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A study of the relationship between age and variousvisual functions (such as acuity and depth perception)reported the following observations on the area of sclerallamina (\({\bf{m}}{{\bf{m}}^2}\)) from human optic nerve heads(鈥淢orphometry of Nerve Fiber Bundle Pores in theOptic Nerve Head of the Human,鈥 Experimental EyeResearch, 1988: 559鈥568):

2.75 2.62 2.74 3.85 2.34 2.74 3.93 4.21 3.88

4.33 3.46 4.52 2.43 3.65 2.78 3.56 3.01

a. Calculate \(\sum {{x_i}} \)and \(\sum {x_i^2} \).

b. Use the values calculated in part (a) to compute the sample variance s2 and then the sample standard deviation s.

Short Answer

Expert verified

a.\(\sum {{x_i}} = 56.8\;m{m^2}\)and\(\sum {x_i^2} = 197.804\;m{m^2}\;square\).

b. The sample variance is 0.5016 \({\rm{m}}{{\rm{m}}^{\rm{2}}}\) square. The sample standard deviation is 0.708 \({\rm{m}}{{\rm{m}}^{\rm{2}}}\).

Step by step solution

01

 Step 1: Given information

The data is provided as,

2.75

2.62

2.74

3.85

2.34

2.74

3.93

4.21

3.88

4.33

3.46

4.52

2.43

3.65

2.78

3.56

3.01


The size of the sample is 17.

02

Compute \(\sum {{x_i}} \) and \(\sum {x_i^2} \)

a.

Let x represents the sample values.

The symbol \(\sum {} \)shows the summation of a range of values.

The table representing the calculations are as follows,

S.No

\({x_i}\)

\(x_i^2\)

1

2.75

7.5625

2

2.62

6.8644

3

2.74

7.5076

4

3.85

14.8225

5

2.34

5.4756

6

2.74

7.5076

7

3.93

15.4449

8

4.21

17.7241

9

3.88

15.0544

10

4.33

18.7489

11

3.46

11.9716

12

4.52

20.4304

13

2.43

5.9049

14

3.65

13.3225

15

2.78

7.7284

16

3.56

12.6736

17

3.01

9.0601

Therefore, the values are,

\(\begin{array}{c}\sum {{x_i}} &=& 2.75 + 2.62 + ... + 3.01\\ &=& 56.8\\\sum {x_i^2} &=& {2.75^2} + {2.62^2} + ... + {3.01^2}\\ &=& 197.804\end{array}\)

Therefore, \(\sum {{x_i}} = 56.8\;m{m^2}\) and \(\sum {x_i^2} = 197.804\;m{m^2}\;square\)

03

Compute the sample variance and sample standard deviation

b.

Referring to part a, the values are,

\(\sum {{x_i}} = 56.8\)and\(\sum {x_i^2} = 197.804\)

The sample variance is given as,

\({s^2} = \frac{{{S_{xx}}}}{{n - 1}}\)

Where,

\({S_{xx}} = \sum {x_i^2} - \frac{{{{\left( {\sum {{x_i}} } \right)}^2}}}{n}\)

The sample variance is computed as,

\(\begin{array}{c}{s^2} &=& \frac{{{S_{xx}}}}{{n - 1}}\\ &=& \frac{{197.804 - \frac{{{{\left( {56.8} \right)}^2}}}{{17}}}}{{17 - 1}}\\ &=& 0.5016\end{array}\)

Therefore, the sample variance is 0.5016\(m{m^2}\;square\).

The sample standard deviation is computed as,

\(\begin{array}{c}s &=& \sqrt {{s^2}} \\ &=& \sqrt {0.5016} \\ &=& 0.708\end{array}\)

Therefore, the sample standard deviation is 0.708\(m{m^2}\).

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