Answer:
(a) [tex]\sum x_i=56.68[/tex] and [tex]\sum x_i^2=197.46[/tex]
(b) The sample variance is [tex]s^2=0.530[/tex] and the sample standard deviation is [tex]s=0.728[/tex]
Step-by-step explanation:
(a)
The sum of these 17 sample observations is
[tex]\sum x_i=2.79\:+2.57+\:2.73\:+3.75\:+2.27+\:2.75+\:4.00+\:4.22+\:3.88+\:4.35+\:3.41+\:4.57+\:2.38+\:3.73\:+2.75+\:3.47+\:3.06\\\\\sum x_i=56.68[/tex]
and the sum of their squares is
[tex]\sum x_i^2=2.79^2\:+2.57^2+\:2.73^2\:+3.75^2\:+2.27^2+\:2.75^2+\:4.00^2+\:4.22^2+\:3.88^2+\:4.35^2+\:3.41^2+\:4.57^2+\:2.38^2+\:3.73^2\:+2.75^2+\:3.47^2+\:3.06^2\\\\\sum x_i^2=197.46[/tex]
(b)
The sample variance, denoted by [tex]s^2[/tex], is given by
[tex]s^2=\frac{S_{xx}}{n-1}[/tex]
where [tex]S_{xx} =\sum x_i^2-\frac{(\sum x_i)^2}{n}[/tex]
Applying the above formula we get that
[tex]S_{xx}=197.46-\frac{(56.68)^2}{17}\\\\S_{xx} =8.482[/tex]
[tex]s^2=\frac{8.482}{17-1}=0.530[/tex]
The sample standard deviation, denoted by s, is the (positive) square root of the variance:
[tex]s=\sqrt{s^2}[/tex]
Applying the above formula we get that
[tex]s=\sqrt{0.530}=0.728[/tex]