Class 10th Mathematics Tamilnadu Board Solution
Exercise 11.1- 59, 46, 30, 23, 27, 40, 52, 35, 29 Find the range and coefficient of range of…
- 41.2, 33.7, 29.1, 34.5, 25.7, 24.8, 56.5, 12.5 Find the range and coefficient…
- The smallest value of a collection of data is 12 and the range is 59. Find the…
- The largest of 50 measurements is 3.84 kg. If the range is 0.46 kg, find the…
- The standard deviation of 20 observations is √5. If each observation is…
- Calculate the standard deviation of the first 13 natural numbers.…
- 10, 20, 15, 8, 3, 4 Calculate the standard deviation of the following data.…
- 38, 40, 34 ,31, 28, 26, 34 Calculate the standard deviation of the following…
- |c|c|c|c|c|c| x&3&8&13&18&23 y&7&10&15&10&8 Calculate the standard deviation of…
- The number of books bought at a book fair by 200 students from a school are…
- Calculate the variance of the following data |c|c|c|c|c|c|c|c|c|
- The time (in seconds) taken by a group of people to walk across a pedestrian…
- A group of 45 house owners contributed money towards green environment of…
- Find the variance of the following distribution. |c|c|c|c|c|c|c|c|…
- Mean of 100 items is 48 and their standard deviation is 10. Find the sum of…
- The mean and standard deviation of 20 items are found to be 10 and 2…
- If n = 10 , bar x = 12 and sum x^2 = 1530 , then calculate the coefficient of…
- Calculate the coefficient of variation of the following data: 20, 18, 32, 24,…
- If the coefficient of variation of a collection of data is 57 and its S.D is…
- A group of 100 candidates have their average height 163.8 cm with coefficient…
- Given sum x = 99 , n = 9 and sum (x-10)^2 = 79 . Find sum x^2sum (x - bar x)^2…
- The marks scored by two students A, B in a class are given below.…
Exercise 11.2- The range of the first 10 prime numbers 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 isA.…
- The least value in a collection of data is 14.1. If the range of the collection…
- The greatest value of a collection of data is 72 and the least value is 28.…
- For a collection of 11 items, sum x = 132 , then the arithmetic mean isA. 11 B.…
- For any collection of n items, sum (x - bar x) =A. sum x B. bar x C. i bar x D.…
- For any collection of n items, (sum x) - bar x =A. i bar x B. (11-2) bar x C.…
- If t is the standard deviation of x, y. z, then the standard deviation of x +…
- If the standard deviation of a set of data is 1.6, then the variance isA. 0.4…
- If the variance of a data is 12.25, then the S.D isA. 3.5 B. 3 C. 2.5 D. 3.25…
- Variance of the first 11 natural numbers isA. root 5 B. root 10 C. 5 root 2 D.…
- The variance of 10, 10, 10, 10, 10 isA. 10 B. root 10 C. 5 D. 0
- If the variance of 14, 18, 22, 26, 30 is 32, then the variance of 28,…
- Standard deviation of a collection of data is 2√2. If each value is multiplied…
- Given sum (x - bar x) = 48 , bar x = 20 and n = 12. The coefficient of…
- Mean and standard deviation of a data are 48 and 12 respectively. The…
- 59, 46, 30, 23, 27, 40, 52, 35, 29 Find the range and coefficient of range of…
- 41.2, 33.7, 29.1, 34.5, 25.7, 24.8, 56.5, 12.5 Find the range and coefficient…
- The smallest value of a collection of data is 12 and the range is 59. Find the…
- The largest of 50 measurements is 3.84 kg. If the range is 0.46 kg, find the…
- The standard deviation of 20 observations is √5. If each observation is…
- Calculate the standard deviation of the first 13 natural numbers.…
- 10, 20, 15, 8, 3, 4 Calculate the standard deviation of the following data.…
- 38, 40, 34 ,31, 28, 26, 34 Calculate the standard deviation of the following…
- |c|c|c|c|c|c| x&3&8&13&18&23 y&7&10&15&10&8 Calculate the standard deviation of…
- The number of books bought at a book fair by 200 students from a school are…
- Calculate the variance of the following data |c|c|c|c|c|c|c|c|c|
- The time (in seconds) taken by a group of people to walk across a pedestrian…
- A group of 45 house owners contributed money towards green environment of…
- Find the variance of the following distribution. |c|c|c|c|c|c|c|c|…
- Mean of 100 items is 48 and their standard deviation is 10. Find the sum of…
- The mean and standard deviation of 20 items are found to be 10 and 2…
- If n = 10 , bar x = 12 and sum x^2 = 1530 , then calculate the coefficient of…
- Calculate the coefficient of variation of the following data: 20, 18, 32, 24,…
- If the coefficient of variation of a collection of data is 57 and its S.D is…
- A group of 100 candidates have their average height 163.8 cm with coefficient…
- Given sum x = 99 , n = 9 and sum (x-10)^2 = 79 . Find sum x^2sum (x - bar x)^2…
- The marks scored by two students A, B in a class are given below.…
- The range of the first 10 prime numbers 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 isA.…
- The least value in a collection of data is 14.1. If the range of the collection…
- The greatest value of a collection of data is 72 and the least value is 28.…
- For a collection of 11 items, sum x = 132 , then the arithmetic mean isA. 11 B.…
- For any collection of n items, sum (x - bar x) =A. sum x B. bar x C. i bar x D.…
- For any collection of n items, (sum x) - bar x =A. i bar x B. (11-2) bar x C.…
- If t is the standard deviation of x, y. z, then the standard deviation of x +…
- If the standard deviation of a set of data is 1.6, then the variance isA. 0.4…
- If the variance of a data is 12.25, then the S.D isA. 3.5 B. 3 C. 2.5 D. 3.25…
- Variance of the first 11 natural numbers isA. root 5 B. root 10 C. 5 root 2 D.…
- The variance of 10, 10, 10, 10, 10 isA. 10 B. root 10 C. 5 D. 0
- If the variance of 14, 18, 22, 26, 30 is 32, then the variance of 28,…
- Standard deviation of a collection of data is 2√2. If each value is multiplied…
- Given sum (x - bar x) = 48 , bar x = 20 and n = 12. The coefficient of…
- Mean and standard deviation of a data are 48 and 12 respectively. The…
Exercise 11.1
Question 1.Find the range and coefficient of range of the following data.
59, 46, 30, 23, 27, 40, 52, 35, 29
Answer:Given the set of data 59, 46, 30, 23, 27, 40, 52, 35, 29.
Let the maximum value in the data set be denoted by xmax and minimum by xmin
Range = Maximum value in the data set – Minimum value in the data set
= xmax - xmin
= 59 – 23
= 36
∴ Range R = 36
Let the Coefficient of Range be denoted by s.
s = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 0.439≈0.44
∴ s = 0.44
Question 2.Find the range and coefficient of range of the following data.
41.2, 33.7, 29.1, 34.5, 25.7, 24.8, 56.5, 12.5
Answer:Given the set of data 41.2, 33.7, 29.1, 34.5, 25.7, 24.8, 56.5, 12.5.
Let the maximum value in the data set be denoted by xmax and minimum by xmin
Range = Maximum value in the data set – Minimum value in the data set
= xmax - xmin
= 56.5 – 12.5
= 44
∴ Range R = 44
Let the Coefficient of Range be denoted by s.
s = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 0.637≈0.64
∴ s = 0.64
Question 3.The smallest value of a collection of data is 12 and the range is 59. Find the largest value of the collection of data.
Answer:Given xmin = 12, R = 59
Range R = xmax - xmin
Substituting the given terms into the above formula, we get
R = xmax - xmin
59 = xmax - 12
xmax = 59 + 12 = 71
Question 4.The largest of 50 measurements is 3.84 kg. If the range is 0.46 kg, find the smallest measurement.
Answer:Given xmax = 3.84kg, R = 0.46kg
Range R = xmax - xmin
Substituting the given terms into the above formula, we get
R = xmax - xmin
0.46 = 3.84- xmin
xmin = 3.84 - 0.46 = 3.38kg
Question 5.The standard deviation of 20 observations is √5. If each observation is multiplied by 2, find the standard deviation and variance of the resulting observations.
Answer:Let the observations be x1, x2, x3….x20
Given SD = ![](data:image/png;base64,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)
Formula for SD = ![](data:image/png;base64,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)
Where N = 20, SD = ![](data:image/png;base64,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)
If each observation is multiplied by 2, we get new set of observations.
Let the new observations be y1, y2, y3….y20
New Standard Deviation SD’ = ![](data:image/png;base64,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)
We know that
and ![](data:image/png;base64,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)
But yi = 2xi
Therefore, ![](data:image/png;base64,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)
Substituting in the SD’ formula,
SD’ = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 2 × SD = 2![](data:image/png;base64,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)
Formula relating SD and Variance is Variance = SD2
So Variance = (2
2 = 20
Question 6.Calculate the standard deviation of the first 13 natural numbers.
Answer:To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
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)
SD = ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEMAAAAuCAMAAABEflTFAAAAAXNSR0IArs4c6QAAAJNQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOgA6OgBmOjoAOjo6OjpmOjqQOma2OpCQOpC2OpDbZgAAZgA6ZgBmZjoAZjpmZmYAZmaQZma2ZpDbZrb/kDoAkDo6kLb/kNv/tmYAtmY6ttuQttv/tv/btv//25A625Bm27Zm27aQ2////7Zm/7aQ/9uQ//+2///b+npDswAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAB7ElEQVRIS+2V21LCMBCGkwK2HhClooItUkQDNiZ5/6czm6QlYA5AccYL94IZtsm/p69bhP7N14EKJ/gUs/WqonN/Rdldgz8sOufBJx+dNdjVGTRua0TMXGRnPqerUFrrWY2QmGM8sCPTixrxPJUXWVYgMnYr8BzCyFNMhuSTFX+0DxK4ThJobFXAiYjpKPzJzkP5RCmzAS1PGpau6h+b7sSq1DWWaSlgZZMlC5Zp0dZap5gvEJ3V7MV6aDBV1RjeZIvIqEYVVNmaccIZ1Rwr4wZThVrzh/SbKLqXGMv8jNPBdYPpEqponq+vZSUUxmCZcsJgof07ZjClI+XVPf16FeVghdTEWjNO5GBSu9gQfkmh/q2zVFRY9sTWaJyu0SkidH5QCdH56KR2a9FOF0GAacM6jMZi3aWhWN836gqmI2YwjUPsADKjMhrTbqYwPWUl4zbuObapg7qj6/pL2/To5PcuKEw9JiGTgye4F1n8fkylMIXrYu6lVSzV2xXElN6VqWtfmLw3w2e9ykOY0pTiIjR9vXWDH31ZaNV7/7m3tv0j8rUMYyo1eH4f0uD5RR3GFBpOkxuo2WcUj8OYgoYoVd98JsreW+ijr/lglyENOf9+/Psaoxi2d+xM7DnLvNs0dnX7/Byb8PBov3PyG1lMMEUBxNZUAAAAAElFTkSuQmCC)
=
3.74
Question 7.Calculate the standard deviation of the following data.
10, 20, 15, 8, 3, 4
Answer:To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
=
5.97
Question 8.Calculate the standard deviation of the following data.
38, 40, 34 ,31, 28, 26, 34
Answer:To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASgAAAAgCAMAAACW/RXyAAAAAXNSR0IArs4c6QAAAJZQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGaQAGa2OgAAOgA6OjoAOjo6OmZmOmaQOma2OpC2OpDbZgAAZgA6ZjoAZjo6ZjpmZpDbZrbbZrb/kDoAkDo6kDqQkGY6kGaQkLbbkNv/tmYAtmY6ttv/tv//25A625Bm27Zm27aQ29uQ29u229vb2////7Zm/9uQ/9u2//+2///bB/NuWQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAC/klEQVRoQ+1Y23qbMAw26ZaVZUuaNN0SsqylK6HrIOD3f7lJNj4ANhDKhRvwTfVV+nX4LcsOhExrYmBYBk4Lb/YTXGZbz1smdd9nfw//pEduVV4CS7iRRUmfb+tu3yDcyha1UAIqfqjn1Bi1Gkm4EiBjMl0YpYdHEs+eCA3WJNusOYQGsm56WKAc3Sbnr08VrcCSwqgElcrT960kSjrO71lYc9RCCXk838mtk1BTVGulwpUA6cl0oadsgxTkG+AjLCrSiIp2SBpqsagqjYQw+riRRYksi3iaYwa1RRV+tVasQktRG2sWO8z/qmQuJio77rCJVkmGZJU7Kl2xBPN7iGGgkTBsYVQjiilLuZWqjda2qIiBXaHB8uGbOHo6tBa1sWZ0BavIpj9RofcJ68EZtcJGzzceW0ha/uOFBrvfiSJK1wJ3iBVGZWih1IgqQ0kMI8ocFf7PlGd/9e8twK5qjNrSGcyVyqY/UYS8wijOtzvYwerRixhl88R+9AArjOpHDx3bOirC/M1RoZ9YcWx7ojkfU6VmrEZt4Iq7wsWz6UsUJoN31tk3ESUTDOeGYS6wehXauOaOQRl+ESNZVZtC/uneEpUr2cGsb54pqp2owpUEacm0dGJVffL5xR/76nmg795f/+YFt96b8YGjsyKwhBuZlazhqvcAP0t7c1ShVAezLaq1ZumqSFVPxk4UjRee9xnamSjpQlrHYR7OHkkWwKOJKGkclV9YJbvfI7zOlHShixGZh2KaECWNqPyupWbH5R9uq6SuWLftQv76g4WzpeuyoPAO4C9dKUn3H1NoI8RcVRsK9a/yIldSF9z4bFLxpiFKugYWBj16SMjZLx5/mnQNRA1YA/9OksLzQEkDur8eVxTfmlkAPzKVdD3VDVlJdoTfc+xTipKG9D/56skADfB2Nnww7+mvP8ydTIw10F9wyg8XPP/6M9GCdCcTa6L4PciN5U4mJj7caCjMzJ1MjH2TujChWGbuZDI11DtGjDvb6E4mU0O9o6Em6PgY+A/gmmBNNNjfqAAAAABJRU5ErkJggg==)
![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
=
4.69
Question 9.Calculate the standard deviation of the following data.
![](data:image/png;base64,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)
Answer:![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFcAAAAgCAMAAABQMRQnAAAAAXNSR0IArs4c6QAAAIFQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOgA6OjoAOjo6OmaQOma2OpC2OpDbZgAAZgA6ZjoAZjo6ZpDbZrbbZrb/kDoAkDo6kGY6kLbbkNv/tmYAtmY6ttv/tv//25A627Zm27aQ29u229vb2////7Zm/9uQ/9u2//+2///bA92AIgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAABgElEQVRIS9WVXVeDMAyGW9SBCorDDXHd7FBGyf//gSb9EjjHYw/Ui/UmFMqT9G2aMHZ941zlHUVtLDQ8eY2wCTg+aaqzMu36+8N6sEwNw9qh3DOon1dzoc6rR9y/s8MLxiqsrxX0Piu+PuuUORuLqzly03kbTYeiA4wXamOZ2MQ5N7XlvMCMcHbY8mQXIGxb6aSB8wPndy5/oEWanwVQ5kswipsPeimSd6bqxIIF3zFVmi9LRp+fpOVS6ki+NxSdSH62hMws1+AmEV6cl5Vc1eSnMWLq5Re44G44De1CF+9Qcm7qix19RqL43yYPATsY6dBmIx3WFoGxvhfuC4q+AwHjTx2I0WeeK9KxJgEO5ktMvGbXPxmAFQGna8qh1OcIdCVUjbShRAF05QaxmAtNhud8i31F0RPVAc21qi3mhukm0TftKlZrc16lvd2xWtuMG6u1eZFIhwKVj9TaxuLTpf4PLuV3bB3g7YA3Ec8uVmuzQsAR04xaWmhrC8veq1r1DRSiIMILqNlTAAAAAElFTkSuQmCC)
SD = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 6.32
Question 10.The number of books bought at a book fair by 200 students from a school are given in the following table.
![](data:image/png;base64,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)
Calculate the standard deviation.
Answer:![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 1.107
Question 11.Calculate the variance of the following data
![](data:image/png;base64,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)
Answer:![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAA///b//+22///tv///9uQkNv//7ZmZrb/25A6OpDbtmYAOma2ZmYAOmY6kDo6AGa2kDoAZjpmADqQZgAAOgAAAABmAAA6AAAASq0sfgAAAAF0Uk5TAEDm2GYAAABQSURBVHgBpcFJEoQgAATBAhQF3BCU/v9LDfU6lwkz+V/Vozh+SVJxQRohNY/JI2BycSFy649l4hWa52H3+ewAzOpt3QCTIgyKkNW8rVLkgwucRwNwiWOk2QAAAABJRU5ErkJggg==)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
= ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACMAAAAuCAMAAACVmVC1AAAAAXNSR0IArs4c6QAAAHtQTFRFAAAAAAAAAAA6AABmADo6ADqQAGa2OgAAOgA6OgBmOjoAOjpmOjqQOma2OpDbZgAAZgA6ZgBmZjoAZjpmZmYAZpDbZrbbZrb/kDoAkDo6kDqQkGY6kNv/tmYAtmY6ttuQtv//25A627Zm29uQ2////7Zm/9uQ//+2///bZ5NqZgAAAAF0Uk5TAEDm2GYAAAEeSURBVHjazZNrd4IwDIYTHGVuA3dxgs6q3Qrk///CndBwqZfFD/Mc+4lDX5Lm6QPA/S6LCZ5bUaZUy1ClZ9rXrZ55O6iZZn5F5sWrmTr1QBUPawAcIiZ8viabTuIMAG080HoL4GSH1k9RppB6OYwZt4qI2EK+5BbcK+d8TE0w16afISvbjwNVqxPMoUx4Ubij+xLMoQwnw0RRr4BZytAOMema/GSz22BWM0Y3tcf8p82cUXz+J5vHO7gfmwWUqExfmHxewDxoanzzGAahXR5jlky7KFkzfvx+Xnb/iB0zQeXOOWt6b/koEWZWMMqAw/IIM/eY9Oo6p36ieq+yTYcz80xYjJgHldt3UVpKz/Yq5hofdMwWdZubzFxp6o3XL5R8G3RwD/xaAAAAAElFTkSuQmCC)
= 3.88
To find Variance
Variance = SD2
= 3.882 = 15.058
Question 12.The time (in seconds) taken by a group of people to walk across a pedestrian crossing is given in the table below.
![](data:image/png;base64,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)
Calculate the variance and standard deviation of the data.
Answer:![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 6.063
To find Variance
Variance = SD2
= 6.0632 = 36.76
Question 13.A group of 45 house owners contributed money towards green environment of their street. The amount of money collected is shown in the table below.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAhIAAAA1CAAAAAAxYF3EAAAAAXNSR0IArs4c6QAABd9JREFUeNrtnNuB4yoMhv9eVAy1qBQqUSHUQS06D4ANNgYce2eye8RTJihcP6ML8kCtWGkKFFasVEWh+H5qf/f3X9zQH9g8Q8KQMCQMCUPCkDAkDIm/BYkIV/3FCIbEP4CEB99sX/bPjlWVk6MTVT19NG0P1I22FQmzo0j1+wAAPgGKBtGmOHRFsCbWduc7IliS2osAoOmQGEj7I2WS1zsSimynxVtIkMPHSARs80vnA8kHSAiixi4TLqbas0j1ey+a6+BV3cVOMqA9EayJVZTG/MODCJakmkkr02RITKpKrBoQkmTnoM4PS4Qk2V6Ld5AISHscIQAklMcyg+ZJVQWqESH1TQBQHxJp7OnT8jFRD9B5VWU3QvAkcnq4WVVIVUPZjMNOkkB7IlgT25cr68aTCJak9s12uWY4JPJ5UZkHq5uQ8C5vVa/FO0gwJ6AiSNXD5Y4LaBUSyGhXD2ua+a44NPY3ZIwEpCze5YRPIj0kEjJde0agAu2JYE2s2UntiWBJqupK8maPhsSIaW7k60O5i4TjvAG9Fu8ggZA2OiJktEIBLSI2p0TuZ0ci7t1QPi8ofIrE1ZAZPREcFzhsiyvdo7DZ674GGolt6FGm/ySCJanW2nDTIflsQmQk4hyJ0G3xBhJSjoQaibi3ekRC6r4ytb6yfFaNiWskqLWjZOt3gESEPy2u7AouJHNktv5XYlKrSpcthTESfam6oYCoDDcZkidVgj8iwa39+C4SDkiW7wMktPJZPkeCLuzy0BXBmYjrUzrkyx8/PqVHYo2LpZCx4riWaq2EsG9gd0hp0T1+UnGkPgPCAYlNccgIiaI44v5Yf6I4BualzxMbmpchdz8yL/PjPzMvL8W2AZVnZWheXku1SMSuyBEJQfxB8zJ3QnxAYjMvk/PTIEF8MC919w8/Mi8HTiiuRHDGZuyEZrUzc0KvxPZNkmLeDJzQa6nWcOyL1B4HqSq5kRO6IfGSE5o78TgiUZzQFCNqkAgdJzRk1faRE3odqkJRa6NQldscnmGoStZCVTIJVcWrCBOWpNpJz0NVbhqqikBem9dCVU/KSbl9FKqygPa/dMfhuKeHDIn/MRJ27WVIfMu0DQlDwpAwJAyJl5GwYsWS9u2UMMVhSBgShoQhYUgYEobE1yBRMi6PEWpD4p9DwqfLtlmgmTcQvgMJKreEny9bvh+Vp+uf8w721wceNqSU01SfIuGBcvN5DwmXstknSOwXmF+BRHQ6SHy4sWwBD9mUsn/b6wNPGyKeNbSKBH14SjiPggSf8fQJfbffrzvmKlFiS3skny/mQ+a8mavkdIqSOU/eZX79Vgu4lMkWF6cteL5s5PWlUyLJyuOG0qyYfxMJJZ+QYDrh6ZHTdfalc5CUWLPVbUhAVCVnhm7j2dKdyKe8f2ZVQkhf+pzUGYGSNubl55AQ6HciMd7OP46EIOfah5LD2OoL5gYJLl+VuoJEyaGTJtnT+Zz1x6zCFJQkN+e8KmJqpKR43pk2nj9JC4fEHSQYzxvKueRvILFkKnWRUPKKkEfUJL3m/F86I+Gquu2UoKQMuLHbtmx6IXXCXhELElxSnktrWuWKzc3Dh+bl4iFxAwl5xbyMAEAvIJHalI+QCIivIKHq9oT25ixNuZkRGiiQ1kjEuqdkhqwpjgkRS8u2ckisIzEh4pYGcvwOEqvewBEJdXxXcbi6TqrVZXd49jbFocROFcwVEronbofDJCbTJtLHSHjoi0j4mdd2A4n47Lx5AYmAxrzc3mrumpcFia3OOVUPr4HLPjtV5XAwL5VTorrUSDCiaii2hPgdrrETOne3VwxH/yIS89fr15GYTQ+6OnBZzGw8IZFzIosTuiOxOZodJLY6BeDJpwx6PgWSpNgV20v/FRIlwpOQiNiNCcyc+MG/U1hbNl5f2dlIYv36wMOGZBbyWkXCYRrzehTQ/vFiAe3fCmgbEoaEIWFIGBKGhCFhSBgShoQhYUgYEoaEIWFIGBKGxG8jYcVK+wKgFStN+Q+NpJfUHQZlngAAAABJRU5ErkJggg==)
Calculate the variance and standard deviation.
Answer:![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEoAAAAkCAMAAAAZ8FxpAAAAAXNSR0IArs4c6QAAAHhQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGaQAGa2OgAAOjoAOjpmOmY6Oma2OpDbZgAAZgA6ZjpmZmYAZpDbZrb/kDoAkDo6kDpmkLb/kNv/tmYAtmY6ttv/tv//25A625Bm27Zm2////7Zm/7aQ/9uQ/9u2//+2///bI7WMhwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAABaElEQVRIS+1V7U6EMBAsqHhVTzwVlaK9Ayt9/zd0yralTSSWcn9M3OQSNtmdnf3oHGN/2mRB9ry9i7HeAUTxM0AxWbbAEueA0k3Vb2+OEBS/p48TL1vFCVgUZTusnyC1CBuqXu57JjC9rxr4HX7GSbfxwUIxefnk0mTV69ePdBCK7PzIjzdobygMEeBLkCIn1Ya9i/x8083VO5NTtrg+GFJwarq837ejbqcMMDvynRbIICi7DXKSTL+YQekmuCvKHidSlmISlH04bokuu2vN+ghKmOYQt/6KFUeSKGiCcDA2FIp4J5H8Mcg8CLPO2UDUWthOSgXF7/yal+M9/OLHNLG1xRcLjofHi+jysxvEwZCsbTYtphcUzT0TtIHgjHg9qVj/Gr9m0F7jM3U9rOVO2uv6GiJxrNf4PF0PwbzGW13PJzVrvNP1XKxZ44N/2SywSOOtrmcBQZFIr4wueV3PhIrUNkEys8p8A9CcGKxMa4/lAAAAAElFTkSuQmCC)
= ![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 20.39
To find Variance
Variance = SD2
= 20.392 = 416
Question 14.Find the variance of the following distribution.
![](data:image/png;base64,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)
Answer:![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
= ![](data:image/png;base64,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)
= 7.361
To find Variance
Variance = SD2
= 7.3612 = 54.19
Question 15.Mean of 100 items is 48 and their standard deviation is 10. Find the sum of all the items and the sum of the squares of all the items.
Answer:Mean of 100 items
= 48
Standard deviation SD = 10
To find : ![](data:image/png;base64,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)
To find
:
Formula for ![](data:image/png;base64,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)
= 48 = ![](data:image/png;base64,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)
Sum of all the items ![](data:image/png;base64,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)
To find
:
SD2 = ![](data:image/png;base64,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)
100 =
482
240400
Question 16.The mean and standard deviation of 20 items are found to be 10 and 2 respectively. At the time of checking it was found that an item 12 was wrongly entered as 8. Calculate the correct mean and standard deviation.
Answer:Mean of 20 items
= 10
SD of 10 items = 2
Wrong Value = 8
Correct Value = 12
To find Mean:
Formula for ![](data:image/png;base64,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)
= 10 = ![](data:image/png;base64,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)
Sum of all the items
200
This value is wrong since item 12 was wrongly entered as 8.
Let
be the corrected sum
200 + (Correct Value) - (Wrong Value)
= 200 + 12 - 8 = 204
Corrected mean will be
=
10.2
To find SD:
SD2 = ![](data:image/png;base64,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)
4 = ![](data:image/png;base64,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)
= 2080
This value is wrong since item 12 was wrongly entered as 8.
Let
be the corrected sum of squares.
= 2080 + (Correct Value)2 - (Wrong Value)2
= 2080 + 122 - 82
= 2160
Corrected SD:
SD2 = ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAHcAAAAjCAMAAACZ+GVZAAAAAXNSR0IArs4c6QAAAJZQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOgA6OjoAOjo6OjpmOjqQOmaQOma2OpCQOpC2OpDbZgAAZgA6ZgBmZjoAZjpmZjqQZmaQZma2ZpDbZrb/kDoAkDo6kDpmkLb/kNv/tmYAtmY6tpBmttv/tv/btv//25A625Bm27Zm2////7Zm/7aQ/9uQ/9u2//+2///bLYDyDgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAACHUlEQVRYR+2V21LCMBCGWxBaRUSsZylKBQ02xuT9X85Nk57SppsgznhhbjoTyPdn/93sBsH/ajog1mE4ef9FT0io1qqtwW92/PbKS9dCsjB4EsEvLDZ0YY/f+cVrJ/VKk9EG9rOOLnvIvcINAhupHyPSqSHAE9ihjzl78hPukAqQdbFY5/EjHm1YPM2l6TyBnPvlV2ZLnWiCBq6u/IFFpzlZQKhjv8TW6JLkBuLXWjcgJ9JaIivtoFWRnECvVU3tzyAhIu3WtuMtKlIXJLZxONJgsZ2Do3RRUj9fRDrZ8fu6GMjFzknSIDVBGiGtIKrMRLqELzuX2SRwlX0ciSxslyCbuQRvkNqgGgFpL2Rl9Ym1TK7dW3ZaJt8eOEIqEV+qEamy1d2tLOkuXJszZDdGUgg21y67lS0esEgRUoGgi1w8Q6R9LbkvKGXi0EJJElE0Ihk3tVvbFskwX3BSE6H/rcdg/6e4QFZXeFb9rXnpYZKBCIhrN2zo9ruNk5oI3B2tguripMN0j5tftAp1uDz5cT23EOir07rHer9VbRhjlsVyzpPQLDfnfqXAfZg2wmwIVEqKtTEH8HDLTq8D6mJMhJoidfyX0O/UmKiXzzxSp6iJ6SLU1CwXjWi4MnT95q/SNTAogkZBNn4z4u3vFEO73hg4wJPlMXT9MKALw2KGj/lhD7wx8oBIXaeUVdwXox6ey7MZjPdIGP+6+psnvgGOJEIS5jxfwgAAAABJRU5ErkJggg==)
SD2 = ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGsAAAAgCAMAAAAi/naRAAAAAXNSR0IArs4c6QAAAI1QTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGaQAGa2OgAAOgA6Ojo6OjqQOmZmOmaQOma2OpC2OpDbZgAAZgA6ZjoAZjo6ZpDbZrbbZrb/kDoAkGY6kLbbkNv/tmYAtmY6tpBmttv/tv//25A625Bm27Zm27aQ29u229vb2/+22////7Zm/9uQ/9u2//+2///bixkkXAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAACAklEQVRIS92WYV+CMBDGhxVhGiZqOguzpRYg+/4fr93dNolChuzXC/dCQGB/nrtnd2PsWsdhHgQTECc3EWncLeKMyXUwePasuZy9sN3glbGPxzmy5OYpUwcRZcWD+tv30JMKZNFvmayY5FPfJDU9zYkUyePFKM7KmdKU6qB6JO4wXZpVDCefBx75ZVlPCI0iXQgR4ZfPGFpP5AqVryB66b0yheSTTPKIpeFZb8i38Xt7aEW8NQ+BJ8okUEOxBBxV4o6wBjJWzoPBsnE2ydGsjO0X5FW1RIKxmVju1Rx3cKMYgQpM0KU+Mw5V33WD8iQPsyOnc+WpYMmOCV6ZdWM80R6L+hM5zVrEW0FnOazQHMKDLEi8oCsR4mo1nujMgnya0BArhUOZVNeIJqMw64nOKFYMTRYY6YIQAgs16IF4uDU9eaI7iiJGo8oioh72eyoFAdzXOCj69rZBOLBO9U0tngvk2Fe0Ieq6KvmqZLQn65cu4w27htLIivHOQoNbz5PRcyL3LOBVH1IOwYJoDYwj+lymyCqTSwsGpcxkQ66Hyje30L6hsqluTiztJoT0bri6brj4i+rGz2FajNM+w71j/yXLthi3fYat8y3STnW+9iC1GLd9Ruf+VWNBi/Hb+xtVY4v5Hxa1GNcYuliw8RnTYlr2Gb0Y+mW77Ti/z/CBuro5vgGDCjOfN7VAWwAAAABJRU5ErkJggg==)
SD = 1.99
The corrected mean is 10.2, corrected SD is 1.99
Question 17.
Answer:SD2 = ![](data:image/png;base64,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)
SD2 = ![](data:image/png;base64,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)
SD = 3
Coefficient of Variation
CV = ![](data:image/png;base64,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)
=
= 25
Question 18.Calculate the coefficient of variation of the following data: 20, 18, 32, 24, 26.
Answer:To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
Formula for ![](data:image/png;base64,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)
=
24
![](data:image/png;base64,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)
SD = ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEMAAAAuCAMAAABEflTFAAAAAXNSR0IArs4c6QAAAJNQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOgA6OgBmOjoAOjo6OjpmOjqQOma2OpCQOpC2OpDbZgAAZgA6ZgBmZjoAZjpmZmYAZmaQZma2ZpDbZrb/kDoAkDo6kLb/kNv/tmYAtmY6ttuQttv/tv/btv//25A625Bm27Zm27aQ2////7Zm/7aQ/9uQ//+2///b+npDswAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAB7ElEQVRIS+2V21LCMBCGkwK2HhClooItUkQDNiZ5/6czm6QlYA5AccYL94IZtsm/p69bhP7N14EKJ/gUs/WqonN/Rdldgz8sOufBJx+dNdjVGTRua0TMXGRnPqerUFrrWY2QmGM8sCPTixrxPJUXWVYgMnYr8BzCyFNMhuSTFX+0DxK4ThJobFXAiYjpKPzJzkP5RCmzAS1PGpau6h+b7sSq1DWWaSlgZZMlC5Zp0dZap5gvEJ3V7MV6aDBV1RjeZIvIqEYVVNmaccIZ1Rwr4wZThVrzh/SbKLqXGMv8jNPBdYPpEqponq+vZSUUxmCZcsJgof07ZjClI+XVPf16FeVghdTEWjNO5GBSu9gQfkmh/q2zVFRY9sTWaJyu0SkidH5QCdH56KR2a9FOF0GAacM6jMZi3aWhWN836gqmI2YwjUPsADKjMhrTbqYwPWUl4zbuObapg7qj6/pL2/To5PcuKEw9JiGTgye4F1n8fkylMIXrYu6lVSzV2xXElN6VqWtfmLw3w2e9ykOY0pTiIjR9vXWDH31ZaNV7/7m3tv0j8rUMYyo1eH4f0uD5RR3GFBpOkxuo2WcUj8OYgoYoVd98JsreW+ijr/lglyENOf9+/Psaoxi2d+xM7DnLvNs0dnX7/Byb8PBov3PyG1lMMEUBxNZUAAAAAElFTkSuQmCC)
=
4.89
CV = ![](data:image/png;base64,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)
=
20.41
Question 19.If the coefficient of variation of a collection of data is 57 and its S.D is 6.84, then find the mean.
Answer:Given SD = 6.84 and CV = 57, to find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
CV = ![](data:image/png;base64,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)
=
57
12
Question 20.A group of 100 candidates have their average height 163.8 cm with coefficient of variation 3.2. What is the standard deviation of their heights?
Answer:Given N = 100, CV = 3.2,
= 163.8 cm
CV = ![](data:image/png;base64,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)
=
3.2
5.24
Question 21.Given
and
. Find
.
Answer:SD = ![](data:image/png;base64,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)
Or SD2 = ![](data:image/png;base64,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)
To find ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAkAAAAXCAMAAADuk5XJAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOmY6Oma2OpDbZgAAZjpmZmYAZrb/kDoAkDo6kNv/tmYAtv//25A62////7Zm/9uQ//+2///bnU0dvQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAUUlEQVQYV52LSRKAIBADJ7ijIiMq/P+lZiiPXrQPqVQWkc94VNrj9anWJGAVURelBBoqs6nur34Yn2NiX8nz0uxmyhaz78wo1ycoAY4ZzP7nBifpArQuYXyPAAAAAElFTkSuQmCC)
= ![](data:image/png;base64,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)
SD2 =
= ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMQAAAAjCAMAAAD8OVVjAAAAAXNSR0IArs4c6QAAAJBQTFRFAAAAAAAAAAA6ADo6ADpmADqQAGaQAGa2OgAAOgA6OgBmOjoAOjpmOjqQOmZmOma2OpDbZgAAZgA6ZgBmZjoAZjpmZmaQZma2ZpDbZrbbZrb/kDoAkDo6kGY6kLbbkLb/kNv/tmYAtmY6tpBmttv/tv//25A625Bm27Zm27aQ2////7Zm/7aQ/9uQ//+2///bq/qGFgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAACb0lEQVRYR+2XjVPCIBjGYWWuT7MsS/twpVhbbP//f9fL+7INHE70ZHel3Ol5IPye5wEGY+xY/lkCxQvnp4vOTSGWCU5lAvzvx/kuKpbjlLH8fp6Pbjd0N2G7kJp9EMvyYR+aZAwmxCYN67jyGlyAj4dNM1HDwI5/bJhSoyQ8mhEWpOPPZMK0FJfS4kM5ZcUrj57Mdl1P9uWjC2UPV8EosPWx2cQVaboxv1PKEVtMewRfP+LyZoQmRD+V5+ieSlnP5MWCZeNUWg6daViw9bGtEi1tZSOaUFhlJcZUiinM71cczWSsXRkqhDKRDyfwL/xzWbAe+r7M8iFsLKvNZaKEUWLqy4+oUmpglQnEqhaaYzTBsl4qBilLSJwtFp3bLaUJ7OtVCIb6tyBCSm4TVS2tLRqSiZNyTaBJ/dyimSATjfqqr48LghkmPIgIaGL1UET9QPXaxPISFlPGnTPRtpx8DNQw04QPUYsrIbQATBPZgNpwmf68F9PTOWxhS1SRnKnNn/Tsjc10PW5sv2LCSJkX0V5OGmuYkFdKgIBHLChZxv0i4bAvLBN4RoHFfMSjZ0NsWe9/wlSwamP7Ea2UKjm0MtUqwi2DoQg9JcrTynJqj7nlhLE71jA6WYwJbCc6H//1TOibAD4z6mvHVibcB6rLdw3Th51nbO6UrI3dxMkYLwUBy+q1o5XYfu0IqPJfDJ3o2zHX17SgpirWXn5skLoXRnOQoAH9+cE7XU5/Pq2jgWMCxwQON4GM9z7jKPBlyow3CFBEb+qFqLtpDAEUYEB9OishgCHGbA0kBDDEmF2bgBeWPnzUC2U3pXNgN7YOgPILbNBL/DopONAAAAAASUVORK5CYII=)
Expanding the numerator in the form of (a - b)2 = a2 + b2 - 2ab,
SD2 =
= ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAOgAAAAjCAMAAACpWLY9AAAAAXNSR0IArs4c6QAAAJNQTFRFAAAAAAAAAAA6ADo6ADpmADqQAGaQAGa2OgAAOgA6OgBmOjoAOjpmOjqQOmZmOma2OpDbZgAAZgA6ZgBmZjoAZjpmZmaQZma2ZpDbZrbbZrb/kDoAkDo6kGY6kLbbkLb/kNv/tmYAtmY6tpBmttv/tv//25A625Bm27Zm27aQ29u22////7Zm/7aQ/9uQ//+2///bGCVr3gAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAC50lEQVRoQ+1X6XKbMBAWNK7pmTh1k9ZOE5M62C0UeP+n665Wl4VAErTMZIadcfKD/a6VxMHYUssE1ATahyS5enklAykSqt0Iv82XY7O9GQTG08cjugZcHM1mDY11tmNwFdP+ujsGZD7dl7yr+Tq8opo+gJQYlaFQRLfPyVGkB+jMIShfG/rrqvYZR8LaH0n6DUbzCZPWd5S3vxQ9QonBWb+3SXLNr5iIwKwSLLxdckjVdr8iszwi+XfV6fOW2yzWZf3uQO3VfVlD6KFS9IxJBld7c/vIznzkzEAE5lRg6c3k0Kp1hglFULR/ztJDnYn4hlaBQZvNDmigq37/0mzgcBO4vyQ9DWmwE+cHpRB9RlwkOHzlzeAwVWmv8CVq93hMq1VZXJcst2xR0FtoxivtA9nylqD3B1WnRiF6jLgkEay8YRoKdaHKr5tBWfFGbki+aOKmbAXlMwkoQe8NeqYjKodJHpURjxAHm0Fdqs/kWK8oO32AjVslzhWVW5cWP6AEvS9ooXIyjXAb6YoS2Ni6Boc6MJVQ0EH/PLX7q6N9otr8Ld6o8hXdjEK3rqQHqGBwDgfbKhqdRriNdPESLL2ZHEq1/ohPwkI8XjDtKVu3eQLn9GJF+UMYrjbbJP0OCLgZBSwnU/SgIRhcMDoiPKhGuI104QosvblUaWFwH9LjxbA/fI90P3DtDa3pQ6aCPQ6EbcR7alyq4m0J7lHihUEflcGgPQ9c24OmDw3qQEQHHVa1XwHrbOAFWL4C2va9ww7Na/R1jPwPkVhfs3iYRcSTfBYPo0TEV9qEfyp6rkj0W0mXl9rtXp9+pEiHLnZnevtHDdvLajXMIvJqt27sNJegNIFZdtUsIsuK/uMzsNAtE5gygSpZ/czSwA/sfqFImsh2S3ccukgf8Ut7yrAQG0kT2W65G4UuICT+JlYkTWS7HXSM52mSykEkTWT7EjR8H8In7xp+N+EIZ2ckTWS7JTkNPTHpAp9vAn8BxjVZKpdraIMAAAAASUVORK5CYII=)
Substituting all the known terms in the above equation,
SD2 =
= 7.77
We know that, SD2 = ![](data:image/png;base64,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)
7.77 = ![](data:image/png;base64,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)
= 1159
SD2 =
= 7.77
= 70
Question 22.The marks scored by two students A, B in a class are given below.
![](data:image/png;base64,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)
Who is more consistent?
Answer:SD of A
=
60
![](data:image/png;base64,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)
SD = ![](data:image/png;base64,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)
SDA = 5.4
SD of B
=
64
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAS4AAACnCAIAAAAg6iawAAAAAXNSR0IArs4c6QAAE3lJREFUeF7tXT2rXUUXfvP+AUnSSYqgaaxsooJfRYpEjOWV+AGSQgIqWIheMcYuhQZsNQkGrK4BA1YJNxYWKhaSIoJgEyVFsEoQ/AO+Dy5Z73Lvs/eZM3tm9tozzy7Cybnzsdaz1ppZM3Pm2Xv++uuv//AhAkRgbgT2SCju2bNnbknYPxFoEQGdC/8fitVPjxhuqGOLzu5YZ+uT/3UsJ0UjAg0hwFBsyNhU1TMCDEXP1qFsDSHAUGzI2M2q+tNPPz3zzDNYmOHfe/fu+cSBoejTLpVIBb8/d+7chQsXiumDHl944YVr167ZHt99990333zz7t27t27d+uWXX4oJs1lH2FSUfUX5UPHjU8e+taaYwJWOcP3Dhw9/9913UzQarzuE3muvvfbFF1/06544cSKfMBEtW3vxMGOzkct5aVcHNpgPAdf29nZ50DA3PvbYY19//fUDDzwgveMbzIcPPfTQ/v37y8sz1OM8hxnIUtDxvn37kLhfvnwZn5FI+AFlrSQQGzLjgfD4DEXwrK21tgAaQZuvv/46Sh46dAif0fjaWv4LwPU//PDDra0tETWT9YfQQ7xhArx06ZLG4ZUrV5588klEI8znFL2SCepHH3304IMPosebN28idUECEzGnR1eZnrwh10Ijv/76q6Re0CJaGK2Ipvbu3SvZFLxnZVoV3st0HcP7Gi8JrACRLZPD+iPoQQBxNjzIVzX8sibMm6Jn7fXPErGYCeFtwAVP4ThMtR4+f/48POz06dPjcShBu/LpuwKaQjTCUyfGYSodN/WnleWhzrFjxzp/CrR+EvSkkSS65GtkzlDElILu4dCqHiDr2yyH8qkMg7G2s/qf3jLGJjQ7fXiaLkkq5FeGYibrr0RvcaFY+jAD6TvG/vfee09XRMjgd3d3nabvPbGw0njnnXdu3LhhlxwyHUU/gAKzIibbDz74ILqRRVTMYf160Cu5VtQtZsyKOgnAh5YyK2IQgQqy1oXYkqNC+ClzkSyb0Q6mRGBi84WICWqKJBHdjVSxSzVdsEkGntD6I+j1BUirYJLW5klQYQB0LK4Mn8NnWVVfvXp1EaEo4ScyyzYApjJ8iRCaEgBoRNqBm8rMNmXFOEWSJL6ljciGCjJS+SaT9UfQw3penM3zM08oDiGyuLViXxE/AeBHEqCEYMCKcTwSMlm/MxC4jUZrr9Jrxf6S5s8//1zEOmdISLe/aZwd1bfeeuvLL7/s/AatI1Um62PV/cknn+j5/uxQhAgwfygeP34cvwxcrkO//PLLALqOc/kQjwkvg3N2bMj9/PPPI79BTW59+Q3qc889t6wfkABV/vAt3LUWUNLVD98WgNfcIs7zw7e5tWb/RMA1AqSZcm0eClc9AnoozQS1KlszQV2WOZmgLstelLYJBObfQW0CZipJBNYhwFBchxD/TgSKIMBQLAIzOyEC6xBgKK5DiH8nAkUQKBGK4DgRKgp9hD9CHmHFk0fYUKp/oL6q/Ntvv1Wvb7iC33///ZCfSCO2QG3eUuCSlDIp9H+VizsZ9loGsF77A+Lon/ai8ei6CSvibke+myhOdIyGq8PU1vEH+Aa+0dse0b34qWjtVWJWHBkUr1+/fvLkSS0AS3zzzTfhg+jiSko6sKCr0oURxm+47W9HMWbdvn1b50PQmeImx7J+5B0O4MyhCKxffPFFFRdseUeOHAmXfnElP/3007Nnzy5O7FkERi6Kkfqll16S3nd2duAt4HyYRZgSnZZJUK0mnauxlo1ryq3ZtVnH7MmbMLvItWl5kmeqs+u41gohBSQRVb42qQKsLHQoUEGmau1VmvFNyH805BCH6o7infmiMbebCrOGPCvvj0soWgWTrxtz6xgSSKnKCFy6d9DZVqhj3ThnKMrwJp6qtKJqPKHuSmXLTjuzu6n4liVfFBKNhPqmbS2hYHFN2Q0/dRttKuvAHSfwprWsvWZeK+o0Ih8OHjwo/lrlg/0GDDR37typUrscSumeDRrHJkLlBz8F1oo29GUe0JkBnzuHGfmogTzMGJJW2XE9rb4edNx0ZrDlMVTpCrCzlumk97LFMKUvD3VLJ6h2EYW+O6ttuxbPd6gI3J1YTqJRnuT6OtEx2sstOP3802ZM+RYy0cJHVLT24n3FHInVbG3yvuJs0Ed1zPuKUbCxEhHIiYCvbZucmrJtIuAaAYaia/NQuHYQYCi2Y2tq6hoBhqJr81C4dhAg+WI7tqamHhGQYzY8PMzwaJ5omXiYEQ3dLBV5mDEL7OyUCIwhwLUi/YMIuECAoejCDBSCCDAU6QNEwAUCDEUXZqAQRKBEKI6TL8IGuIeGrSRwmVRsD3BMWspJ0fTQoUOWa7Bi9deqBicBGiuLKUqdApVBVyIUgW/nSguIvQR0obXscJasNduyCogngTGpIzZ0x58sg8GQLy5L302lFVZYsLn1K8oYferUKUEJb6fWMvher5jhA/67ab/uyhe4OjzCgyq995kmIq5+ra0C6NeWyVegzwfR6SsJuca8Ok5Bb6WTYIxeebW6XxjwJr/8OUWdwLrWXoVmRXcjkD+Bzpw5Y8nv/AlYWiJkDRij33777ZCOQVaydAbdQqEITDWzv3z5cgi4jZSRHAzP559/rnl7I7qPqykkQEePHu2/x+Hxxx+HR1lHqoD2pkQobm9v63wNwhJwENcRjdBi+s4BhnMBB1TFba4VxwMSLNW6isF6Ut6TAWJicSTF3y4jFzqQlQhFCw1ARFr/7bffLhQvKzYo5e2SYKJGmBIx0te9jRwBkdLy44N9jwMcqQP+0nnlS4dihDEaqfL77783ommgmo8++ihKhmSesqrc2toKbNlnsRKhaDeakdTZFyH4BKWMVMi17GvJ8Bqfyt8JsSGsmAYBiB7GIt7wxhH7siNpD98/9dRT2D5d/GttChxmjJAvCtelfSx5duCOcGAx9BJYMm2xzqmp7s53yJeTvD9jLh2nINbZN+6/KkPdw/qGpWlc7sszrL14X3HDsdp3cd5X9G2frnS8r7gse1HaJhAosVZsAkgqSQSmIcBQnIYfaxOBRAgwFBMByWaIwDQEGIrT8GNtIpAIAZIvJgKSzRCBKARwDiT1eJgRhZ/XSjzM8GqZ1XLxMGNZ9qK0TSDAtWITZqaS/hFgKPq3ESVsAgGGYhNmppL+EWAo+rcRJWwCgXKhCPZBvXNtoRXCL3nauTgrPBp9OsYmnC5ASYCzkupBKALlsVfMApr0XqRQKAI4ZY7QgxRgAzRxCU2u2OAKDC6eNRKNdfNNRnu9Mv2sbAHeAg/RK1EgaonuyGHFEqGI+RCX8VZyKF28eFFIB/EI18bOzo5DmNKKBA4bXLfrXONM28VCW9Pxui8/xmhw22C8XvwV4QHbZA9FjHO4tg9mwb4A+BNGuAMHDuifQE8SQqCwUD8TsTEwgcWsshG9gEUwRtfNcpA9FIWyBTwImuKPL5AqoO4a8UvojkGdJIsRoYsxGr5hKfYqG7Wzh6KADpJP5VzA4rDK7Qq7L7VSQXwJ72EcRsShVLHvNRCC8JqisVAo3n///WoAgAhyyyF7LJcLdHd3V4eboXhDrq7jOj5jVMJ/a/Kn6DALqWhXiZLh//jjjyEVF1EmeyhiMwZArGQWBLIY2IQEWh5wrde6KId2iE9Lx6Rv0ahY5YQxUP0+QvZQhDFA6aWceZgBsA+GdwOJkfABHM/yGVtk5GVM6LuVNQWaU7iHHjbKEgCs0PWoWYB8EV1Ygj2cXtjJwf4pH/OiHGZO4QhMXnftu6UievSm46YqdAgpoY71FvvXDkfjph05KW/txfuK9Yyq0IT3FZdlTt5XXJa9KG0TCJRYKzYBJJUkAtMQYChOw4+1iUAiBBiKiYBkM0RgGgIMxWn4sTYRSIQAyRcTAclmiEAUAiRfjILNfSUeZrg30b8E5GHGsuxFaZtAgGvFJsxMJf0jwFD0byNK2AQCDMUmzEwl/SPAUPRvI0rYBALlQnEl+SLuvFiKBHxGsWqAB0PZcm9C+7GCOIkfeTJJUigUh8gXRSt7YwV34TOpWrJZIXfFzcySndbXF8YywKg3WutT0GpUIhRHyBdrBVcu7IM6pFYFy+gF1gzAqPScZTqdq5fsoThCvjiXzuyXCDhEIHsohpAv1kq97tDeFMktAtlDUTQfIl8EN4kuFEGXgMVVZS9CcGt4CuYNgUKhGEK+COIz8NyA9M0bRpSHCBRAIHsojpAvFlCPXRCBpSCQPRQBxAj5Io7dlJAX5Iug6FWaxqUgSDmJQBoE5iVftMyL0Kca8sWOXsWYAoGhE1rBJGL0T4OSNOunEWsvki+mGdGctML7ik4MESgG7ysGAsViRKAcAiXWiuW0YU9EYLEIMBQXazoKXhcCDMW67EltFosAQ3GxpqPgdSFA8sW67EltloYAyReXZrEweXmYEYaTl1I8zPBiCcpBBBQBrhXpDETABQIMRRdmoBBEgKFIHyACLhBgKLowA4UgAuVCcSX5ohhA+NHk0TtTi7YNLn+pRuRfnG5K4V/Ev9ObcttCoVAcIV+UK4t6bwV3+d2CFSgY1IFSqhE+10TuGghCwmK4yNoE/2KB+4rH/n5WXhLDvb7q7/IJd2CZO3LFOiqjDnoB4xGUwkVW/Aski/VbpiNrr+yz4jj5Iq7tnz17NuEIyqYqQwAjNSJQaFnqfrKH4gj5oiwLz5w5o8uqKhM5JFedS/11u1RC7ZDbIw5BC5iwTbdNZQ9F0XyIfBF/wqyoycCtW7cqi0aog3EdZOFuPcCtYIjDU6dONRKHsEKhUBwhXzxw4IB6A8Ly+vXrbp1jU8EQhxhc8GxakeUFAfDiasaE/yK/qHg7OnsojpAvYrMUM8adO3eq9Dw4DeNwimWBnt07QVNIVise17KHIhAcIV9EBmL3qetYVmENjLH86NGjFfvNlBhj3dUIFDjMQBd236KzJW0J9vA53yYy9M/XuG155ZuPsqqmvRfTsQyS/VmxfL9Ze7T2IvliVWM07ysuy5y8r7gse1HaJhAosVZsAkgqSQSmIcBQnIYfaxOBRAgwFBMByWaIwDQEGIrT8GNtIpAIAZIvJgKSzRCBKARIvhgFm/tKPMxwb6J/CcjDjGXZi9I2gQDXik2YmUr6R4Ch6N9GlLAJBBiKTZh56UqC3uaRRx7Bymrfvn21kk0xFJfupTHynzt3Tu8Bdj7ENJe/zg8//PD888/fvXsXoYhr6Pk7nKGH+FAUPrwOY6LaOKEqK/2mssv+CeEKaWpra2vv3r0d7q8+6wdue73//vvXrl0LaTNhGXQKo9sGt/9+9u/fD7FzcALKvbZxfkd7ibmjrOXajPfM6EtS9ioQrCjt6I2nhFdLUrUJ+BJK5bOpcB3Pnz+PwlevXlVFMOfY6jdv3jx8+DAI17Jq2g9g6Q7epU6lAoD3DSJBzoQidW60DZHK4Y67irryQlwcUBbwf7wz3IQqh+qAwRXVARNDMaGLxDW1kR1hOEwyQ56Nv4pN53pOnDhhA0OGhrRxaINcIm1lKEq+IH5uERZiSHX+CKBsa/EJqg4SoGzD50AORcsR3lmlrOUF1/Kd7CVhatRUU0g3/vjjjzfeeKOvNbZJQEGglIdqNSCfcA0y3uwrr7zy8ccfi2z37t179dVXP/vsM+So2L8ZMtPQAngKIw70BUUosDpy5Ein3ytXrsg3Ozs72jWgi/Si6QkqxgYZNjCIpkomhwYYnYr72cvaMWmjGWNtaz4LbKqj2Ks/++H706dPq46YkTB/oiQSWsxL+JxkwlzbLHqUxM+uYzE3JgdfKI/xdGZF+V4W1X3fVqmklv43HBxrrwQJqoCFRpFP5w5FdNTPEwINs6mbBjbrqtimOiKuYLU+dzu+6TCAIGzQuGz24PO41mqjzvzQ72i82ZXDRA7AV4ai5J/KXj8SiiKS5qvh5CnWXgkSVIEb3UOUixcvRs7OrDYfArLEGH8efvhh7PQgoUUxfNbCSMz6Kdnu7u7KgMH3nV6Gml0nTom/S/4Jr5bkE0yQ0uvaF+ncvn07Qr5koYi9ZpF7XIiItSLeM9Vvc2jcjYCg5SpYKIJ0L4QGH6u1r776ClspIKq1FkHIhVQfAnmo2TijpF0rwqXtgGJnRSFKfvrpp0XOzjbHwYMHY+RPslaURuy+cMIsAhmCJgnaRXg6rpIAnYRS+WxqIx0B5tC6C25nV+MYYVFSTj7wAb3I6ki8Mzwf64A21Kw1WdwhwabWGVorDoWifi+HHIKVrhXDe7f2SrZWlO71+CVcmrUlLTujDDZrq6wsEF0xrrtZaoXrCBeHsTqOrkMeXNO+4Utgl+MNHe/xWY4io0NxqFmBDpFf4C1j4UyZQ/sgNkEbemPakDOkCcVZXG1Kp+FuOqWXeeuG64jJrbNbCNe31VEgJPWYEorjWMGt63uLW0dlC3iytWJMcsw6MyGAs7IbN26Ai90uro4fP27FwSHeyZMnxw97sdLLpAF+VokfuLXz7hrASEriTL40T7Npb/EjDi9duvTEE088++yzK/VBSMu+Igb7hArjN6j33XefbATW/Vh7MRSrsnXaUKwKGpfKkFDDpVkoVNsIcK3Ytv2pvRsESL7oxhQUpEkEdJn9Tyg2CQKVJgKOEGCC6sgYFKVlBBiKLVufujtCgKHoyBgUpWUEGIotW5+6O0KAoejIGBSlZQT+B7sCPcTl3n/kAAAAAElFTkSuQmCC)
SD = ![](data:image/png;base64,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)
SDB = 6.7
5.4<6.7
SD of A is less than SD of B.
A is more consistent than B
Find the range and coefficient of range of the following data.
59, 46, 30, 23, 27, 40, 52, 35, 29
Answer:
Given the set of data 59, 46, 30, 23, 27, 40, 52, 35, 29.
Let the maximum value in the data set be denoted by xmax and minimum by xmin
Range = Maximum value in the data set – Minimum value in the data set
= xmax - xmin
= 59 – 23
= 36
∴ Range R = 36
Let the Coefficient of Range be denoted by s.
s =
=
=
= 0.439≈0.44
∴ s = 0.44
Question 2.
Find the range and coefficient of range of the following data.
41.2, 33.7, 29.1, 34.5, 25.7, 24.8, 56.5, 12.5
Answer:
Given the set of data 41.2, 33.7, 29.1, 34.5, 25.7, 24.8, 56.5, 12.5.
Let the maximum value in the data set be denoted by xmax and minimum by xmin
Range = Maximum value in the data set – Minimum value in the data set
= xmax - xmin
= 56.5 – 12.5
= 44
∴ Range R = 44
Let the Coefficient of Range be denoted by s.
s =
=
=
= 0.637≈0.64
∴ s = 0.64
Question 3.
The smallest value of a collection of data is 12 and the range is 59. Find the largest value of the collection of data.
Answer:
Given xmin = 12, R = 59
Range R = xmax - xmin
Substituting the given terms into the above formula, we get
R = xmax - xmin
59 = xmax - 12
xmax = 59 + 12 = 71
Question 4.
The largest of 50 measurements is 3.84 kg. If the range is 0.46 kg, find the smallest measurement.
Answer:
Given xmax = 3.84kg, R = 0.46kg
Range R = xmax - xmin
Substituting the given terms into the above formula, we get
R = xmax - xmin
0.46 = 3.84- xmin
xmin = 3.84 - 0.46 = 3.38kg
Question 5.
The standard deviation of 20 observations is √5. If each observation is multiplied by 2, find the standard deviation and variance of the resulting observations.
Answer:
Let the observations be x1, x2, x3….x20
Given SD =
Formula for SD =
Where N = 20, SD =
If each observation is multiplied by 2, we get new set of observations.
Let the new observations be y1, y2, y3….y20
New Standard Deviation SD’ =
We know that and
But yi = 2xi
Therefore,
Substituting in the SD’ formula,
SD’ =
=
= 2 × SD = 2
Formula relating SD and Variance is Variance = SD2
So Variance = (22 = 20
Question 6.
Calculate the standard deviation of the first 13 natural numbers.
Answer:
To find
Formula for
=
SD =
= 3.74
Question 7.
Calculate the standard deviation of the following data.
10, 20, 15, 8, 3, 4
Answer:
To find
Formula for
=
SD =
= 5.97
Question 8.
Calculate the standard deviation of the following data.
38, 40, 34 ,31, 28, 26, 34
Answer:
To find
Formula for
=
SD =
= 4.69
Question 9.
Calculate the standard deviation of the following data.
Answer:
To find
Formula for
=
SD =
=
= 6.32
Question 10.
The number of books bought at a book fair by 200 students from a school are given in the following table.
Calculate the standard deviation.
Answer:
To find
Formula for
=
SD =
=
= 1.107
Question 11.
Calculate the variance of the following data
Answer:
To find
Formula for
=
SD =
=
= 3.88
To find Variance
Variance = SD2
= 3.882 = 15.058
Question 12.
The time (in seconds) taken by a group of people to walk across a pedestrian crossing is given in the table below.
Calculate the variance and standard deviation of the data.
Answer:
To find
Formula for
=
SD =
=
= 6.063
To find Variance
Variance = SD2
= 6.0632 = 36.76
Question 13.
A group of 45 house owners contributed money towards green environment of their street. The amount of money collected is shown in the table below.
Calculate the variance and standard deviation.
Answer:
To find
Formula for
=
SD =
=
= 20.39
To find Variance
Variance = SD2
= 20.392 = 416
Question 14.
Find the variance of the following distribution.
Answer:
To find
Formula for
=
SD =
=
= 7.361
To find Variance
Variance = SD2
= 7.3612 = 54.19
Question 15.
Mean of 100 items is 48 and their standard deviation is 10. Find the sum of all the items and the sum of the squares of all the items.
Answer:
Mean of 100 items = 48
Standard deviation SD = 10
To find :
To find :
Formula for
= 48 =
Sum of all the items
To find:
SD2 =
100 = 482
240400
Question 16.
The mean and standard deviation of 20 items are found to be 10 and 2 respectively. At the time of checking it was found that an item 12 was wrongly entered as 8. Calculate the correct mean and standard deviation.
Answer:
Mean of 20 items = 10
SD of 10 items = 2
Wrong Value = 8
Correct Value = 12
To find Mean:
Formula for
= 10 =
Sum of all the items
200
This value is wrong since item 12 was wrongly entered as 8.
Let be the corrected sum
200 + (Correct Value) - (Wrong Value)
= 200 + 12 - 8 = 204
Corrected mean will be =
10.2
To find SD:
SD2 =
4 =
= 2080
This value is wrong since item 12 was wrongly entered as 8.
Let be the corrected sum of squares.
= 2080 + (Correct Value)2 - (Wrong Value)2
= 2080 + 122 - 82
= 2160
Corrected SD:
SD2 =
SD2 =
SD = 1.99
The corrected mean is 10.2, corrected SD is 1.99
Question 17.
Answer:
SD2 =
SD2 =
SD = 3
Coefficient of Variation
CV =
= = 25
Question 18.
Calculate the coefficient of variation of the following data: 20, 18, 32, 24, 26.
Answer:
To find
Formula for
= 24
SD =
= 4.89
CV =
= 20.41
Question 19.
If the coefficient of variation of a collection of data is 57 and its S.D is 6.84, then find the mean.
Answer:
Given SD = 6.84 and CV = 57, to find
CV =
= 57
12
Question 20.
A group of 100 candidates have their average height 163.8 cm with coefficient of variation 3.2. What is the standard deviation of their heights?
Answer:
Given N = 100, CV = 3.2, = 163.8 cm
CV =
= 3.2
5.24
Question 21.
Given and
. Find
.
Answer:
SD =
Or SD2 =
To find
=
SD2 =
=
Expanding the numerator in the form of (a - b)2 = a2 + b2 - 2ab,
SD2 = =
Substituting all the known terms in the above equation,
SD2 = = 7.77
We know that, SD2 =
7.77 =
= 1159
SD2 = = 7.77
= 70
Question 22.
The marks scored by two students A, B in a class are given below.
Who is more consistent?
Answer:
SD of A
=
60
SD =
SDA = 5.4
SD of B
=
64
SD =
SDB = 6.7
5.4<6.7
SD of A is less than SD of B.
A is more consistent than B
Exercise 11.2
Question 1.The range of the first 10 prime numbers 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 is
A. 28
B. 26
C. 29
D. 27
Answer:Range R = xmax - xmin
R = 29 - 2 = 27
So the correct option is D.
Question 2.The least value in a collection of data is 14.1. If the range of the collection is 28.4, then the greatest value of the collection is
A. 42.5
B. 43.5
C. 42.4
D. 42.1
Answer:Given xmin = 14.1, R = 28.4
Range R = xmax - xmin
28.4 = xmax - 14.1
xmax = 42.5
So the correct option is A.
Question 3.The greatest value of a collection of data is 72 and the least value is 28. Then the coefficient of range is
A. 44
B. 0.72
C. 0.44
D. 0.28
Answer:Given xmax = 72, xmin = 28
Coefficient of range s = ![](data:image/png;base64,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)
= ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAG0AAAAgCAMAAAAv4AbWAAAAAXNSR0IArs4c6QAAAGxQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOgA6OjqQOmaQOma2OpDbZgAAZgA6ZjoAZmZmZrbbZrb/kDoAkDo6kDqQkGY6kLbbkNv/tmYAttv/tv//25A627Zm29u22////7Zm/9uQ/9u2//+2///bTx2M8wAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAABoklEQVRIS91W2XKDMAyUe9AUSFLoAS0Ug/3//1jLl0xDGpJROp3qgdEI0Hol2V6Af226FsbuAT53QuQp1RCYzIvHgacI+mUA/dyA2r5Bf9NQ0hDQdQFTWfCgYRbpOY0PCRq+wIAqK4DWkGcypGat+84AA7rOhwkRmUz6hfdIUZXYRyEwvQ0A9i1n6ptZvKfWzWYEqdqA2j0ZfmyV9NSweTItmA+MGSeap+YqmKDFQJ+t3QH6VYjNe9LfLg45eUztN/W+G6b69iPmG7OARh4XGEjMLak+qtx7NPLYwKBFWqqMA9UWFt8YeWxoWEhEw6c9MzaDRyPvErDWbUlj6VHn0NwTd862cbUlL/62wjm1rjkaHq+I1lXRO5XgrPehkq5vMqy/it5Z2ejj5UqGKUkO2zAlluWFYMd+63D4kx2QYvCj4TjacsZdcM2zxN4WeMsHNNMxX0DymOv5Q7rjqsT+xCxNFlWJrv1VcA1pcqBKIhrSY5Ymh6pkhsYsTWaqZK5LDDVmabKgShJu3NJkQZUQGrc0WVIlNJNBq6yXJr+3ef8W0hdTTiP6awJdSQAAAABJRU5ErkJggg==)
So the correct option is C.
Question 4.For a collection of 11 items,
, then the arithmetic mean is
A. 11
B. 12
C. 14
D. 13
Answer:N = 11
AM ![](data:image/png;base64,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)
So the correct option is B.
Question 5.For any collection of n items,
=
A. ![](data:image/png;base64,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)
B. ![](data:image/png;base64,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)
C. ![](data:image/png;base64,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)
D. 0
Answer:
can be rewritten as ![](data:image/png;base64,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)
We know that
so ![](data:image/png;base64,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)
Also,
because
is just a constant term.
Substituting these in
, we get
= 0
So the correct option is D.
Question 6.For any collection of n items,
=
A. ![](data:image/png;base64,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)
B. ![](data:image/png;base64,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)
C. ![](data:image/png;base64,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)
D. 0
Answer:We know that
so ![](data:image/png;base64,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)
Substituting this in the question, we have
![](data:image/png;base64,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)
So the correct option is C.
Question 7.If t is the standard deviation of x, y. z, then the standard deviation of x + 5, y + 5, z + 5 is
A. ![](data:image/png;base64,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)
B. t + 5
C. t
D. x y z
Answer:It is a direct consequence of the theory of standard deviation that, the same constant term added to the terms in a set of data doesn’t change the value of standard deviation.
So the correct answer is t and correct option is C.
Question 8.If the standard deviation of a set of data is 1.6, then the variance is
A. 0.4
B. 2.56
C. 1.96
D. 0.04
Answer:Variance = SD2
So Variance = 1.62 = 2.56
Therefore, the correct option is B.
Question 9.If the variance of a data is 12.25, then the S.D is
A. 3.5
B. 3
C. 2.5
D. 3.25
Answer:Variance = SD2
Or SD = ![](data:image/png;base64,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)
SD =
3.5
So the correct option is A.
Question 10.Variance of the first 11 natural numbers is
A. ![](data:image/png;base64,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)
B. ![](data:image/png;base64,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)
C. ![](data:image/png;base64,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)
D. 10
Answer:Variance of first n natural numbers is given by ![](data:image/png;base64,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)
V =
10
So the correct option is D.
Question 11.The variance of 10, 10, 10, 10, 10 is
A. 10
B. ![](data:image/png;base64,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)
C. 5
D. 0
Answer:Standard deviation of a constant set of data is 0.
We know that Variance = SD2
Variance = 0
So the correct option is D.
Question 12.If the variance of 14, 18, 22, 26, 30 is 32, then the variance of 28, 36,44,52,60 is
A. 64
B. 128
C. 32√2
D. 32
Answer:Two data sets are given.
Set A : 14, 18, 22, 26, 30 with V = 32
Set B : 28, 36, 44, 52, 60 with V = ?
We notice that each data entry in set B is twice the corresponding data entry in set A.
SD of set B = 2 × SD of set A.
We know that Variance = SD2
Variance of Set B = 22 × Variance of Set A.
= 4 × 32 = 128.
So the correct option is B.
Question 13.Standard deviation of a collection of data is 2√2. If each value is multiplied by 3, then the standard deviation of the new data is
A. ![](data:image/png;base64,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)
B. 4√2
C. 6√2
D. 9√2
Answer:SD of set B = n × SD of set A, where n is the data multiplier
Here, n = 3.
So, SD of set B = 3 × SD of set A = 3 × 2
= 6![](data:image/png;base64,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)
So, the correct option is C.
Question 14.Given
and n = 12. The coefficient of variation is
A. 25
B. 20
C. 30
D. 10
Answer:Insufficient data.
Question 15.Mean and standard deviation of a data are 48 and 12 respectively. The coefficient of variation is
A. 42
B. 25
C. 28
D. 48
Answer:CV = ![](data:image/png;base64,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)
CV = ![](data:image/png;base64,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)
So, the correct option is B.
The range of the first 10 prime numbers 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 is
A. 28
B. 26
C. 29
D. 27
Answer:
Range R = xmax - xmin
R = 29 - 2 = 27
So the correct option is D.
Question 2.
The least value in a collection of data is 14.1. If the range of the collection is 28.4, then the greatest value of the collection is
A. 42.5
B. 43.5
C. 42.4
D. 42.1
Answer:
Given xmin = 14.1, R = 28.4
Range R = xmax - xmin
28.4 = xmax - 14.1
xmax = 42.5
So the correct option is A.
Question 3.
The greatest value of a collection of data is 72 and the least value is 28. Then the coefficient of range is
A. 44
B. 0.72
C. 0.44
D. 0.28
Answer:
Given xmax = 72, xmin = 28
Coefficient of range s =
=
So the correct option is C.
Question 4.
For a collection of 11 items, , then the arithmetic mean is
A. 11
B. 12
C. 14
D. 13
Answer:
N = 11
AM
So the correct option is B.
Question 5.
For any collection of n items, =
A.
B.
C.
D. 0
Answer:
can be rewritten as
We know that so
Also, because
is just a constant term.
Substituting these in , we get
= 0
So the correct option is D.
Question 6.
For any collection of n items, =
A.
B.
C.
D. 0
Answer:
We know that so
Substituting this in the question, we have
So the correct option is C.
Question 7.
If t is the standard deviation of x, y. z, then the standard deviation of x + 5, y + 5, z + 5 is
A.
B. t + 5
C. t
D. x y z
Answer:
It is a direct consequence of the theory of standard deviation that, the same constant term added to the terms in a set of data doesn’t change the value of standard deviation.
So the correct answer is t and correct option is C.
Question 8.
If the standard deviation of a set of data is 1.6, then the variance is
A. 0.4
B. 2.56
C. 1.96
D. 0.04
Answer:
Variance = SD2
So Variance = 1.62 = 2.56
Therefore, the correct option is B.
Question 9.
If the variance of a data is 12.25, then the S.D is
A. 3.5
B. 3
C. 2.5
D. 3.25
Answer:
Variance = SD2
Or SD =
SD = 3.5
So the correct option is A.
Question 10.
Variance of the first 11 natural numbers is
A.
B.
C.
D. 10
Answer:
Variance of first n natural numbers is given by
V = 10
So the correct option is D.
Question 11.
The variance of 10, 10, 10, 10, 10 is
A. 10
B.
C. 5
D. 0
Answer:
Standard deviation of a constant set of data is 0.
We know that Variance = SD2
Variance = 0
So the correct option is D.
Question 12.
If the variance of 14, 18, 22, 26, 30 is 32, then the variance of 28, 36,44,52,60 is
A. 64
B. 128
C. 32√2
D. 32
Answer:
Two data sets are given.
Set A : 14, 18, 22, 26, 30 with V = 32
Set B : 28, 36, 44, 52, 60 with V = ?
We notice that each data entry in set B is twice the corresponding data entry in set A.
SD of set B = 2 × SD of set A.
We know that Variance = SD2
Variance of Set B = 22 × Variance of Set A.
= 4 × 32 = 128.
So the correct option is B.
Question 13.
Standard deviation of a collection of data is 2√2. If each value is multiplied by 3, then the standard deviation of the new data is
A.
B. 4√2
C. 6√2
D. 9√2
Answer:
SD of set B = n × SD of set A, where n is the data multiplier
Here, n = 3.
So, SD of set B = 3 × SD of set A = 3 × 2 = 6
So, the correct option is C.
Question 14.
Given and n = 12. The coefficient of variation is
A. 25
B. 20
C. 30
D. 10
Answer:
Insufficient data.
Question 15.
Mean and standard deviation of a data are 48 and 12 respectively. The coefficient of variation is
A. 42
B. 25
C. 28
D. 48
Answer:
CV =
CV =
So, the correct option is B.