In the data set below, what is the variance? 8 7 6 4 3 If the answer is a decimal, round it to the nearest tenth. variance (σ2):__

Calculate squared differences: Now, we will calculate the squared differences from the mean for each data point. (8 - 5.6)^2 + (7 - 5.6)^2 + (6 - 5.6)^2 + (4 - 5.6)^2 + (3 - 5.6)^2 = (2.4)^2 + (1.4)^2 + (0.4)^2 + (-1.6)^2 + (-2.6)^2 = 5.76 + 1.96 + 0.16 + 2.56 + 6.76 = 17.2Sum squared differences: Finally, we will divide the sum of squared differences by the number of data points to find the variance. σ^2 = Σ(x_i - μ)^2 / N σ^2 = 17.2 / 5 σ^2 = 3.44 Since we need to round to the nearest tenth, the variance σ^2 ≈ 3.4.

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In the data set below, what is the variance? $\newline$ $8$ $7$ $6$ $4$ $3$ $\newline$ If the answer is a decimal, round it to the nearest tenth. $\newline$ variance ( $\sigma^2$ ):__

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Algebra 1

Variance and standard deviation

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Q. In the data set below, what is the variance?

\newline

8

7

6

4

3

\newline

If the answer is a decimal, round it to the nearest tenth.

\newline

variance (

\sigma^2

):__

Calculate squared differences: Now, we will calculate the squared differences from the mean for each data point. $\newline$ $(8 - 5.6)^2 + (7 - 5.6)^2 + (6 - 5.6)^2 + (4 - 5.6)^2 + (3 - 5.6)^2$ $\newline$ $= (2.4)^2 + (1.4)^2 + (0.4)^2 + (-1.6)^2 + (-2.6)^2$ $\newline$ $= 5.76 + 1.96 + 0.16 + 2.56 + 6.76$ $\newline$ $= 17.2$
Sum squared differences: Finally, we will divide the sum of squared differences by the number of data points to find the variance. $\newline$ $\sigma^2 = \frac{\Sigma(x_i - \mu)^2}{N}$ $\newline$ $\sigma^2 = \frac{17.2}{5}$ $\newline$ $\sigma^2 = 3.44$ $\newline$ Since we need to round to the nearest tenth, the variance $\sigma^2 \approx 3.4$ .