Analytical Methods in Economics and Finance - Assignment Example

Name: Course: Tutor: Date: Part A (i) I would have expected the mean to be around 5 as most people one gets to meet seems to be frustrated with life in one way or the other. From the calculations the mean is 7.723815 which show that most people interviewed scored a high index in terms of life satisfaction. On the other hand, I would have expected the variance to be a value above 4 due to major disparities in life satisfaction between the wealth and the poor but it turns out to be 2.864. LIFESAT Mean 7.723815 Standard Error 0.040937 Median 8 Mode 8 Standard Deviation 1.692323 Sample Variance 2.863958 Kurtosis 2.845112 Skewness -1.28741 Range 10 Minimum 0 Maximum 10 Sum 13200 Count 1709 From both the histogram and line graph seen below the distribution exhibits majority of people interviewed to have a life satisfaction level of above 5 with majority scoring 8.  X Frequency 1 11 2 12 3 26 4 22 5 112 6 100 7 298 8 616 9 285 10 227 More 0 (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 = 0.08885 Steps and calculations: Log on and open excel, click on Data menu then choose Data analysis From the dialogue box choose regression Input 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 analysis From the dialogue box choose regression Input 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 analysis From the dialogue box choose regression Input 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 C LIFESAT = 7.35 – 0.01MEDCON + 0.002HRSWORK + 0.094GENDER -0.049AGE + 0.0006AGESQ + 0.38ALONE + 0.18INCOME + 0.1 Β1= y-intercept while β2, β3, β4, β5, β6, β7, β8 are slopes of respective variables. e represents the standard error. Part D Participant (X) Age = 25(years) Age squares = 625 Hours worked = 10 hours Medical 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 + 0.1 LIFESAT = 7.35 -0.01(2) + 0.002(10) + 0.094(1) – 0.049(25) + 0.0006 (625) + 0.38(1) + 0.18 (2) + 0.1 LIFESAT = 8.679 – 1.245 LIFESAT = 7.434 Part 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. Steps and calculations: Log on and open excel, click on Data menu then choose Data analysis From the dialogue box choose regression Input the y-range by highlighting column “AGE” Input the x-range by highlighting the column “AGESQ” Click on “labels” then ok. Calculations and regression outputs are found in excel documents. (ii) AGESQ is a significant variable to this model. Without AGESQ then age would not have been a significant parameter to this model. The non- linear relationship between age and life satisfaction demonstrates that the higher the age the lower is the satisfaction level i.e. age and life satisfaction are inversely proportional. Steps and calculations: Log on and open excel, click on Data menu then choose Data analysis From the dialogue box choose regression Input the y-range by highlighting column “LIFESAT” Input the x-range by highlighting the column “AGESQ” Click on “labels” then ok. Calculations and regression outputs are found in excel documents. (iii)From the above hypothesis test the model incorporates the use of significant variables to determine the level of life satisfaction. The use of R-squared also shows that the model is effective and is able to meet its goal. Steps and calculations: Log on and open excel, click on Data menu then choose Data analysis From the dialogue box choose regression Input the y-range by highlighting column “LIFESAT” Input the x-range by highlighting all the columns in a contiguous manner. Click on “labels” then ok. Calculations and regression outputs are found in excel documents. Read More