Answer the 3 economitrics questions using Excel and Eviews

Anonymous
timer Asked: Oct 11th, 2016
account_balance_wallet $50

Question Description

Answer the problem set using Excel for creating variables  and Eviews to run the regressions

Read the requirement file carefully and answer the questions .

All the required data are available and attached with Question. 

Please I need quality work with complete interpretations,  there is no time to redo the work please make sure to full fill the requirement.

submission: should include a written document in Microsoft Word containing your responses as well as something showing your underlying data and calculations. Depending on the statistical program you’re using, this might be an Excel file for adding new varibles, screen shots from Eviews for run regressions (you can use the free version just do the screen shot for the tables , also  copy -paste the table in word document.

Thanks


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1 Problem Set 6 Submission: Your email should include a written document in Microsoft Word containing your responses as well as something showing your underlying data and calculations. Depending on the statistical program you’re using, this might be an Excel file, screen shots from Eviews. Data: The data for this problem set comes from an RCT evaluating a vocational training program for youth in Colombia. The link to the paper and data is: https://www.aeaweb.org/articles?id=10.1257/app.3.3.188 Download the full paper for this evaluation. Read the beginning of the paper, through page 200. This will give you an understanding of the contents of the data set and the overall purpose of the evaluation. The final estimation regression equation is given on page 200. You should also review the Data Appendix on pages 212-213. Download the data set for this evaluation. The data set is in Stata format. I have reformatted the data set to a “comma separated value” file that you can open in Excel. The csv file is available in the Google Drive folder. You must still download the data set in order to get the data set documentation. Specifically, the data download includes a “readme” file that gives the description of the variables in the data set. It is extremely important that you download and read the “readme” file, which describes individual variables and the naming conventions for baseline and follow-up measurement. There is additional information on the variables in the Data Appendix, beginning on page 212 of the paper. (The data are available) Question 1: Variable Construction 1. Consider the variables salary_06 and profit_06. What are the units for these variables, including the time period? For example, wages in the US are often measured as dollars per hour. 2. Construct a new variable earnday_06 that gives the total daily earnings from salary and profit in the 2006 survey round. The value of earnday_06 should be zero for people who are unemployed, and should be missing if the respondent was not interviewed in the 2006 round. Confirm that your new variable conforms to the statistics below in terms of mean and number of observations. . summarize earnday_06 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------earnday_06 | 3238 11562.25 11366.05 0 145714.3 3. Construct a binary indicator variables treatment and control. Your new variable should be named train, and should be based off of the existing variable, select. The value of train should be 1 for respondents who were selected into the vocational training program, and should be 0 for respondents who were in the control group. What percentage of initial program participants were selected for the vocational training program? 2 4. Construct 7 additional binary variables based on the categorical variable city. Your new variables should be named city_1, city_2, … , city_7. Each of the city_ indicator variables should be equal to 1 if the respondent resides in that city and equal to zero otherwise. Calculate the mean of each indicator variable. Which city has the largest number of respondents in this data set? What percentage of respondents live in that city? The remainder of the problem set is data analysis. There is no additional variable creation. At this point, you should transfer your data set to whichever program you will use to conduct statistical analysis. This might be Excel, Eviews. Question 2: Treatment Effects 1. Estimate and report the mean value of earnday_06 separately for treatment and control groups. What is the estimated effect of the program on average daily earnings based on a simple difference in means? Include the appropriate measurement units when reporting the estimated effect. 2. Run a simple bivariate regression of earnday_06 on train. Report and interpret the estimated coefficient on train. Is this coefficient estimate significantly different from zero? 3. Run a multivariate regression of earnday_06 on train. Include control variables for sex, age in 2006, and marital status in 2004. Report and interpret the estimated coefficient on train. Is this coefficient estimate significantly different from zero? 4. Discuss the two coefficient estimates on train, from parts 2 and 3. Is there any difference between the two coefficients in terms of accuracy (bias)? Is there any difference between the two coefficients in terms of precision (variance)? Question 3: City Differences 1. Estimate the mean value of earnday_06 for each city in the sample. Which city has the highest average daily earnings for respondents in this sample in 2006? Report the average daily earnings for that city in 2006. 2. Regress earnday_06 on your city indicator variables. Make city 1 the baseline category of the regression. Based on your regression estimates, what is the estimated average daily earnings for city 1? 3. Consider the coefficient estimate on city_3. Is this coefficient statistically significant at the 1% level? Interpret this coefficient. What is the estimated average daily earnings in city 3? 4. Are there any cities for which the estimate for average daily earnings is “statistically identical” to the estimate for city 1? In other words, are there any cities for which the difference in average daily earnings from city 1 is not statistically different from zero? If so, which cities? 5. Rerun the regression, adding control variables for sex, age, employment status, and marital status in 2006 (all variables from 2006). Is the coefficient for city_7 statistically significant? If so, interpret this coefficient. age_s 22 22 24 24 22 25 27 20 23 25 20 22 25 20 23 20 20 20 23 20 22 26 22 25 22 20 20 21 23 21 25 24 21 24 22 25 25 20 20 24 25 dmarried_sempl_06 salary_06 profit_06 tenure_06days_06 hours_06 contract_06 0 1 0 240000 15.2333 22 84 0 0 1 116000 0 1.86667 10 14 0 0 1 650000 0 1.86667 28 91 0 0 1 408000 0 0.1 28 48 0 0 0 0 0 0 0 0 0 0 1 0 70000 12.4333 20 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 408000 0 12.6 28 56 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 400000 13.1 16 28 0 0 1 220000 0 5.9 28 84 0 1 0 0 0 0 0 0 0 0 1 360000 0 0.96667 26 48 0 0 0 0 0 0 0 0 0 0 1 260000 0 2.9 28 70 0 1 0 0 0 0 0 0 0 0 1 140000 0 14.2333 15 40 0 1 0 0 0 0 0 0 0 1 1 0 150000 5.16667 20 35 0 0 1 100000 0 0.13333 16 55 0 0 1 0 0 13 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 408000 0 11.7333 26 48 1 0 1 0 100000 42.7333 15 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 320000 0 0.4 26 48 0 0 1 0 150000 18.5 22 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 350000 0 5.2 26 60 0 1 1 408000 0 13.1667 30 56 0 1 1 156000 0 12.7333 15 32 0 20 24 21 20 20 24 21 22 23 22 23 24 25 25 26 21 22 21 25 24 21 25 27 20 23 20 21 23 25 21 23 27 20 24 23 21 25 24 20 20 21 21 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 408000 408000 408000 0 0 145000 408000 0 160000 0 0 0 408000 0 0 380000 0 280000 420000 200000 0 260000 408000 180000 0 0 200000 408000 0 50000 800000 0 0 408000 250000 381500 408000 200000 240000 0 0 0 0 0 0 0 100000 0 0 0 0 100000 300000 0 0 0 0 0 50000 0 0 0 0 0 0 0 0 0 0 0 200000 0 0 180000 0 0 0 0 0 0 0 0 0 240000 13.1333 13.1667 3.46667 0 10.8333 0.16667 5.86667 0 10.1 87.3333 16.0333 0 2.5 0 0 0.53333 18.3667 43.5333 12.1667 8.9 0 0 13.3 5.8 0 0 2.06667 14.7333 31.3 3 12.5667 6.13333 0 9.9 1 4.6 14.9333 0.53333 2.83333 0 0 26.8 26 30 20 0 30 12 30 0 26 26 8 0 26 0 0 25 4 15 28 26 0 16 30 20 0 0 26 30 30 20 29 30 0 26 26 26 26 30 16 0 0 30 48 84 60 0 48 20 60 0 72 56 20 0 48 0 0 66 4 45 48 40 0 84 48 20 0 0 90 56 56 25 70 42 0 60 48 66 48 98 42 0 0 21 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 0 24 21 22 24 26 20 22 21 25 21 20 25 20 23 23 23 22 23 24 25 20 25 22 20 25 25 24 20 23 21 20 22 21 21 24 21 20 22 20 21 26 21 0 0 1 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 0 200000 0 0 0 0 0 200000 370000 360000 0 0 700000 408000 0 0 360000 0 120000 0 400000 120000 240000 0 400000 0 0 0 0 120000 0 0 0 0 0 250000 200000 0 0 248000 330000 434000 0 0 0 0 0 0 0 0 0 0 120000 0 0 0 200000 0 0 0 0 300000 0 0 0 0 0 0 0 0 0 0 200000 0 200000 0 0 0 0 0 0 0 0 0 0 27.3 0 0 0 5.6 0 4.56667 1.43333 0 3 0 7.73333 8.96667 31.2667 0 13.0333 0 9.6 30.0333 1.53333 2.06667 0.83333 0 10.4333 0 0 0 0 4 1.4 0 0.16667 0 0 3.96667 9.1 0 0 2.26667 0.73333 2.2 0 25 0 0 0 15 0 26 20 28 20 0 30 30 30 0 26 0 26 28 26 8 26 0 26 0 0 0 0 8 25 0 15 0 0 26 30 0 0 30 15 30 0 60 0 0 0 40 0 48 30 48 64 0 98 56 60 0 54 0 36 48 48 22 48 0 66 0 0 0 0 72 20 0 40 0 0 48 48 0 0 104 48 70 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 21 21 24 23 25 21 20 22 24 20 22 20 24 27 20 22 23 22 22 25 21 22 20 23 21 20 21 20 21 20 26 25 20 22 24 22 21 20 21 21 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 381500 0 180000 0 0 0 160000 360000 0 381500 0 300000 240000 0 300000 0 0 306000 0 450000 160000 250000 0 381500 181000 0 0 408000 408000 408000 152000 0 408000 0 408000 553000 170000 650000 186000 100000 400000 300000 0 0 0 360000 150000 0 0 0 160000 0 0 0 0 0 0 0 0 0 150000 0 0 0 75000 0 0 0 200000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.5 0 10.4667 11.3 12.6333 0 3.06667 12.8667 5.93333 1.53333 0 0.96667 1 0 1.46667 0 0 6.13333 54.1667 9.3 7.4 0.93333 1.36667 21.2667 0 18.3333 0.1 5.83333 2.03333 3.13333 0 2.86667 0 13.1333 1.56667 25.6 17.1667 4.43333 9.6 2.03333 2.33333 30 0 30 30 30 0 30 30 7 26 0 23 15 0 27 0 0 20 15 26 28 20 15 30 18 0 26 28 30 26 8 0 26 0 26 26 12 30 15 28 26 26 56 0 49 84 70 0 96 80 21 72 0 56 42 0 90 0 0 40 12 84 70 40 84 60 90 0 60 60 100 48 20 0 48 0 48 48 30 72 48 70 60 48 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 22 22 23 23 21 20 22 25 20 21 24 21 25 27 24 21 26 25 21 23 27 22 25 21 23 21 20 26 27 21 20 20 21 23 20 25 26 25 26 23 24 23 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 1 1 1 600000 300000 408000 120000 100000 100000 0 200000 0 0 280000 140000 500000 0 210000 120000 0 381500 300000 350000 408000 408000 408000 556000 160000 0 600000 180000 120000 408000 0 0 250000 0 0 500000 408000 0 0 350000 170000 408000 0 0 0 0 0 0 0 0 400000 80000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 200000 0 0 0 0 0 0 140000 0 0 0 0 13.2 12.3333 8.6 5.1 8.63333 3.73333 0 11.1333 9.6 10.4 11.9333 0.7 0.2 0 2.03333 10.2333 0 2.16667 15.9 17.9 6 0.8 0.3 3.23333 1.43333 0 27.1 0.3 5.03333 12.8 2.23333 0 17.2333 0 0 3.5 9.73333 0.43333 0 17.4 6.03333 31.3 28 27 30 8 26 26 0 20 26 15 30 26 26 0 26 30 0 20 28 6 30 30 4 20 26 0 30 15 8 26 12 0 26 0 0 30 28 6 0 20 28 28 56 72 60 10 30 52 0 25 70 20 10 36 48 0 60 70 0 56 72 48 56 56 32 40 30 0 49 66 98 48 9 0 72 0 0 70 56 24 0 48 72 84 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 20 22 21 25 21 21 22 22 22 20 20 22 22 22 20 25 24 26 21 23 23 21 19 26 22 20 21 22 26 21 25 23 21 20 22 22 25 23 20 21 24 25 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 250000 0 0 0 0 381500 640000 0 130000 0 0 150000 650000 540000 0 0 408000 0 0 70000 500000 400000 408000 0 0 0 0 0 0 48000 0 240000 452000 280000 408000 240000 0 0 400000 381500 408000 381500 0 0 0 90000 0 0 0 0 0 300000 0 0 0 0 100000 0 0 350000 0 0 0 0 0 0 0 0 0 360000 450000 0 100000 0 0 0 0 0 0 0 0 0 0 0 9.03333 0 5.1 0.5 0 1.53333 13.3 0 20.2 21.4333 0 5.26667 1.03333 1.36667 8.93333 0 9.13333 4.16667 0 0.5 10 8.43333 1.63333 0 0 7.06667 0 18.1667 33.3 7.13333 12.9 1.56667 8.53333 55.7667 0.76667 0.93333 0 0 10.3667 4.23333 2.9 0.53333 17 0 20 20 0 30 30 0 15 20 0 10 30 30 15 0 26 20 0 15 30 28 30 0 0 26 0 26 26 8 30 23 26 28 24 26 0 0 20 26 26 22 105 0 40 40 0 56 84 0 84 18 0 24 84 84 105 0 72 60 0 32 45 70 56 0 0 42 0 60 84 20 42 40 48 54 54 72 0 0 98 40 48 48 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 21 21 20 23 22 21 22 22 21 26 20 23 21 22 21 26 26 22 20 20 24 20 24 24 23 20 21 21 20 22 20 26 24 22 25 23 23 26 23 20 23 26 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 74800 0 170000 0 0 180000 400000 200000 0 540000 0 440000 160000 0 0 0 0 200000 0 0 381500 0 0 350000 0 150000 0 470000 150000 0 396000 408000 0 400000 80000 200000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 300000 100000 200000 450000 0 200000 0 0 0 0 0 150000 0 350000 0 0 200000 0 0 28000 0 0 0 0 0 0 0 0 0 1.53333 0 3.2 0 0 1.13333 1.4 8.66667 0 5.43333 0 1.16667 0.46667 8.3 0 6.56667 39.0667 0.33333 8.36667 0 3.03333 0 0 2.1 14.6 12.5333 4.5 10.3333 6.9 43.3333 11.5 9.93333 1.83333 12.7667 4.3 0.43333 0 0 0 0 0 0 4 0 15 0 0 25 26 20 0 24 0 24 26 26 2 20 10 25 30 0 28 0 0 28 15 26 26 30 26 20 26 28 12 26 2 20 0 0 0 0 0 0 10 0 86 0 0 91 48 40 0 72 0 48 48 48 13 30 16 66 84 0 84 0 0 96 48 54 60 90 36 48 48 54 28 48 20 40 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 21 24 23 21 24 22 23 25 23 20 23 25 22 21 24 26 22 26 26 22 21 25 25 21 27 23 21 24 21 20 22 20 23 25 23 22 21 23 22 23 23 22 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 0 160000 0 325000 500000 0 0 0 408000 408000 0 408000 0 0 600000 0 160000 408000 0 250000 0 250000 200000 160000 0 0 408000 0 650000 0 600000 160000 0 300000 0 350000 308000 50000 408000 408000 160000 620000 0 0 250000 0 0 200000 0 350000 0 0 0 0 0 280000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.1 3.33333 9.03333 12.6333 14.6333 0 39.6333 2.06667 1.96667 0 4.03333 0 0.53333 39.5667 0 3 3.7 0 3.13333 20.8667 2.46667 1.2 1.06667 0 0 14.2 0 2.46667 0 12.0667 9.2 0 0.93333 0 3.26667 2.03333 0.26667 14.5 8.6 1.1 1.3 0 20 30 25 28 15 0 27 26 29 0 26 0 25 26 0 26 28 0 26 30 26 20 20 0 0 26 0 28 0 26 25 0 28 0 30 26 8 26 26 20 24 0 63 90 48 60 40 0 24 84 70 0 48 0 30 48 0 48 84 0 24 70 54 60 10 0 0 54 0 56 0 84 60 0 70 0 56 48 84 48 48 48 56 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 1 0 1 0 26 23 26 24 21 25 24 20 23 22 23 21 21 21 23 23 25 24 23 22 20 23 24 22 25 24 25 20 25 26 25 20 24 21 24 24 24 20 21 22 25 25 1 1 1 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 600000 381500 0 250000 150000 0 0 360000 408000 381500 0 0 100000 0 0 140000 0 500000 381500 480000 127000 0 180000 0 0 0 0 0 408000 0 0 168000 0 408000 55000 408000 600000 55000 408000 408000 600000 408000 0 0 0 0 0 70000 160000 0 0 0 0 0 0 60000 300000 0 0 0 0 0 0 100000 0 0 200000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27.2 2.33333 0 18.9 1.3 0.53333 0 15.6667 12.6667 2.56667 0 0 12.5333 18.0333 9.93333 7.16667 20.6667 2.5 0.53333 5.1 7.2 51.5333 3.33333 0 9.56667 0 0 0 7.5 0 0 9.8 0 11.7 2.5 3.9 5.86667 3.46667 13.8667 8.63333 13.7 0.96667 30 27 0 16 26 7 26 30 30 26 0 0 26 30 8 20 20 30 30 26 28 26 28 0 30 0 0 0 20 0 0 26 0 26 26 26 28 26 26 23 20 26 86 72 0 84 60 70 36 105 70 72 0 0 42 36 6 20 40 54 70 96 60 60 84 0 98 0 0 0 30 0 0 24 0 72 48 48 48 48 48 48 48 66 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 1 1 22 22 22 23 25 22 22 24 24 23 24 26 24 21 24 21 24 23 22 20 26 23 22 25 21 21 22 21 24 22 22 26 23 21 21 27 23 26 25 20 21 24 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 55000 810000 408000 408000 730000 408000 381500 0 0 408000 0 500000 408000 408000 900000 381500 0 450000 156000 550000 0 0 430000 0 408000 140000 408000 408000 408000 600000 190000 200000 0 300000 450000 0 900000 700000 0 408000 200000 408000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50000 0 0 0 80000 0 0 0 0 0 0 0 0 0 0 0 250000 0 0 0 0 0 0 0 0 0 15.6333 11.4 12.0667 18.2667 1.2 16.1667 5.1 0 0 7.63333 0 3.36667 14.8 3.83333 4.56667 2.5 0.16667 13.2667 0 3.56667 16.5667 0 6.26667 0 12.2667 0.53333 12.6 4.43333 2.26667 7.56667 2.4 7.6 18.3667 0.5 4.63333 0 2.93333 4.06667 0 13.3 9.16667 10.1 26 20 22 20 30 26 30 0 0 20 0 20 26 26 24 26 2 24 7 26 4 0 28 0 22 26 26 26 26 26 26 22 22 28 20 0 26 28 0 24 15 20 48 45 40 40 73 48 70 0 0 20 0 48 48 48 64 48 9 48 48 38 8 0 56 0 50 42 48 48 48 60 36 30 40 77 40 0 48 72 0 60 32 59 0 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 0 1 0 1 21 21 23 25 24 25 21 22 24 27 23 23 21 23 25 27 20 22 23 24 20 22 23 21 20 23 26 23 23 21 22 26 25 26 23 23 20 23 21 27 24 23 0 0 1 0 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 55000 320000 408000 408000 0 100000 400000 240000 460000 120000 0 408000 0 0 408000 500000 240000 0 420000 408000 480000 190000 0 0 300000 0 408000 408000 600000 408000 381500 500000 0 700000 640000 408000 340000 400000 408000 350000 385000 550000 0 0 0 0 0 0 0 0 0 0 0 0 132000 50000 0 0 0 240000 0 0 0 0 400000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.6333 1.53333 2.6 14 0 2.3 4 28.4333 4.56667 2.46667 0 0 1.5 13.8333 7.13333 4.86667 12.6 31.8 13.5333 5.03333 1.43333 79.9 0 1.2 0 15.6 8.93333 8.6 4.26667 2.5 11.3333 0 10.4667 7.56667 2.9 6.06667 2.8 2.46667 5.33333 17.9667 9.96667 26 24 22 26 0 26 28 22 24 8 0 15 20 8 20 26 12 20 26 26 20 15 26 0 15 0 30 22 26 30 26 26 0 26 22 22 28 26 26 22 29 22 48 54 54 48 0 48 57 22 48 14 0 48 30 15 42 48 36 35 48 48 53 40 78 0 63 0 42 40 66 48 48 80 0 55 48 48 91 48 48 60 70 48 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 1 1 1 0 0 1 1 1 22 21 23 22 21 25 21 27 24 23 22 26 21 22 22 22 23 21 21 22 23 23 20 23 21 22 27 24 22 21 24 23 26 22 20 22 20 22 22 22 24 20 0 0 1 0 0 0 1 1 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 300000 314000 300000 408000 0 408000 350000 470000 32000 450000 260000 448000 0 220000 408000 150000 408000 0 150000 0 350000 408000 0 120000 0 300000 0 150000 0 120000 600000 408000 420000 408000 0 0 0 0 408000 0 408000 480000 0 0 0 0 0 0 0 0 0 0 0 0 150000 0 0 0 0 0 0 0 0 0 0 0 0 0 50000 0 80000 0 0 0 0 0 0 0 0 0 0 0 0 0 39.2667 8.93333 9.73333 1.96667 0 2.43333 15.1333 2.03333 1.86667 7.06667 12.2333 9 0.36667 1.83333 2.06667 3.16667 5.3 0 6.2 0 15.6333 11.7333 0 24.3333 0 0.83333 6.13333 6.03333 8.1 1.53333 2.13333 13.7667 4.6 7.83333 0 0 0 0 13.2 0 11.9 1.53333 28 26 30 26 0 26 30 26 2 26 26 30 30 22 26 20 24 0 7 0 26 26 0 8 0 30 26 30 22 26 20 26 26 26 0 0 0 0 26 0 28 26 84 42 80 48 0 48 70 48 20 48 36 64 16 55 48 48 54 0 30 0 60 48 0 13 0 84 48 70 20 48 40 48 48 48 0 0 0 0 48 0 48 66 0 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 22 20 21 25 24 23 20 20 23 22 22 23 24 25 20 23 20 24 19 22 27 23 22 23 22 24 20 25 23 21 20 21 23 26 20 21 25 21 21 20 22 20 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 270000 0 0 1 408000 0 0 1 500000 0 0 1 360000 1 300000 1 360000 1 408000 1 381500 1 408000 1 408000 1 440000 1 300000 1 460000 1 408000 1 350000 0 0 1 390000 1 500000 1 408000 1 480000 1 350000 1 315000 1 408000 1 408000 1 408000 1 120000 ...

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