Essays on Analytical Method In Economics And Finance Math Problem

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(ii) My expectations for the covariance and correlation matrix were that the figures would all be positive in line with the life satisfaction level but this is not the case as the figures are mixed with others exhibiting a big range from others. This may have resulted from the fact that some variables used don’t conform to others hence the change of trend. Part B (i) ‘Money can’t buy happiness’ is an English proverb that provides a twist to human life. The question that many people ask themselves is whether having money guarantees someone a happy life.

Money alone cannot guarantee someone a happy life but its factor in determining whether one gets to enjoy life. When it comes to material things that one may wish for, money is able to provide but it cannot buy other life fulfilling aspects such as health, love and filling acceptable to the society. When we come up with a regression analysis of life satisfaction on income we find that an individual income is significant to one’s life satisfaction level. Regression outputs are: β1 = 7.3; β2 = 0.172; e = 0.08885Steps and calculations: Log on and open excel, click on Data menu then choose Data analysisFrom the dialogue box choose regressionInput the y-range by highlighting column “LIFESAT”Input the x-range by highlighting the column “INCOME”Click on “labels” then ok. Calculations are found in excel documents. (ii) According to the calculations in excel the regression output of life satisfaction on people living in Melbourne is β1 = 7.79: β2 = -0.249 and e = 0.07.

This shows that most people in this town are satisfied with life as compared to people whose are not residents of this town. Steps and calculations: Log on and open excel, click on Data menu then choose Data analysisFrom the dialogue box choose regressionInput the y-range by highlighting column “LIFESAT”Input the x-range by highlighting the column “MELB”Click on “labels” then ok. Calculations are found in excel documents. (iii) Majority of married people are happier with life according to data collected.

Individuals who are married recorded a high level of life satisfaction as compared to the unmarried individuals. Steps and calculations: Log on and open excel, click on Data menu then choose Data analysisFrom the dialogue box choose regressionInput the y-range by highlighting column “LIFESAT”Input the x-range by highlighting the column “MARRIED”Click on “labels” then ok. Calculations and regression outputs are found in excel documents. Part CLIFESAT = 7.35 – 0.01MEDCON + 0.002HRSWORK + 0.094GENDER -0.049AGE + 0.0006AGESQ + 0.38ALONE + 0.18INCOME + 0.1Β1= y-intercept while β2, β3, β4, β5, β6, β7, β8 are slopes of respective variables.

e represents the standard error. Part DParticipant (X)Age = 25(years)Age squares = 625Hours worked = 10 hoursMedical condition = 2 (duration of illness in years)Gender = 1(female)Relationship status = 0 (has never been married)Income category = 2 (annual income ranges between $31000 - $60000)Lives alone = 1 (lives by themselves)Substituting into the equation below: LIFESAT = 7.35 – 0.01MEDCON + 0.002HRSWORK + 0.094GENDER -0.049AGE + 0.0006AGESQ + 0.38ALONE + 0.18INCOME + 0.1LIFESAT = 7.35 -0.01(2) + 0.002(10) + 0.094(1) – 0.049(25) + 0.0006 (625) + 0.38(1) + 0.18 (2) + 0.1LIFESAT = 8.679 – 1.245 LIFESAT = 7.434Part E(i) According to the above hypothesis test AGE and AGESQ are jointly significant.

They both fall along the same path with only few deviations being pointed out.

From the test, age significantly affects the level of life satisfaction; young people feel life to be more satisfying as compared to old people. The majority of people aged between 20 and 30 years scored the highest levels of satisfaction.

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