 0.9794 Females: y = 1943.8x 2 - 8E+06x + 8E+09 r 2 = 0.9827

7. MINIMUM Male: 268,734 Female: 362,874 MAXIMUM Male: 325,374Female: 460,284 RANGE Male: 56,640Female: 97,410 As shown above, one can see that the minimum and maximum number of undergraduate enrolments of males are substantially lower than that of females.

8. Undergrad Enrolments: Measures of Spread Q 1 : Male: 277525Female: 375008.5 Q 2 : Male: 276,461Female: 379,634 Q 3 : Male: 300917Female: 419959 Inter-quartile Range: Male: 23392Female: 44950.5 Box and Whisker Plot

9. Male= L 1 Female= L 2 Z-score: MaleZ-score: Female Minimum: -0.871Minimum: -0.882 Maximum: 1.902 Maximum: 1.771

10. Males: y = -3.8346x 5 + 38339x 4 - 2E+08x 3 + 3E+11x 2 - 3E+14x + 1E+17 r 2 = 0.9972 Females: y = 293.33x 2 - 1E+06x + 1E+09 r 2 = 0.9946

11. Graduate Enrolments Male=L 3 Female=L 4 RANGE Male: 15,861Female: 22,035 - As illustrated above, one can see that overall, more females are enrolled in graduate programs. It can also be seen that the minimum number of male enrolments is higher than the number for females. By contrast, the maximum number of graduate enrolments for males is much lower than the number for females. This difference proves that on average, more females are enrolled in graduate programs.

12. Graduate Enrolments: Measures of Spread Q1: Male: 58275.75Female: 56759.25 Q2: Male: 58,250Female: 59,297 Q3: Male: 65354.5Female: 67,777 Inter-quartile Range Male: 7078.75Female: 11017.75 Box and Whisker Plot

13. Standard Deviation Male: 5983.846Female: 7785.44 Variance Male: 35,806,414Female: 60,613,079.6 Z-score: Male Minimum: -0.833Maximum: 1.818 Z-score: Female Minimum: -1.002Maximum: 1.828

14. My hypothesis was PARTIALLY correct. The graphs proved that there is an increase in the number of enrolments to university, but the increase has not been steady. Between 1997 and 2000 the numbers of female and male enrolments dipped, but steadily increased after 2001. My hypothesis was correct in predicting that females are enrolling in university programs at a greater rate than males. The mean for females is higher for both undergraduate and graduate enrolments.

15. As more students are enrolled in university programs, have college and trade enrolments declined? Who has a higher enrolment rate into these programs, males or females? I predict that the number of students enrolled in college and trade programs throughout the years has not been declining, but remaining steady. Also, I predict that males have a higher enrolment rate than females.

16. C e n t r a l T e n d e n c y MEAN College: Male: 1,187Female: 1,235 Trade: Male: 23.5 Female: 121 MEDIAN College: Male: 1,175Female: 1,226 Trade: Male: 22.5Female: 127.5 MODE College: Male: noneFemale: none Trade: Male: noneFemale: none Based on the central tendencies provided above, one can see that the mean and median for female enrolments are higher than the mean and median for male enrolments, proving that more females are enrolled in college and trade programs.

17. Males: y = -0.434x 6 + 5208x 5 - 3E+07x 4 + 7E+10x 3 - 1E+14x 2 + 8E+16x - 3E+19 r 2 = 0.7823 Females: y = -0.5321x 6 + 6385.8x 5 - 3E+07x 4 + 9E+10x 3 - 1E+14x 2 + 1E+17x - 3E+19 r 2 = 0.9641

18. MINIMUM Male: 945Female: 1,005 MAXIMUM Male: 1,536Female: 1,644 RANGE Male: 591Female: 639 One can see that the minimum and maximum for the number of college enrolments is higher for females than for males. This proves that more females are enrolled in college programs.

19. Q1: Male: 1151.3Female: 1253.25 Q2: Male: 1175Female: 1226 Q3: Male: 1059.8Female: 1322.25 Inter-quartile Range Male: -91.5Female: 69 Box and Whisker Plot

20. Male=L 1 Female=L 2 Z-score: MaleZ-score: Female Minimum: -1.543Minimum: 2.229 Maximum: -1.201Maximum: 2.139

21. Males: y = 2.05x 5 - 20527x 4 + 8E+07x 3 - 2E+11x 2 + 2E+14x - 7E+16 r 2 = 1 Females: y = 5.25x 5 - 52571x 4 + 2E+08x 3 – 4E+11x 2 + 4E+14x - 2E+17 r 2 = 1

22. MINIMUM Male: 9 Female: 78 MAXIMUM Male: 42 Female: 159 RANGE Male: 33 Female: 81 Based on the data above, it can be seen that more women are entering the trades because the minimum and maximum values are higher.

23. Trade Enrolments: Measures of Spread Q1: Male: 24.75Female: 122.25 Q2: Male: 22.5Female: 127.5 Q3: Male: 20.25Female: 107.25 Inter-quartile Range Male: -4.5Female: -15 Box and Whisker Plot

24. Male=L 1 Female=L 2 Z-score (2005) Males: -0.501Females: -1.0317

25. My hypothesis was NOT correct. I predicted that more males would be enrolled in college and trade programs and that the enrolment rate of males and females would be steady. The graphs prove that females are enrolled in college and trade programs in greater numbers than males. The data also proved that the enrolment rates fluctuated greatly each year from 1996 to 2005. There were also many outliers in the data, which also proved that my prediction was incorrect.

26. If females are enrolled in university, college and trade programs in higher numbers than males, which sex earned the greatest amount of money between 1996 and 2005? I predict that males made more money between 1996 and 2005 because it will take more time to see the economic effects of more women enrolling in post-secondary school or trade programs because each program takes a specific number of years to complete.

27. Central Tendency MEAN Males: 53,590 Females: 37,620 MEDIAN Males: 53,850 Females: 38,000 MODE Males: noneFemales: 38000 Based on the central tendencies listed above, it can be seen that on average, males earn more money than females.

28. Males:Females: y = -6.5268x 4 + 52247x 3 – y = -0.2768x 4 + 2220.7x 3 - 2E+08x 2 + 2E+11x - 1E+14 7E+06x 2 + 9E+09x - 4E+12 r 2 = 0.9616 r 2 = 0.8073

29. Male=L1Female=L2 RANGE Males: 6,900 Females: 3,900 The minimum and maximum values are higher for males than for females. This data proves that between the year 1996 and 2005, males have made more money.

30. Earnings: Measures of Spread Q1: Male: 51625Female: 36700 Q2: Male: 53850Female: 38000 Q3: Male: 54775Female: 38650 Inter-quartile Range Male: 3150Female: 1950 Standard Deviation Male: 1980.152Female: 1228.82 Variance Male: 3,921,000Female: 1,510,000 Z-score: MaleZ-score: Female Minimum: -2.12Minimum: -1.98 Maximum:1Maximum: 1.19

31. Conclusion: Was my hypothesis correct? My hypothesis WAS correct. I predicted that since females have enrolled in post-secondary and trade programs more than males between 1996 and 2005, it will take a few more years before the data will show a rise in female wages. Currently on average, males earn more money than females.

32. Which graduate will make the most money in the future? A college, university or only high school graduate? I predict that those who have graduated from high school will earn the most money because most often these graduates work in the skilled trade careers, which are generally well paid.

34. Correlation Coefficients University Degree y = 0.0069x 6 - 83.108x 5 + 415659x 4 - 1E+09x 3 + 2E+12x 2 - 1E+15x + 4E+17 r 2 = 0.872r = 0.934 College Certificate or Diploma y = -0.0004x 6 + 4.4216x 5 - 22132x 4 + 6E+07x 3 - 9E+10x 2 + 7E+13x - 2E+16 r 2 = 0.3828r = 0.619 Graduated High School y = 0.0003x 6 - 4.0881x 5 + 20465x 4 - 5E+07x 3 + 8E+10x 2 - 7E+13x + 2E+16 r 2 = 0.5561r = 0.746

35. My hypothesis WAS correct. I predicted that those who graduated from trade preparatory programs would earn the most money. The correlation coefficients indicate that a university degree and earnings have a strong polynomial correlation, however, there is a large outlier in the data. On average, the earnings for trade employees are higher than the earnings for university and college graduates.

36. By: Amanda Bettencourt

37. Works Cited 1.CANSIM Table 477-0013. “University Enrolments, by registration status, program level, Classification of Instructional Programs, Primary Grouping, and sex, annual.” StatisticsCanada: E-STAT. July 2007. http://estat.statcan.ca/cgi- win/CNSMCGI.EXE?regtkt=&C2Sub=&ARRAYID=4770013&C2D B=EST&VEC=&HILITE=ENROLMENTS&LANG=E&SrchVer=&Chu nkSize=50&SDDSLOC=%2F%2Fwww.statcan.ca%2Fenglish%2Fs dds%2F*.htm&ROOTDIR=ESTAT/&RESULTTEMPLATE=ESTAT/CII _PICK&ARRAY_PICK=1&SDDSID=&SDDSDESC=http://estat.statcan.ca/cgi- win/CNSMCGI.EXE?regtkt=&C2Sub=&ARRAYID=4770013&C2D B=EST&VEC=&HILITE=ENROLMENTS&LANG=E&SrchVer=&Chu nkSize=50&SDDSLOC=%2F%2Fwww.statcan.ca%2Fenglish%2Fs dds%2F*.htm&ROOTDIR=ESTAT/&RESULTTEMPLATE=ESTAT/CII _PICK&ARRAY_PICK=1&SDDSID=&SDDSDESC= 2.CANSIM Table 202-0104. “Female-to-male earnings ratios, by selected characteristics, annual.” StatisticsCanada: E-STAT. July 2007. http://estat.statcan.ca/cgi-win/CNSMCGI.EXE