Name: DATA Set: Indicate one: [Apartments Directions: Fill in the answer to the questions posed below. Attach the specied printouts Mate indicated and upload the project in Canvas on or before the deadline. Please give specific interpretations as generic answers will not receive full credit. 1. Unemployment is a big concern due to people being laid off due to the Coronavirus pandemic. It is estimated that 20% of all households have been directly affected. Suppose we randomly sample 25 households and determine the number of households that have been directly affected. a. Identify the type of distribution that can be used to analyze this data. Make sure to identify any parameters that would be needed in this identication (ex. N, p, p, a). b. What is the probability that at least 6 of these 25 sampled households has been directly affected? Show work to get full credit. c. What is the probability that at least 4 but no more than 8 of the sampled household has been directly affected? Show work to get full credit. 2. Provide a printout in which you find the Descriptive Statistics Here mean and standard deviation for the 75 sizes/mileages of your data set. Also, list your size/mileage for your 20th and your 40th observation below. For all of question 2, treat these values as the mean and standard deviation of the population when answering questions. Size/Mileage for Observation #20: Size/Mileage for Observation #40: a. For this problem, assume that the distribution for all sizes/mileages in the population are normally distributed (may not really be true). Find the proportion of all sizes/mileages that would fall between the sizes/mileages of your 20th and 40th observations. To receive full credit, you should include pictures of the normal curve (labeled with both x and z- values) with the pertinent probabilities shaded in the picture. Zb. Assuming the distribution of the sizes/mileages are normal, use the mean and standard deviation you found above to locate the 9th percentile in the size/mileage distribution. To receive full credit, you show as much work as possible. c. Suppose you take a future sample of 100 sizes/mileages from your population. Describe the sampling distribution for the x-bar value that you would find. (hint: there should be three parts to this answer) d. Use the sampling distribution to find the probability that the sample mean from that future sample would be more than the size/mileage for Observation #20. To receive full credit, you should include pictures of the normal curve (labeled with both x-bar and z- values) with the pertinent probabilities shaded in the picture.3. Use two of the techniques discussed in class to assess the normality of the sizefmileage distribution from your data set. You can choose the two techniques you use. Just make sure you show us enough information (including printouts) to check your work. APPENDIX D O Tables 795 Table I (continued) e. n = 9 K 01 05 10 .20 .30 .40 .50 .60 .70 .80 .90 95 .99 .914 630 387 134 040 .010 .002 .000 .000 .000 .000 000 000 .997 929 .775 .436 196 071 020 .004 000 000 000 1.000 992 947 000 .738 46 232 .090 025 .004 000 .000 1.000 00 000 .999 .992 .914 .730 483 .254 099 025 003 000 1.000 1.000 000 000 .999 ONOUIAWNG 980 .901 .733 .500 267 .099 020 .001 1.000 1.000 1.000 000 0OO .997 975 .901 .746 .517 270 086 .008 .001 000 1.000 1.000 1.000 1.000 996 .975 910 .768 .537 262 .053 008 000 1.000 1.000 1.000 1.000 1.000 996 980 .929 804 .564 .225 1.000 071 00 1.000 1.000 1.000 1.000 1.000 998 .990 .960 866 .613 370 086 f. n = 10 K 01 05 10 .20 30 .40 .50 .60 70 80 .90 .95 99 904 .599 .349 .107 028 .006 .001 .000 .000 .000 .000 .000 000 .996 .914 .736 .376 .149 046 011 .002 000 000 000 000 000 1.000 988 930 678 383 167 .055 .012 .002 .000 1.000 00 000 .999 .987 879 650 382 .172 .055 011 001 .000 1.000 1.000 998 000 000 967 .850 .633 .377 .166 .047 006 000 000 1.000 1.000 1.000 994 000 .953 834 .623 .367 .150 033 .002 000 000 1.000 1.000 1.000 999 989 945 828 .618 .350 121 013 001 000 1.000 1.000 1.000 1.000 998 988 945 833 .617 .322 .070 012 000 1.000 1.000 1.000 1.000 1.000 .998 989 .954 .851 .624 1.000 .264 1.000 1.000 1.000 086 004 1.000 1.000 .999 .994 .972 .893 .651 .401 096 g. n = 15 P 01 .05 .10 .20 .30 40 50 60 .70 .80 .90 .95 .99 860 .463 .206 035 .005 000 000 000 000 000 000 000 000 990 829 .549 .167 035 .005 000 000 000 000 .000 000 1.000 000 964 .816 .398 .127 .027 004 000 000 .000 000 1.000 .995 944 000 000 648 .297 091 018 002 000 000 000 000 1.000 .999 987 JUU 838 .515 .217 059 009 .001 000 .000 000 000 1.000 1.000 .998 939 .722 .403 .151 034 .004 000 000 1.000 000 000 1.000 1.000 .982 869 610 304 095 015 .001 000 000 1.000 1.000 1.000 996 .950 .787 .500 213 .050 .004 .000 1.000 000 1.000 1.000 000 999 985 905 .696 .390 .131 018 000 000 1.000 1.000 1.000 00O 1.000 .996 .966 .849 597 .278 .061 .002 1.000 00 000 1.000 1.000 1.000 999 991 941 .783 .485 .164 013 001 000 1.000 1.000 1.000 1.000 1.000 998 982 909 .703 .352 .056 .005 000 1.000 1.000 1.000 1.000 1.000 1.000 996 973 .873 .602 .184 036 1.000 000 1.000 1.000 1.000 1.000 1.000 1.000 .995 .965 .833 .451 1.000 1.000 1.000 1.000 171 .010 1.000 1.000 1.000 1.000 .995 .965 .794 .537 140 (continued)796 APPENDIX D Tables Table I (continued) h. n = 20 k 01 .05 .10 20 30 40 .50 .60 .70 .80 .90 .95 .99 818 358 122 012 001 000 000 000 000 000 000 000 .000 983 .736 .392 069 .008 001 000 000 00 000 00 000 000 .999 925 677 206 035 004 000 .000 000 000 000 000 000 1.000 984 867 411 107 .016 001 000 000 000 000 000 1.000 997 957 .630 .238 051 006 000 000 0OO 000 000 000 1.000 1.000 989 804 .416 .126 .021 002 000 000 000 000 000 1.000 1.000 866 .913 608 250 .058 .006 .000 000 00 00 .000 1.000 1.00 1.000 968 772 .416 132 021 001 000 000 000 .000 1.000 1.000 1.000 .990 .887 .596 .252 .057 .005 00 000 00 000 1.000 1.000 1.000 997 952 .755 412 128 017 001 000 000 000 10 1.00 1.00 1.000 999 .983 .872 588 .245 .048 003 0OO 000 OOO 1.000 1.000 1.000 1.00 995 .943 .748 .404 113 010 000 000 .000 1.000 1.000 1.000 1.000 999 979 868 584 228 032 000 000 .000 1.00 1.000 1.000 1.000 1.000 994 942 .750 .392 087 002 000 .000 1.000 1.000 1.000 1.000 1.000 998 979 874 .584 196 011 000 000 1.00 1.000 1.000 1.000 1.000 1.000 994 949 762 370 .043 00 000 16 1.000 1.000 1.000 1.000 1.000 1.000 .999 984 .893 .589 133 016 000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 996 965 794 .323 18 075 001 1.000 1.000 1.000 1.000 1.000 1.000 1.000 999 992 .931 .608 .264 .017 1.00 1.000 1.000 1.000 1.000 1.000 1.000 1.000 .999 .988 878 .642 182 i. n = 25 D k .01 .05 10 .20 30 40 .50 .60 .70 .80 .90 .95 99 778 .277 072 004 000 000 000 .000 000 OOC 000 .000 .000 974 642 .271 027 .002 000 000 000 000 000 000 000 .998 873 .537 098 009 000 000 .000 .000 .000 .000 .000 .000 1.000 966 .764 234 033 002 .000 000 000 000 000 .000 .000 1.00 993 .902 .421 090 009 000 .000 000 .000 000 .000 000 1.000 999 .967 617 .193 029 002 000 000 000 000 .000 000 1.00 1.000 991 .780 .341 .074 .007 .000 000 000 000 000 000 1.000 1.000 998 .891 .512 .154 .022 001 000 .000 .000 000 .000 1.00 1.00 1.000 953 .677 .274 054 .004 000 000 000 000 000 1.000 1.000 1.000 983 .811 425 .115 .013 .000 .000 .000 .000 .000 1.000 1.000 1.000 994 .902 586 .212 .034 002 .000 .000 000 .000 1.00 1.000 1.000 998 956 732 345 .078 .006 000 000 .000 000 1.000 1.000 1.000 1.000 983 .846 .500 .154 .017 000 000 .000 000 1000 1.000 1.000 1.000 994 922 655 268 .044 002 000 000 000 1.00 1.000 1.00 1.00 998 .966 788 .414 098 .006 .000 .000 .000 1.000 1.000 1.000 1.000 1.000 .987 .885 .575 .189 017 .000 .000 1.00 1.000 1.000 1.000 1.00 .996 .946 726 .323 .047 .000 .000 .000 1.000 1.000 1.000 1.000 1.000 .999 .978 846 .488 .109 .002 DOO .000 1.000 1.000 1.000 1.000 1.000 1.000 993 926 .659 .220 .009 .000 .000 1.000 1.000 1.000 1.000 1.000 1.000 .998 971 807 .383 .033 .001 .000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 991 .910 579 .098 .007 .000 1.00 1.000 1.000 1.000 1.000 1.000 1.00 998 .967 .766 .236 .034 .000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 991 .902 .463 .127 .002 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 .998 973 .729 .358 .026 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 996 .928 .723 222