Education in the PastEducation 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.

• 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.

Central TendencyCentral TendencyMEAN

Undergrad: Male: 286,529 Female: 395,275Graduate: Male: 61,583 Female: 62,026

MEDIANUndergrad: Male: 276,461 Female: 379,634Graduate: Male: 58,250 Female: 59,297

MODEUndergrad: Male: none Female: noneGraduate: 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.

Undergraduate Enrolments

0

100,000

200,000

300,000

400,000

500,000

1995 2000 2005 2010

Year

En

rolm

en

t N

um

be

rs

Undergraduate levelMales

Undergraduate levelFemales

Poly. (Undergraduatelevel Females)

Poly. (Undergraduatelevel Males)

Males:

•y = 1478.1x2 - 6E+06x + 6E+09

•r2 = 0.9794

Females:

•y = 1943.8x2 - 8E+06x + 8E+09

•r2 = 0.9827

MINIMUM Male: 268,734 Female: 362,874

MAXIMUM Male: 325,374 Female: 460,284

RANGE Male: 56,640 Female: 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.

Undergrad Enrolments: Undergrad Enrolments: Measures of SpreadMeasures of Spread

Q1: Male: 277525 Female: 375008.5

Q2: Male: 276,461 Female: 379,634

Q3: Male: 300917 Female: 419959

Inter-quartile Range: Male: 23392 Female: 44950.5

Box and Whisker Plot

Male= L1 Female= L2

Z-score: Male Z-score: FemaleMinimum: -0.871 Minimum: -0.882Maximum: 1.902 Maximum: 1.771

Graduate Enrolments

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1994 1996 1998 2000 2002 2004 2006Year

En

rolm

en

t N

um

bers Graduate level Males

Graduate levelFemales

Poly. (Graduate levelFemales)

Poly. (Graduate levelMales)

Males:

•y = -3.8346x5 + 38339x4 - 2E+08x3 + 3E+11x2 - 3E+14x + 1E+17

•r2 = 0.9972

Females:

•y = 293.33x2 - 1E+06x + 1E+09

•r2 = 0.9946

Graduate EnrolmentsGraduate EnrolmentsMale=L3 Female=L4

RANGEMale: 15,861 Female: 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.

Graduate Enrolments: Graduate Enrolments: Measures of SpreadMeasures of Spread

Q1: Male: 58275.75 Female: 56759.25Q2: Male: 58,250 Female: 59,297Q3: Male: 65354.5 Female: 67,777

Inter-quartile RangeMale: 7078.75 Female: 11017.75

Box and Whisker Plot

Standard DeviationMale: 5983.846 Female: 7785.44VarianceMale: 35,806,414 Female:

60,613,079.6Z-score: MaleMinimum: -0.833 Maximum: 1.818Z-score: FemaleMinimum: -1.002 Maximum: 1.828

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.

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.

Central TendencyCentral TendencyMEAN

College: Male: 1,187 Female: 1,235Trade: Male: 23.5 Female: 121

MEDIANCollege: Male: 1,175 Female: 1,226Trade: Male: 22.5 Female: 127.5

MODECollege: Male: none Female: noneTrade: Male: none Female: 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.

Community College Enrolments

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1995 2000 2005 2010

Year

En

ro

lme

nt

Nu

mb

ers Community college

certificate ordiploma and othercommunity collegelevel Males

Community collegecertificate ordiploma and othercommunity collegelevel Females

Poly. (Communitycollege certificateor diploma andother communitycollege level Males)

Poly. (Communitycollege certificateor diploma andother communitycollege levelFemales)

Males:

•y = -0.434x6 + 5208x5 - 3E+07x4 + 7E+10x3 - 1E+14x2 + 8E+16x - 3E+19

•r2 = 0.7823

Females:

•y = -0.5321x6 + 6385.8x5 - 3E+07x4 + 9E+10x3 - 1E+14x2 + 1E+17x - 3E+19

•r2 = 0.9641

MINIMUMMale: 945 Female: 1,005

MAXIMUMMale: 1,536 Female: 1,644

RANGEMale: 591 Female: 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.

Q1: Male: 1151.3 Female: 1253.25Q2: Male: 1175 Female: 1226Q3: Male: 1059.8 Female: 1322.25

Inter-quartile Range Male: -91.5 Female: 69

Box and Whisker Plot

Male=L1 Female=L2

Z-score: Male Z-score: FemaleMinimum: -1.543 Minimum: 2.229 Maximum: -1.201 Maximum: 2.139

Trade/Vocational and Preparatory Training

0

50

100

150

200

250

1998 2000 2002 2004 2006

Year

Nu

mb

er

of

En

rolm

en

ts

Trade/vocational andpreparatory trainingcertificate or diplomaMales

Trade/vocational andpreparatory trainingcertificate or diplomaFemales

Poly. (Trade/vocational andpreparatory trainingcertificate or diplomaFemales)

Poly. (Trade/vocational andpreparatory trainingcertificate or diplomaMales)

Males:

y = 2.05x5 - 20527x4 + 8E+07x3 - 2E+11x2 + 2E+14x - 7E+16

r2 = 1

Females:

y = 5.25x5 - 52571x4 + 2E+08x3 –

4E+11x2 + 4E+14x - 2E+17

r2 = 1

MINIMUMMale: 9 Female: 78

MAXIMUMMale: 42 Female: 159

RANGEMale: 33 Female: 81Based on the data above, it can be seen that more women are entering the trades because the

minimum and maximum values are higher.

Trade Enrolments: Trade Enrolments: Measures of SpreadMeasures of Spread

Q1: Male: 24.75 Female: 122.25Q2: Male: 22.5 Female: 127.5Q3: Male: 20.25 Female: 107.25

Inter-quartile RangeMale: -4.5 Female: -15

Box and Whisker Plot

Male=L1 Female=L2

Z-score (2005)Males: -0.501 Females: -1.0317

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.

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 morewomen enrolling in post-secondary school or trade programs because each program takes a specific number of years to complete.

Central TendencyCentral TendencyMEAN

Males: 53,590 Females: 37,620MEDIAN

Males: 53,850 Females: 38,000MODE

Males: none Females: 38000

Based on the central tendencies listed above, it can be seen that on average, males earn more money than females.

Males: Females:y = -6.5268x4 + 52247x3 – y = -0.2768x4 + 2220.7x3 - 2E+08x2

+ 2E+11x - 1E+14 7E+06x2 + 9E+09x - 4E+12r2 = 0.9616 r2 = 0.8073

Average Earnings for Males and Females

-

10,000

20,000

30,000

40,000

50,000

60,000

1994 1996 1998 2000 2002 2004 2006Year

Earn

ings

($)

Average earnings,males (dollars) Fulltime workers

Average Earnings,Females Full TimeWorkers

Poly. (AverageEarnings, FemalesFull Time Workers)

Poly. (Averageearnings, males(dollars) Full timeworkers)

Male=L1 Female=L2

RANGEMales: 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.

Earnings: Earnings: Measures of Measures of

SpreadSpreadQ1: Male: 51625 Female: 36700Q2: Male: 53850 Female: 38000Q3: Male: 54775 Female: 38650

Inter-quartile RangeMale: 3150 Female: 1950

Standard DeviationMale: 1980.152 Female: 1228.82

VarianceMale: 3,921,000 Female: 1,510,000Z-score: Male Z-score: FemaleMinimum: -2.12 Minimum: -1.98Maximum:1 Maximum: 1.19

Conclusion: Was my Conclusion: Was my hypothesis correct?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.

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.

Earnings vs. Education

64

66

68

70

72

74

76

78

1994 1996 1998 2000 2002 2004 2006

Year

Earn

ing

sGraduated HighSchool

CollegeCertificate orDiploma

University Degree

Poly. (GraduatedHigh School)

Poly. (CollegeCertificate orDiploma)

Poly. (UniversityDegree)

Correlation CoefficientsCorrelation CoefficientsUniversity Degreey = 0.0069x6 - 83.108x5 + 415659x4 - 1E+09x3 + 2E+12x2 - 1E+15x +

4E+17r2 = 0.872

r = 0.934College Certificate or Diplomay = -0.0004x6 + 4.4216x5 - 22132x4 + 6E+07x3 - 9E+10x2 + 7E+13x - 2E+16r2 = 0.3828

r = 0.619Graduated High Schooly = 0.0003x6 - 4.0881x5 + 20465x4 - 5E+07x3 + 8E+10x2 - 7E+13x + 2E+16r2 = 0.5561

r = 0.746

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.

By: Amanda BettencourtBy: Amanda Bettencourt

Works Cited1. 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&C2DB=EST&VEC=&HILITE=ENROLMENTS&LANG=E&SrchVer=&ChunkSize=50&SDDSLOC=%2F%2Fwww.statcan.ca%2Fenglish%2Fsdds%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