5.08 1986 Year Male, yean Female ye Difference F-M. years 1980 65 90 70 54 464 1961 66.27 70.94 1962 66.60 7130 4.89 196

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5 08 1986 Year Male Yean Female Ye Difference F M Years 1980 65 90 70 54 464 1961 66 27 70 94 1962 66 60 7130 4 89 196 1 (31.63 KiB) Viewed 22 times

5 08 1986 Year Male Yean Female Ye Difference F M Years 1980 65 90 70 54 464 1961 66 27 70 94 1962 66 60 7130 4 89 196 2 (70.61 KiB) Viewed 22 times

5.08 1986 Year Male, yean Female ye Difference F-M. years 1980 65 90 70 54 464 1961 66.27 70.94 1962 66.60 7130 4.89 1963 68.87 7161 4.74 1964 67.00 71.00 480 1965 67.26 72.14 488 1968 6742 7230 4.96 1967 67.59 72.54 1968 67.81 72.99 5.12 1969 68.06 73.24 5.18 1970 68.35 73.59 5.24 1971 68,65 73 93 5.28 1972 68.94 74 27 5.33 1973 69.19 74.57 5.38 1974 09.41 74.85 5.44 1975 69.60 75.12 5.52 1978 69.78 7539 5.61 1977 69.97 75.70 5.73 1978 70.17 78.03 5.86 1979 70.40 76.40 6.00 1980 70.65 76.79 6.14 1981 70.91 77.18 6.27 1982 71.16 77.56 6.40 1983 7140 77.89 6.49 1954 71.63 78.18 6.55 1985 71.88 78.44 6.56 72.16 78.66 6.50 1987 7248 78.87 6.38 1988 7285 79.07 1989 74.20 79.30 5.10 1990 74.50 79.50 5.00 1991 74.60 79.80 5.20 74.70 80.20 5.50 1993 75.00 79.90 490 1994 75.20 80.20 5.00 1995 75.00 80 30 5.30 1996 75.10 80.40 5.30 1997 75.60 80.80 5.20 1996 80.40 5.00 1999 75.50 80 80 5.10 2000 75.40 80.50 5.10 2001 75.90 81.00 5.10 2002 78.30 81.10 430 2003 76.50 81.30 4.80 78.60 81.60 5.00 2005 78.80 81.80 5.00 2005 77.00 5.00 2007 77.00 8200 5.00 2000 77.50 82.50 5.00 2009 77.70 82.80 5.10 77.90 B3.00 5.10 2011 78.00 83.60 5.60 2012 78.00 83.40 2013 78.70 14.00 5.30 2014 78.80 84.10 5.30 2015 78.50 83.70 5.20 2016 78.90 34.00 5.10 2017 78.80 83.90 5.10 2018 79.30 84.40 5.10 621 1992 75.40 2004 82.00 2010 5.40 Mean Sid. dov. Q1 Q2 73.03 4.08 66.51 74.20 75.55 7.04 10.57 78,38 4.07 74.90 79,30 8145 6.46 9.70 5.34 0.53 5.00 5.18 551 051 0.76 IOR (9.5) IOR

VS Instructions. When answering each question, please make sure to include relevant math- ematical justification (aimed at the statistically-inclined reader) as well as a common sense interpretation tailored to the general audience. 1. You already know that the population is that of years, and the spreadsheet you chose represents a sample summarizing data about a specific country. Looking at the data, do you think that the sample was taken using the SRS (simple random sampling) technique? Please explain. 2. Calculate the p-value associated with the hypothesis testing problem about the difference between the two sub-groups (either male/female or urban/rural, depending on which dataset you chose): Ho difference = 0 Ho difference +0. Based on your calculations, do you think there is evidence to support the claim that there is going to be a difference between the two categories in 2023? Hint: It does not matter what the variable of interest is called or how we constructed it. We can use the same techniques when working with sample mean of any variable be it an absolute value, or a difference between other variables. Just focus on one column, and work with it as if there were no other columns in the table. 3. Reflection. Try to think about this project outside the context of statistics. Think about the bigger picture. Specifics aside, reflect on what these data mean to you. Do you think this project affected your view/understanding of the world in any way? P.S. Please be honest, and do take some time thinking about it. The effort is all that counts for this question. Bonus questions. 1. Construct a 95%-confidence interval for the expected difference between the two sub- groups. Explain what it means, and how you interpret it. List all your assumptions, as well as any concerns you might have regarding your conclusion 2. Would it make sense to construct confidence intervals for individual sub-group values (rather than for the difference), and then compare the two confidence intervals to see if they overlap? Explain why, or why not.