Blood Test Measure (x) Age in Years (y) -15.49 78.2 -11.04 51.3 -13.62 70.1 -7.79 7.6 -14.84 77.9 -8.3 20.1 -9.83 24.4 -15.12 65.7 -13.31 55 -14.66 72.5 -15.77 63 -15 64.7 -10.45 38.7 -7.68 26.1 -15.93 76.2 -6.06 17.3 -16.82 77.6 -16.74 68.1 -14.83 80.5 -8.82 27.7 -9.72 32.2 -15.54 79.9 -7.5 14.3 -6.55 15.4 -12.21 56.8 -10.57 51.4 -11.12 42.3 -5.92 5 -8.03 18.5 -7.72 23 -5.04 2.9 -5.98 4.8 -8.06 10.1 -9.55 39.9 -6.14 11.1 -15.3 81.4 -9.55 20.3 -13.03 52.1 -6.42 7.7 -11.92 59.8 -6.8 22.3 -13.38 53.4 -12.92 64.8 -15.49 72.2 -12.97 66.1 -12.53 39 -12.22 65 -9.6 29 -11.42 24.6 -16.3 88.6 -10.16 46.2 -15.13 66.9 -10.42 23.8 -8.94 22.3 -6.76 16.7 -5.13 1.7 -13.74 64.1 -15.41 62.7 -9.8 27.3 -8 32.8 -9.43 25.4 -13.14 52.9 -15.58 62.9 -6.85 3.3 -8.11 27.2 -15.89 78.7 -14.95 59 -11.92 58 -7.56 31.7 -14.65 77.1 -16.08 80.9 -10.8 55.5 -15.83 48.8 -14.85 65.3 -9.17 47.6 -11.84 65.1 -10.26 40.3 -5.36 7.2 -8.25 11.4 -8.66 45.6 -16.63 82.8 -9.72 32.6 -11.91 59.5 -11.13 39.9 -10.3 37.9 -8.5 14.8 -4.91 4.1 -13.16 61.6 -9.33 21.5 -8.97 41.5 -16.03 68.9 -9.98 13 -11.02 37.6 -9.15 21.6 -11.36 35.4 -5.3 4.9 -11.85 43.6 -11.3 44.3 -11.14 35.8 -8.55 31.2 -12.48 50.8 -9.41 39.8 -9.88 44.7 -5.54 7.8 -7.08 16.7 -14.03 69.4 -10.49 39.3 -16.05 79.6 -6.97 8.3 -8.17 25.2 -5.64 11.3 -10.5 45.6 -7.85 33 -11.88 52.4 -9 27.9 -11.05 50.6 -6.99 10.7 -10.99 25.8 -15.28 78.6 -9.29 46.8 -10.6 39.5 -6.77 4.2 -12.58 46.2 -8.57 21.9 -10.86 19.3 -7.17 10.7 -11.02 42 -5.64 17.2 -8.16 22.1 -7.34 27.1 -9.31 26.5 -15.6 80.8 -10.55 31.9 -14.62 74.2 -12.88 45.3 -13.53 58.8 -13.05 48.2 -12.51 40 -9.19 16.7 -11.46 30.2 -12.37 51.9 -12.94 59.3 -5.2 6.9 -6.15 32.7 -14.37 72.2 -12.83 60.9 -14.31 65.8 -7.27 22.5 -10.6 46.6 -8.58 35.6 -14.21 72 -11.18 44.6 -6 20.9 -7.19 26.1 -14.62 68.5 -10.99 44.5 -8.77 31.2 -16.13 81 -9.64 18.7 -15.49 70.3 -11.04 32.8 -13.62 53.3 -7.79 31.4 -14.84 72.2 -8.3 12 -9.83 51.2 -15.12 83.3 -13.31 67.7 -14.66 63.2 -15.77 69.8 -15 62.5 -10.45 36.2 -7.68 32.6 -15.93 69.6 -6.06 10 -16.82 70.2 -16.74 71.5 -14.83 81.6 -8.82 33.1 -9.72 26.4 -15.54 63.3 -7.5 15.2 -6.55 10 -12.21 48.5 -10.57 29.3 -11.12 36.6 -5.92 5 -8.03 20.4 -5.77 14.5 -7.72 31.9 -8.06 13 -9.55 37.5 -6.14 15.2 -15.3 79.4 -9.55 25.9 -13.03 49.1 -6.42 5.9 -11.92 61.2 -6.02 6.7 -6.8 14.1 -13.38 54.8 -12.92 60.1 -15.49 69.4 -12.97 52.6 -12.53 49.6 -12.22 54.8 -9.6 36.1 -11.42 54.2 -16.3 83.5 -10.16 40.5 -15.13 71.2 -6.44 12.2 -10.42 34 -8.94 25.5 -6.76 7.1 -13.74 38.9 -15.41 70.1 -9.8 35.3 -8 24 -9.43 26.6 -13.14 66.1 -15.58 60.9 -6.85 22.9 -8.11 22.2 -7.26 12 -15.89 62.8 -14.95 61.6 -11.92 53.2 -7.56 29.5 -14.65 65.3 -16.08 79.7 -10.8 45.3 -15.83 79.2 -14.85 65 -9.17 37.2 -11.84 38.3 -10.26 37.2 -8.25 27.4 -8.66 28.7 -16.63 96.2 -9.72 33.1 -11.91 44.5 -11.13 47.6 -10.3 38.4 -8.5 26.9 -4.91 8.4 -13.16 56 -9.33 22.6 -8.97 20 -16.03 78.1 -9.98 21.8 -3.84 10.6 -11.02 35 -9.15 21.3 -11.36 30.6 -5.3 5.7 -11.85 39.6 -11.3 33.3 -11.14 53.4 -8.55 19.1 -12.48 48.9 -9.41 56.4 -9.88 45.8 -5.54 1.9 -7.08 16.3 -14.03 69.7 -10.49 36.1 -16.05 77.9 -6.97 19.2 -8.17 31.4 -5.64 7.2 -5.73 8.5 -10.5 39 -7.85 14 -11.88 48.5 -9 40.8 -11.05 41.6 -6.99 20.5 -10.99 59.3 -15.28 77 -9.29 45.1 -10.6 35.7 -6.77 4.7 -12.58 63.8 -8.57 30.8 -10.86 51.3 -7.17 12.8 -11.02 48.8 -8.16 32.4 -7.34 21.2 -9.31 34.5 -4.58 22.5 -15.6 71.4 -10.55 37 -14.62 61.8 -12.88 66.3 -13.53 66.4 -13.05 54.2 -12.51 39.9 -9.19 30.5 -11.46 38.4 -12.37 58.2 -12.94 61.5 -3.41 2.4 -6.15 7.9 -14.37 82.6 -12.83 56 -14.31 65.5 -7.27 3.5 -10.6 41.8 -8.58 23.9 -14.21 62.2 -11.18 32.8 -5.14 3.5 -6 11.2 -7.19 24.2 -14.62 56.3 -10.99 54.6 -8.77 27.4 -16.13 64.7 -9.64 34.2

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Related questions
Question
Blood Test Measure (x) Age in Years (y)
-15.49 78.2
-11.04 51.3
-13.62 70.1
-7.79 7.6
-14.84 77.9
-8.3 20.1
-9.83 24.4
-15.12 65.7
-13.31 55
-14.66 72.5
-15.77 63
-15 64.7
-10.45 38.7
-7.68 26.1
-15.93 76.2
-6.06 17.3
-16.82 77.6
-16.74 68.1
-14.83 80.5
-8.82 27.7
-9.72 32.2
-15.54 79.9
-7.5 14.3
-6.55 15.4
-12.21 56.8
-10.57 51.4
-11.12 42.3
-5.92 5
-8.03 18.5
-7.72 23
-5.04 2.9
-5.98 4.8
-8.06 10.1
-9.55 39.9
-6.14 11.1
-15.3 81.4
-9.55 20.3
-13.03 52.1
-6.42 7.7
-11.92 59.8
-6.8 22.3
-13.38 53.4
-12.92 64.8
-15.49 72.2
-12.97 66.1
-12.53 39
-12.22 65
-9.6 29
-11.42 24.6
-16.3 88.6
-10.16 46.2
-15.13 66.9
-10.42 23.8
-8.94 22.3
-6.76 16.7
-5.13 1.7
-13.74 64.1
-15.41 62.7
-9.8 27.3
-8 32.8
-9.43 25.4
-13.14 52.9
-15.58 62.9
-6.85 3.3
-8.11 27.2
-15.89 78.7
-14.95 59
-11.92 58
-7.56 31.7
-14.65 77.1
-16.08 80.9
-10.8 55.5
-15.83 48.8
-14.85 65.3
-9.17 47.6
-11.84 65.1
-10.26 40.3
-5.36 7.2
-8.25 11.4
-8.66 45.6
-16.63 82.8
-9.72 32.6
-11.91 59.5
-11.13 39.9
-10.3 37.9
-8.5 14.8
-4.91 4.1
-13.16 61.6
-9.33 21.5
-8.97 41.5
-16.03 68.9
-9.98 13
-11.02 37.6
-9.15 21.6
-11.36 35.4
-5.3 4.9
-11.85 43.6
-11.3 44.3
-11.14 35.8
-8.55 31.2
-12.48 50.8
-9.41 39.8
-9.88 44.7
-5.54 7.8
-7.08 16.7
-14.03 69.4
-10.49 39.3
-16.05 79.6
-6.97 8.3
-8.17 25.2
-5.64 11.3
-10.5 45.6
-7.85 33
-11.88 52.4
-9 27.9
-11.05 50.6
-6.99 10.7
-10.99 25.8
-15.28 78.6
-9.29 46.8
-10.6 39.5
-6.77 4.2
-12.58 46.2
-8.57 21.9
-10.86 19.3
-7.17 10.7
-11.02 42
-5.64 17.2
-8.16 22.1
-7.34 27.1
-9.31 26.5
-15.6 80.8
-10.55 31.9
-14.62 74.2
-12.88 45.3
-13.53 58.8
-13.05 48.2
-12.51 40
-9.19 16.7
-11.46 30.2
-12.37 51.9
-12.94 59.3
-5.2 6.9
-6.15 32.7
-14.37 72.2
-12.83 60.9
-14.31 65.8
-7.27 22.5
-10.6 46.6
-8.58 35.6
-14.21 72
-11.18 44.6
-6 20.9
-7.19 26.1
-14.62 68.5
-10.99 44.5
-8.77 31.2
-16.13 81
-9.64 18.7
-15.49 70.3
-11.04 32.8
-13.62 53.3
-7.79 31.4
-14.84 72.2
-8.3 12
-9.83 51.2
-15.12 83.3
-13.31 67.7
-14.66 63.2
-15.77 69.8
-15 62.5
-10.45 36.2
-7.68 32.6
-15.93 69.6
-6.06 10
-16.82 70.2
-16.74 71.5
-14.83 81.6
-8.82 33.1
-9.72 26.4
-15.54 63.3
-7.5 15.2
-6.55 10
-12.21 48.5
-10.57 29.3
-11.12 36.6
-5.92 5
-8.03 20.4
-5.77 14.5
-7.72 31.9
-8.06 13
-9.55 37.5
-6.14 15.2
-15.3 79.4
-9.55 25.9
-13.03 49.1
-6.42 5.9
-11.92 61.2
-6.02 6.7
-6.8 14.1
-13.38 54.8
-12.92 60.1
-15.49 69.4
-12.97 52.6
-12.53 49.6
-12.22 54.8
-9.6 36.1
-11.42 54.2
-16.3 83.5
-10.16 40.5
-15.13 71.2
-6.44 12.2
-10.42 34
-8.94 25.5
-6.76 7.1
-13.74 38.9
-15.41 70.1
-9.8 35.3
-8 24
-9.43 26.6
-13.14 66.1
-15.58 60.9
-6.85 22.9
-8.11 22.2
-7.26 12
-15.89 62.8
-14.95 61.6
-11.92 53.2
-7.56 29.5
-14.65 65.3
-16.08 79.7
-10.8 45.3
-15.83 79.2
-14.85 65
-9.17 37.2
-11.84 38.3
-10.26 37.2
-8.25 27.4
-8.66 28.7
-16.63 96.2
-9.72 33.1
-11.91 44.5
-11.13 47.6
-10.3 38.4
-8.5 26.9
-4.91 8.4
-13.16 56
-9.33 22.6
-8.97 20
-16.03 78.1
-9.98 21.8
-3.84 10.6
-11.02 35
-9.15 21.3
-11.36 30.6
-5.3 5.7
-11.85 39.6
-11.3 33.3
-11.14 53.4
-8.55 19.1
-12.48 48.9
-9.41 56.4
-9.88 45.8
-5.54 1.9
-7.08 16.3
-14.03 69.7
-10.49 36.1
-16.05 77.9
-6.97 19.2
-8.17 31.4
-5.64 7.2
-5.73 8.5
-10.5 39
-7.85 14
-11.88 48.5
-9 40.8
-11.05 41.6
-6.99 20.5
-10.99 59.3
-15.28 77
-9.29 45.1
-10.6 35.7
-6.77 4.7
-12.58 63.8
-8.57 30.8
-10.86 51.3
-7.17 12.8
-11.02 48.8
-8.16 32.4
-7.34 21.2
-9.31 34.5
-4.58 22.5
-15.6 71.4
-10.55 37
-14.62 61.8
-12.88 66.3
-13.53 66.4
-13.05 54.2
-12.51 39.9
-9.19 30.5
-11.46 38.4
-12.37 58.2
-12.94 61.5
-3.41 2.4
-6.15 7.9
-14.37 82.6
-12.83 56
-14.31 65.5
-7.27 3.5
-10.6 41.8
-8.58 23.9
-14.21 62.2
-11.18 32.8
-5.14 3.5
-6 11.2
-7.19 24.2
-14.62 56.3
-10.99 54.6
-8.77 27.4
-16.13 64.7
-9.64 34.2

1) With the blood test measurements as the x-variable and age as the y-variable, find the slope b1 and intercept b0 of the least squares prediction line y=b0+b1*blood test measurement

2) a wealthy philanthropist well-known in London's high society was murdered in his home. Sherlock Holmes finds a blood-stained golf club in a dumpster 2 blocks from the philanthropist's home. The blood test measurement on the golf club blood has value of -14.2. Use the least prediction equation to estimate the age of the person whose blood is on the golf club.

2a) Help Holmes solve this murder mystery by calculating a 90% prediction interval for the age of the person whose blood is on the golf club.

Traditional methods that forensic investigators use to determine human age require the destructive testing of bones or teeth. This presents a problem for crime scene investigators when the body of the deceased is not available.
A recently developed DNA-based blood test now enables forensic investigators with only a drop of blood for evidence to determine the age of the person who was bleeding. The test also works on dried bloodstains even those revealed by a heat-vision camera. This could help detectives reopen cases that went cold years ago.
This Excel file Blood Test for Age has data on the DNA-based blood test measurements of 321 volunteer human subjects with known ages from just under 2 to just over 96.
We would like to use a linear model to model the relationship between the blood test measurements and human age. scatterplot of the data with the blood test measurements as the explanatory (x) variable and age as the response (y) variable indicates that a linear model is appropriate. I suggest you create your own scatterplot to verify this and to better understand the data.
Transcribed Image Text:Traditional methods that forensic investigators use to determine human age require the destructive testing of bones or teeth. This presents a problem for crime scene investigators when the body of the deceased is not available. A recently developed DNA-based blood test now enables forensic investigators with only a drop of blood for evidence to determine the age of the person who was bleeding. The test also works on dried bloodstains even those revealed by a heat-vision camera. This could help detectives reopen cases that went cold years ago. This Excel file Blood Test for Age has data on the DNA-based blood test measurements of 321 volunteer human subjects with known ages from just under 2 to just over 96. We would like to use a linear model to model the relationship between the blood test measurements and human age. scatterplot of the data with the blood test measurements as the explanatory (x) variable and age as the response (y) variable indicates that a linear model is appropriate. I suggest you create your own scatterplot to verify this and to better understand the data.
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