399.1 395 530.7 495.1 476.8 460.9 480.9 450.4 472.7 453.5 446.2 567.3 428.2 547.2 507.8 383 453.5 507.7 506 473.8 392.3 450.7 464.5 403.4 465.8 534.2 471.7 507.7 490.2 481.7 492.4 477.2 507.7 476.8 464.7 390.2 481.7 469 515.7 519.4 396.3 517.2 493.9 515.2 486.5 460.3 475.4 445.5 467.7 531 532.7 486.4 533.8 453.5 Find the first quartile for this data set. Q1 = Find the third quartile for this data set. Q3 = Find the interquartile range for this data set. IQR =
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hw 2
| 399.1 | 395 | 530.7 | 495.1 | 476.8 | 460.9 |
| 480.9 | 450.4 | 472.7 | 453.5 | 446.2 | 567.3 |
| 428.2 | 547.2 | 507.8 | 383 | 453.5 | 507.7 |
| 506 | 473.8 | 392.3 | 450.7 | 464.5 | 403.4 |
| 465.8 | 534.2 | 471.7 | 507.7 | 490.2 | 481.7 |
| 492.4 | 477.2 | 507.7 | 476.8 | 464.7 | 390.2 |
| 481.7 | 469 | 515.7 | 519.4 | 396.3 | 517.2 |
| 493.9 | 515.2 | 486.5 | 460.3 | 475.4 | 445.5 |
| 467.7 | 531 | 532.7 | 486.4 | 533.8 | 453.5 |
Find the first
Q1 =
Find the third quartile for this data set.
Q3 =
Find the
IQR =
Step by step
Solved in 3 steps with 2 images
- Find the positive geometric mean between 4 and 64.Crime for 2019 728 294 43,836 46 16,251 6,345 21,678 62,977 65 10,519 19 405 44 1,056 69,062 10,868 4,807 203 2,519 3,877 409 4,848 4,038 Crime for 2020 252 539 48,462 63 16,400 4,148 23,816 54,932 81 9,522 13 640 27 1,182 63,720 12,451 2,465 98 2,897 3,651 306 5,004 3,659 Crime for 2011 426 26,978 67 22,028 8,924 18,634 35,665 0 27 7,100 10 223 459 55,765 4,400 2,590 112 1,774 8,033 550 3,401 1,723PercentUnder 21 Fatal Accidentsper 1000 13 2.962 12 0.708 8 0.885 12 1.652 11 2.091 17 2.627 18 3.83 8 0.368 13 1.142 8 0.645 9 1.028 16 2.801 12 1.405 9 1.433 10 0.039 9 0.338 11 1.849 12 2.246 14 2.855 14 2.352 11 1.294 17 4.1 8 2.19 16 3.623 15 2.623 9 0.835 8 0.82 14 2.89 8 1.267 15 3.224 10 1.014 10 0.493 14 1.443 18 3.614 10 1.926 14 1.643 16 2.943 12 1.913 15 2.814 13 2.634 9 0.926 17 3.256 D. Give a 95% interval estimate of the number of fatal accidents per 1000 licenses ina city where 15% of the licensed drivers are under 21
- The following list gives the gross federal debt(in millions of dollars) for the U.S. every 5 years from 1945 to 2000: Year Gross Federal Debt ($millions) 1945 260,123 1950 256,853 1955 274,366 1960 290,525 1965 322,318 1970 380,921 1975 541,925 1980 909,050 1985 1,817,521 1990 3,206,564 1995 4,921,005 2000 5,686,338 Construct a scatter plot with this data. Do you observe a trend? If so, what type of trend do you observe? Use Excel to fit a linear trend and an exponential trend to the data. Display the models and their respective r^2. Interpret both models. Which model seems to be more approprate and why?Male Female 28,429 21,890 14,798 23,569 5,443 6,800 26,258 16,895 26,723 10,669 6,907 14,890 23,016 13,017 19,009 17,690 25,050 14,742 21,192 20,420 10,338 14,333 10,465 14,395 14,010 16,864 20,648 23,820 19,856 4,245 63 19,225 21,420 23,626 11,319 17,292 20,356 15,343 17,492 13,514 16,299 28,660 21,793 10,057 7,288 18,481 7,960 5,205 25,062 9,611 10,186 17,995 13,921 13,983 12,942 21,676 11,655 22,780 19,207 11,796 12,895 33,935 7,911 9,825 20,749 11,633 17,120 10,489 12,477 30,066 13,810 20,778 17,041 18,452 11,364 20,982 20,462 21,013 10,940 15,980 13,828 17,134 5,741…part C D 33000331 UPH-1 St. Lawrence dolomite 618 0.05 2.69 2.74 2.83 48000168 Langer Platteville dolomite 127.8 0.06 2.69 2.75 2.86 52000415 3-21-13z1 Galena dolomite 575 0.04 2.72 2.76 2.83 52000415 3-21-13z1 Platteville dolomite 740 0.05 2.7 2.75 2.85 60000086 VD #1 Silurian dolomite 684 0.03 2.75 2.78 2.83 60000086 VD #1 Galena dolomite 1070 0.05 2.69 2.74 2.84 60000086 VD #1 Platteville dolomite 1142 0.02 2.77 2.79 2.83 13001466 NS-2 Tunnel City sandstone 60.1 0.14 2.41 2.54 2.79 13001466 NS-2 Tunnel City sandstone 60.2 0.28 1.93 2.21 2.67 13001466 NS-2 Tunnel City sandstone 118.3 0.3 1.88 2.18 2.7 33000331 UPH-1 St. Peter sandstone 222 0.22 2.06 2.28 2.65 33000331 UPH-1 Jordan sandstone 531 0.21 2.09 2.3 2.65 33000331 UPH-1 Tunnel City sandstone 644 0.18 2.22 2.4 2.7 33000331 UPH-1 Wonewoc sandstone 735 0.2 2.12 2.32 2.65 33000331 UPH-1 Wonewoc sandstone 840 0.27 1.93 2.2 2.66 33000331 UPH-1 Mt. Simon sandstone 927 0.18 2.31 2.49 2.81…
- 0.7200.7400.6400.3900.7002.2001.9800.6401.2200.2001.6401.3402.9500.9001.7601.0101.2600.0000.6501.4601.6201.8300.9901.5600.4101.2800.8301.3200.5401.2500.9201.0000.7800.7901.4401.0002.2402.5001.7901.2501.4900.8401.4201.0001.2501.4201.3500.9300.4001.390 The accompanying data table lists the magnitudes of 50 earthquakes measured on the Richter scale. Test the claim that the population of earthquakes has a mean magnitude greater than 1.00. Use a 0.01significance level. Identify the null hypothesis, alternative hypothesis, test statistic, P-value, and conclusion for the test. Assume this is a simple random sample the sample data is above. What are the hypotheses? A. H0: μ=1.00 in magnitude H1: μ≠1.00 in magnitude B. H0: μ=1.00 in magnitude H1: μ<1.00 in magnitude C. H0: μ=1.00 in magnitude H1: μ>1.00 in magnitude D. H0: μ≠1.00 in magnitude H1: μ=1.00 in magnitude Identify the test statistic. t= (Round to two decimal places as…2) . In February 2021, the Chicago Tribune reported:“The overdose epidemic that has ravaged Chicago has not spared the nearby suburbs, some of which have death rates comparable to anything seen in the city, according to a report released this week by the Cook County Department of Public Health. ZIP codes in the towns of Worth, Broadview, Maywood, and Forest Park, areas that “have substantially lower median household incomes and higher poverty rates,” have been especially hard-hit, the report said.”In the article, they claimed that lower incomes were associated with higher overdose death rates and that overdose death rates are increasing over time. The data you have been analyzing wasn’t focused on Chicago, obviously. But do your results support the general gist of the statistical argument being made in this Tribune excerpt? Please address:• Do overdose death rates appear to be associated with per capita income (use scatterplot)?• Do overdose death rates appear to be increasing over…Group 1 Group 2 Number in Group 1,900 1,900 Number who are expected to have a heart attack without the drug Number who are expected to have a heart attack with the drug What percentage of the group is expected to have a heart attack with the drug?
- Date Ford stock Price y Japanese Yen x1 Deutsche Mark x2 S&P500 x3 1992.1 38.3 133.2 1.64 407.36 1992.2 45.2 125.5 1.53 408.21 1992.3 39.4 119.2 1.41 418.48 1992.4 42.7 124.7 1.61 435.64 1993.1 52.3 121.3 1.61 450.16 1993.2 55.3 110.1 1.69 447.29 1993.3 64.7 105.2 1.62 459.24 1993.4 58.1 111.3 1.73 465.34 1994.1 59.9 103.4 1.76 463.21 1994.2 27.6 99.5 1.6 454.83 1994.3 27.5 98.5 1.55 466.96 1994.4 26.3 99.6 1.38 455.19 1995.1 29.5 89.4 1.39 493.15 1995.2 31.2 84.6 1.42 539.23 1995.3 28.7 98.2 1.43 578.32 1995.4 28.9 102.4 1.48 614.57 1996.1 34.8 106.3 1.52 647.05 1996.2 36.2 109.8 1.58 668.53 1996.3 32.1 110.5 1.61 672.92 1996.4 33.9 112.8 1.69 689.35ID Age (yrs) Sex: 0: male 1: female Height (in) Family Income status: Low (0), Middle (1), High (2) 301 9 1 57.0 1 451 8 1 69.5 1 501 7 0 70.5 2 642 9 0 50.0 2 901 10 0 57.0 1 1701 8 0 62.0 0 1752 6 1 58.0 0 1753 6 1 58.5 0 1901 8 1 60.0 1 1951 9 1 53.0 1 1952 6 1 54.0 2 2001 10 0 58.5 0 2101 8 0 61.5 2 The data above are a sample of 13 children from a larger data set collected on children born in 2019. For the sex variable, a code of 0 represents male, 1 represents female. What is the range of children’s height? What is the interquartile range of children’s height?ID Age (yrs) Sex: 0: male 1: female Height (in) Family Income status: Low (0), Middle (1), High (2) 301 9 1 57.0 1 451 8 1 69.5 1 501 7 0 70.5 2 642 9 0 50.0 2 901 10 0 57.0 1 1701 8 0 62.0 0 1752 6 1 58.0 0 1753 6 1 58.5 0 1901 8 1 60.0 1 1951 9 1 53.0 1 1952 6 1 54.0 2 2001 10 0 58.5 0 2101 8 0 61.5 2 Use graphic methods to compare children’s height by income status. What is your interpretation of the data?
