The following bivariate data set contains an outlier. 12.4 -410.6 13 -1023.1 17 1871.8 57.6 963.8 32.8 -915.7 17.5 -825.8 20.6 1984.8 48.9 -3054.2 47.1 -545.8 59.4 -1075.9 56.1 -3731.6 42.8 2552.5 34.9 -2246.9 32.9 1427.8 200.4 1088 What is the correlation coefficient with the outlier? "w = What is the correlation coefficient without the outlier? rwo = Would indusion of the outlier change the evidence for or against a significant linear correlation? O No. Induding the outlier does not change the evidence regarding a linear correlation. Yes. Induding the outlier changes the evidence regarding a linear correlation.

The following bivariate data set contains an outlier. 12.4 -410.6 13 -1023.1 17 1871.8 57.6 963.8 32.8 -915.7 17.5 -825.8 20.6 1984.8 48.9 -3054.2 47.1 -545.8 59.4 -1075.9 56.1 -3731.6 42.8 2552.5 34.9 -2246.9 32.9 1427.8 200.4 1088 What is the correlation coefficient with the outlier? "w = What is the correlation coefficient without the outlier? rwo = Would indusion of the outlier change the evidence for or against a significant linear correlation? O No. Induding the outlier does not change the evidence regarding a linear correlation. Yes. Induding the outlier changes the evidence regarding a linear correlation.

Name: The following bivariate data set contains an ou tlier.y12.4-410.613-1
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Description: The following bivariate data set contains an ou tlier.y12.4-410.613-1023.1171871.857.6963.832.8-915.717.5-825.820.61984.848.9-3054.247.1-545.859.4-1075.956.1-3731.642.82552.534.9-2246.932.91427.8200.41088What is the correlation coefficient with the

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Grade-point averages. Find the mean and median for the grouped data in the following table. Interval 1.95−2.15 2.15−2.35 2.35−2.55 2.55−2.75 2.75−2.95 2.95−3.15 3.15−3.35 3.35−3.55 3.55−3.75 3.75−3.95 Frequency 19 15 13 12 10 5 4 5 2 1 Mean = ______. (Round to two decimal places.) Median = ______.
b. For the hypothetical salaries in the following dataset, what is the average salary per year? Dataset of hypothetical salaries: $45,000 per year, $20,000 per year, $33,000 per year, $27,000 per year, $91,000 per year. From question 9b, which salary is the outlier? What is the new average if you delete the outlier from your calculations?
Consider the following data representing the price of refrigerators (in dollars). 1260, 1446, 1106, 1269, 1443, 1424, 1118, 1132, 1177, 1115, 1156, 1262, 1215, 1236, 1243, 1169, 1437, 1191, 1401, 1439, 1365 Answer Class 1093-1152 1153-1212 1213-1272 1273-1332 1333-1392 1393-1452 Step 2 of 4: Determine the frequency of the first class. Price of Refrigerators (in Dollars) Frequency Relative Frequency Cumulative Frequency Tables
Here is a sample data set. Frequency = 238.4 238.6 272.3 280.6 288.1 291.1 296.8 303.8 308.5 316.5 320.1 360.4 360.4 360.4 363.9 364.3 377.1 378.6 380.4 381.6 388.1 390.2 392.1 404.2 405.3 405.3 406.1 408.5 409 412.4 414.4 416.1 419.7 419.8 422 423.8 424.6 424.6 424.6 427 430.7 439.9 440 459.7 466 471 488.1 491 494.9 498.4 508.1 420.8 = 20 15 10 Find the first quartile for this data set. Q₁ = 5 200 250 300 350 400 450 500 550 length (cm) Find the third quartile for this data set. Q3 Find the interquartile range for this data set. IQR 339 368 Find the lower fence separating outliers from usual values. Fence Low = Find the upper fence separating outliers from usual values. Fence high =
The following bivariate data set contains an outlier. x y 65.5 288.7 25.2 -94.1 17.7 -164.9 46.7 192.9 69.4 204.2 62.7 330.2 58.4 379.7 49.1 237.8 40.8 232.8 77.8 30.2 63.9 86.5 35.8 -188.9 43.4 -200.2 42.7 368.3 258.1 -180.7 What is the correlation coefficient with the outlier?rw = What is the correlation coefficient without the outlier?rwo =
The following bivariate data set contains an outlier. x y 33.3 -236 48 2775.5 35.8 113.6 37.2 -847.7 36.7 -771.2 27.9 -497.6 42.1 647.7 26.2 1112.5 17.1 -912.3 62.5 -1162.1 33.6 -423.5 48.9 18.3 32.4 -513.2 45.2 1011.7 198.7 -1199 This data can be downloaded as a *.csv file with this link: Download CSVWhat is the correlation coefficient with the outlier?rw = What is the correlation coefficient without the outlier?rwo = Would inclusion of the outlier change the evidence for or against a significant linear correlation? No. Including the outlier does not change the evidence regarding a linear correlation. Yes. Including the outlier changes the evidence regarding a linear correlation.
The following bivariate data set contains an outlier. x y 52.9 788.3 55.7 583.8 43.2 12.3 50.3 486.2 60.5 297.6 48.1 176.8 60 443.1 31.7 174.6 46.6 -261.6 43.6 624 32 310.7 43.4 270.8 52.9 260.1 57.2 -955.2 146.9 6018.8 What is the correlation coefficient with the outlier? rw = What is the correlation coefficient without the outlier? rwo = Would inclusion of the outlier change the evidence for or against a significant linear correlation? No. Including the outlier does not change the evidence regarding a linear correlation. Yes. Including the outlier changes the evidence regarding a linear correlation.
Suppose the mean price for used cars is $10,198. A manager of a Kansas City used car dealership reviewed a sample of 50 recent used car sales at the dealership in an attempt to determine whether the population mean price for used cars at this particular dealership differed from the national mean. The prices for the sample of 50 cars are shown in the file named UsedCars. 12,400 10,400 12,100 10,000 11,000 8,895 7,675 9,975 6,350 10,470 9,895 11,250 8,795 12,500 9,340 10,150 9,200 9,395 11,000 10,640 10,000 7,500 8,000 10,440 10,200 10,300 9,740 9,280 10,930 8,000 9,000 7,680 9,400 10,730 7,350 12,240 11,970 8,240 9,910 10,080 9,440 8,970 9,500 10,050 10,130 11,400 8,500 7,500 9,090 10,500 (a) Formulate the hypotheses that can be used to determine whether a difference exists in the mean price for used cars at the dealership. H0: ? ≤ 10,198 Ha: ? > 10,198 H0: ? < 10,198 Ha: ? ≥ 10,198 H0: ? = 10,198 Ha: ? ≠ 10,198 H0: ? > 10,198 Ha: ? ≤ 10,198 H0: ? ≥ 10,198…
Over the first five years of owning her car, Gina drove about 12,700 miles the first year, 15,478 miles the second year, 12,675 the third year, 11,850 the fourth year, and 13,075 the fifth year. a. Find the mean, median, and mode of this data. b. Explain which measure of central tendency will best predict how many miles Gina will drive in the sixth year. a. mean= 12,700; median= 13,156; no mode; the mean is the best choice because it is representative of the entire data set. b. mean= 13,156; median= 12,700; mode= 3,628; the median is the best choice because it is not skewed by the high outlier. c. mean= 13,156; median= 12,700; no mode; the mean is the best choice because it is representative of the entire data set. d. mean= 13,156; median= 12,700; no mode; the median is the best choice because it is not skewed by the high outlier.
Here is a sample data set that appears to be nearly normal (as suggested by the histogram). 85.4 63.6 85.7 85.6 92.4 89.8 84.2 82.2 88 76.5 87.6 79 61.4 88.6 89.4 95 96.4 76.3 92.4 79.8 82.2 86.5 76.7 90.7 85.4 94 75.8 79.8 92.4 74.3 65 81.4 75.5 76.7 76.5 84.2 78.6 82.9 84.7 74.9 84 91.2 84.4 68.2 76 75.8 74.6 77.3 79.8 69.2 82.9 74.6 71.7 76.7 80 82.2 91.8 74.9 72.7 74.8 84 80.6 67.6 71.7 79.6 81.2 78.2 83.7 82.2 71.4 What is the mean of this data set? (Report answer using the rules suggested in class: 1 more d.p. than the data.)M=What is the standard deviation of this data set? (Report answer using the rules suggested in class: 2 more d.p. than the data.)SD=
Here is a sample data set. 278.2 282.5 311.5 311.6 320.4 322.9 352.3 367.4 375.7 390.8 395.8 411.7 412.9 412.9 412.9 418.6 419 420.6 423.1 425.2 426.9 429.1 429.6 433.5 434.3 434.8 435.4 435.4 436.2 444 451.7 453 456.5 464.1 473.2 476.1 478.2 489.2 497 502.7 502.7 502.7 513.5 517.9 522.2 566.6 568.3 576.1 577.1 580.8 604.5 618.9 621.6 628.4 24681012141618length (cm)250300350400450500550600650Frequency[Graphs generated by this script: setBorder(54,40,20,15); initPicture(205,650,0,19);axes(1256.8,2,1,null,2); fill="blue"; textabs([165,0],"length (cm)","above");line([250,-0.38],[250,0.38]); text([250,0],"250","below");line([300,-0.38],[300,0.38]); text([300,0],"300","below");line([350,-0.38],[350,0.38]); text([350,0],"350","below");line([400,-0.38],[400,0.38]); text([400,0],"400","below");line([450,-0.38],[450,0.38]); text([450,0],"450","below");line([500,-0.38],[500,0.38]); text([500,0],"500","below");line([550,-0.38],[550,0.38]);…
The following bivariate data set contains an outlier. x y 26.1 211.3 35.4 -464.7 41.6 -98.6 19.9 305.9 31.9 99.5 50.1 -676.3 42.3 -180.3 32 494.1 21 345.4 35.4 792.7 22.4 -46.3 30.5 -22.6 50.9 700.4 16.8 146.3 153.2 -5083.3 What is the correlation coefficient with the outlier?rw = What is the correlation coefficient without the outlier?rwo = Would inclusion of the outlier change the evidence for or against a significant linear correlation? Yes. Including the outlier changes the evidence regarding a linear correlation. No. Including the outlier does not change the evidence regarding a linear correlation. Question for thought: Would you always draw the same conclusion with the addition of an outlier?