Use time series to predict the old age dependency ratio trend (total 1000words)

Anonymous
timer Asked: Dec 28th, 2018

Question description

  • Use data set 1 in attached file
  • Do Part 3 + 4
  • Times New Romans/ Double Space/ size 12
  • Limit: 800 words for part 3 and 200 words for part 4
  • Individual (by each team member)

    • Use data set 1 of the countries A and B for the years 1980 - 2011
    • Apply the following time trend models for each country (Country A / Country B)
      • Linear trend model (LIN)
      • Quadratic trend model (QUA)
      • Exponential trend model (EXP)
    • Based on the results found in 2):
      • Which trend model will you recommend to predict the old age dependency ratio for Country A?
      • Which trend model will you recommend to predict the old age dependency ratio for Country B?

    For each trend model (LIN / QUA / EXP)

    • Provide the regression output in the report
    • Provide the formula of the trend model
    • Predict the old age dependency ratio for the years
      • 2012, 2013, 2014and 2015
    • Calculated the errors for the years
      • 2012, 2013, 2014and 2015
    • Calculated the MAD and SSE

    Explain your answers.


    Part 4: Team Time Series conclusion:

    In part 3, your team has looked at several trend models to predict the old age dependency ratio over time. In this part your team will write a conclusion based on your findings in part 3.

    Task: Write a conclusion to predict the old age dependency ratio over time

    • Provide a line chart of old age dependency ratio over time from 1980 - 2015, including the countries of allthe team members.
    • Comment on the line chart. Are all the countries following the same trend?
    • Compare the trend models of the countries analyzed by each team member in part 3.
      • Which countries are following the same trend model?
      • Which trend model does your team consider to be the best old age dependency ratio predictor? Explain your answer.

    Write your conclusion in an unbiased manner to a non-technical audience.

    Indicator: Year 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Adolescent fertility rate (births per 1,000 women ages 15-19) Benin Spain 107.425 23 109.74 22.124 112.055 21.248 114.264 20.0218 116.473 18.7956 118.682 17.5694 120.891 16.3432 123.1 15.117 123.8332 14.0294 124.5664 12.9418 125.2996 11.8542 126.0328 10.7666 126.766 9.679 125.9774 9.3544 125.1888 9.0298 124.4002 8.7052 123.6116 8.3806 122.823 8.056 121.3902 8.499 119.9574 8.942 118.5246 9.385 117.0918 9.828 115.659 10.271 114.263 10.6604 112.867 11.0498 111.471 11.4392 110.075 11.8286 108.679 12.218 106.2162 11.574 103.7534 10.93 101.2906 10.286 98.8278 9.642 96.365 8.998 94.3112 8.918 92.2574 8.838 90.2036 8.758 88.1498 8.678 Development over time period 140 120 100 80 60 40 20 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 Benin Box and Whisker plot of Benin 2 1 80 85 90 95 100 105 110 Development over time period 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Benin Spain Central Tendency Adolescent fertility birth rate in Benin Mean 113.310 Median 115.659 Mode No mode Adolescent fertility birth rate in Spain Mean 12.102 Median 10.660 Mode No mode Variation Adolescent fertility birth rate in Benin Mean 113.310 Min 88.150 Max 126.766 Range 38.616 Quartile 1 104.9848 Quartile 3 122.9615 IQR 17.9767 Variance 125.954 Standard deviation 11.223 CV (%) 9.90% Adolescent fertility birth rate in Spain Mean 12.102 Min 8.056 Max 23 Range 14.944 Quartile 1 8.930 Quartile 3 12.580 IQR 3.650 Variance 17.921 Standard deviation 4.233 CV (%) 34.98% Box-and-Whisker plot Adolescent fertility birth rate in Benin Adolescent fertility birth rate in Spain Box 1 88.150 Box 1 8.056 Whisker Bottom 16.835 Whisker Bottom 0.874 Box 2 Box 3 Whisker Top 10.674 7.302 3.805 Box 2 Box 3 Whisker Top 1.730 1.920 10.420 Box and Whisker plot of Spain Whisker plot of Benin 2 1 110 115 120 125 130 6 8 10 12 14 16 d Whisker plot of Spain 18 20 22 24 YEAR 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 No. Country Name 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Belgium Benin Brazil Comoros France Georgia Germany Ghana Haiti Honduras Iceland India Indonesia Kenya Lebanon Malawi Mali Mexico Nigeria Norway Pakistan Philippines Portugal Qatar Senegal Spain Sweden Tanzania Togo Tunisia Turkey Uganda United Kingdom Vietnam Zimbabwe Country Code BEL BEN BRA COM FRA GEO DEU GHA HTI HND ISL IND IDN KEN LBN MWI MLI MEX NGA NOR PAK PHL PRT QAT SEN ESP SWE TZA TGO TUN TUR UGA GBR VNM ZWE Adolescent fertility rate GNI per capita, (births per 1,000 women Atlas method ages 15-19) (current US$) 5.3384 44230 90.2036 870 63.7578 10100 68.989 790 8.9614 40730 48.201 4120 7.1462 45790 68.677 1490 38.976 810 73.5778 2090 7.827 49960 25.9968 1600 48.6144 3430 83.0942 1310 12.5314 8040 141.779 340 173.0382 790 62.5464 9860 111.218 2850 5.7708 93050 38.4744 1430 59.211 3520 10.4192 20440 10.4604 75660 77.0508 980 8.758 28420 5.3086 57880 118.1232 910 90.1494 610 7.6278 3930 28.006 12000 114.5604 670 14.5964 43720 30.7942 1950 107.5554 890 Contigency Table Level of Adolescent fertility rate High (>30) Low Total Level of GNI per capita Low (<$1,045) Middle High (>$12,736) 10 11 0 0 4 10 10 15 10 Total 21 14 35 Benin 88.1498 90.2036 92.2574 94.3112 96.365 98.8278 101.2906 103.7534 106.2162 107.425 108.679 109.74 110.075 111.471 112.055 112.867 114.263 114.264 115.659 116.473 117.0918 118.5246 118.682 119.9574 120.891 121.3902 122.823 123.1 123.6116 123.8332 124.4002 124.5664 125.1888 125.2996 125.9774 126.0328 126.766 n = 37 Quartile 1 = X (37+1/4) = 104.985 Quartile 3 = X (3x38/4) = 122.962 Spain 8.056 8.3806 8.499 8.678 8.7052 8.758 8.838 8.918 8.942 8.998 9.0298 9.3544 9.385 9.642 9.679 9.828 10.271 10.286 10.6604 10.7666 10.93 11.0498 11.4392 11.574 11.8286 11.8542 12.218 12.9418 14.0294 15.117 16.3432 17.5694 18.7956 20.0218 21.248 22.124 23 n = 37 Quartile 1 = 8.930 Quartile 3 = 12.580 Indicator Name Adolescent fertility rate (births per 1,000 women ages 15-19) GNI per capita, Atlas method (current US$) Long definition Adolescent fertility rate is the number of births per 1,000 women ages 15-19. GNI per capita is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of
    Business Statistics ECON1193 - Semester 3, 2018 Assessment Task 3: Team assessment Statistics (35%) Course learning outcomes (CLO): CLO 1: Illustrate and describe information using a range of numerical and graphical procedures both written and in Excel. CLO 2: Using sample information, apply the scientific process to arrive at probable conclusions about population values. CLO 3: Highlight statistical relationships between variables in data sets and to predict values of strategic variables using regression and correlation analysis. CLO 4: Critically analyse, in team based collaboration, the statistical findings published by the media, research agencies and the Government to validate and verify its accuracy. CLO 5: Communicate statistical findings and results in an unbiased manner to a nontechnical audience such as decision makers, stakeholders and the general public. Submission: Team assessment is due on 11th January (Week 12), Friday (midnight) The team leader will need to complete the following: o Report submission in TII through Canvas o Upload the Excel file in Canvas o The Excel file is for the lecturer’s reference only. The Excel file will NOT be marked. o The front page of the report should state: ▪ The topic ▪ The name of the team members ▪ The student number of each member ▪ The lecturer’s name ▪ Your group number Each team will have at most 4 team members. Contact your lecturer for your team formation. 1 out of 8 Version OLD – Age dependency ratio, old (% of working-age population) The United Nations has set 17 Sustainable Development Goals (SDG) for 2030. SDG 3 is the Good Health and Well-being goal: Ensure healthy lives and promote well-being for all at all ages. One way to measure this is by looking at the age dependency ratio. In this assignment you will use the data sets from the World Bank. These are the same data sets you have used in your first and second assignment. Your case study report must contain the following 5 parts: 1. 2. 3. 4. 5. Multiple Regression Team Regression conclusion Time Series Team Time Series conclusion Overall conclusion 2 out of 8 Part 1: Multiple Regression (use data set 2): In this part you will build a regression model that will estimate the old age dependency ratio. You will use the data of all the team members. Task: Building regression models to estimate the old age dependency ratio 1. Combine data set 2 from all the team members. 2. Remove countries that appear more than once; ie if “Australia” is given 3 times in the combined data set, remove the 2 extra “Australia” from the data set. 3. Define this as Data set I: All Countries (ALL) 4. Split up Data set I: All Countries (ALL) into Low / Middle / High Income. Define Low / Middle / High income as follows • GNI less than $1,045 per capita are classified as Low-Income (LI) countries. • GNI greater than $12,736 per capita are considered High-Income (HI) countries. • GNI in between, are Middle- Income countries (MI). Split up the data set into 3 data sets: • Data set II: Low income countries (LI) • Data set III: Middle income countries (MI) • Data set IV: High income countries (HI) 5. Build a regression model for the data sets: a. Data set I: All Countries (ALL) b. Data set II: Low income countries (LI) c. Data set III: Middle income countries (MI) d. Data set IV: High income countries (HI) For each data set (ALL / LI / MI / HI) apply backward eliminations to build your model i. Identify the dependent and the independent variables in the data set. ii. Perform a multiple regression with all the variables in the data set. iii. Provide the regression output in the report iv. Determine the significant and non-significant independent variables. Use 𝛼 = 0.05. v. Identify the non-significant independent variable with the highest pvalue. vi. Remove the non-significant variable with the highest p-value from the data set. vii. Perform another multiple regression with the remaining independent variables. viii. Provide the regression output of the updated regression in the report. ix. Repeat this process until only significant independent variable(s) remain. x. In the final model (FINAL) only significant variables remain. 3 out of 8 6. From the final regression model (FINAL) of each data set (ALL / LI / MI / HI): a. Provide the Regression output in your report. b. Provide the Regression Equation in your report. c. Interpret the regression coefficient of the significant independent variable(s) in the context of your research. d. Interpret the coefficient of determination in the context of your research. 4 out of 8 Part 2: Team Regression conclusion: In part 1, your team has built several regression models to predict the old age dependency ratio, depending on factors like GNI and/or other factors. In this part your team will write a conclusion based on your findings in part 1. Task: Write a conclusion to estimate old age dependency ratio based on regression • • Compare the final regressions of each data set (ALL / LI / MI / HI) o Do all the models have the same significant independent variable(s)? o Which regression model will provide the best old age dependency ratio estimation? Explain your answer. Write your conclusion in an unbiased manner to a non-technical audience. Word limit: 150 words 5 out of 8 Part 3: Time Series (use data set 1): In this part you will use time to predict the trend of old age dependency ratio. Part 3 is an individual task. Each team member will analyze the trend of his/her countries A and B over time of their data set 1. This is the same data set 1 as used for the individual assessment 1. Task: Use time series to predict the old age dependency ratio trend Individual (by each team member) 1. Use data set 1 of the countries A and B for the years 1980 - 2011 2. Apply the following time trend models for each country (Country A / Country B) • Linear trend model (LIN) • Quadratic trend model (QUA) • Exponential trend model (EXP) For each trend model (LIN / QUA / EXP) a. Provide the regression output in the report b. Provide the formula of the trend model c. Predict the old age dependency ratio for the years o 2012, 2013, 2014 and 2015 d. Calculated the errors for the years o 2012, 2013, 2014 and 2015 e. Calculated the MAD and SSE 3. Based on the results found in 2): a. Which trend model will you recommend to predict the old age dependency ratio for Country A? b. Which trend model will you recommend to predict the old age dependency ratio for Country B? Explain your answers. 6 out of 8 Part 4: Team Time Series conclusion: In part 3, your team has looked at several trend models to predict the old age dependency ratio over time. In this part your team will write a conclusion based on your findings in part 3. Task: Write a conclusion to predict the old age dependency ratio over time a. Provide a line chart of old age dependency ratio over time from 1980 - 2015, including the countries of all the team members. b. Comment on the line chart. Are all the countries following the same trend? c. Compare the trend models of the countries analyzed by each team member in part 3. o Which countries are following the same trend model? o Which trend model does your team consider to be the best old age dependency ratio predictor? Explain your answer. Write your conclusion in an unbiased manner to a non-technical audience. Word limit: 200 words 7 out of 8 Part 5: Overall Team Conclusion: In assessment 1, you provided descriptive statistics. In assessment 2 you provided inferential statistics. In assessment 3, your team built regression models and used time series to find the trend of the old dependency ratio. Write on overall conclusion based on the (calculated) findings of all your assessments. Task: Write an overall conclusion on old age dependency ratio Your conclusion needs to answer the following questions: 1. 2. 3. 4. Is old age dependency ratio and country income related? What is/are the main factor(s) that will determine the old age dependency ratio? According to your team, where will the old age dependency ratio be in 2020? According to your team, is the projection of the United Nations correct of having a world average old age dependency ratio will be exceeding 20% by 2030? ● ● ● ● All four questions need to be answered. Support your answer of each question with at least 1 calculated result. o This calculated result can be coming from your assessments 1, 2 or 3. o This calculated result can be a new calculation. Use in-text reliable references to support your answers. Write your conclusion in an unbiased manner to a non-technical audience. Word limit: 450 words (*) Note for part 1 and part 3: Regression output should not exceed 3 decimals - End of the Assessment - 8 out of 8

    Tutor Answer

    (Top Tutor) Studypool Tutor
    School: UT Austin
    Studypool has helped 1,244,100 students
    flag Report DMCA
    Similar Questions
    Hot Questions
    Related Tags

    Brown University





    1271 Tutors

    California Institute of Technology




    2131 Tutors

    Carnegie Mellon University




    982 Tutors

    Columbia University





    1256 Tutors

    Dartmouth University





    2113 Tutors

    Emory University





    2279 Tutors

    Harvard University





    599 Tutors

    Massachusetts Institute of Technology



    2319 Tutors

    New York University





    1645 Tutors

    Notre Dam University





    1911 Tutors

    Oklahoma University





    2122 Tutors

    Pennsylvania State University





    932 Tutors

    Princeton University





    1211 Tutors

    Stanford University





    983 Tutors

    University of California





    1282 Tutors

    Oxford University





    123 Tutors

    Yale University





    2325 Tutors