To calculate $\Sigma x^2$, we need to square each value in the dataset $x$ and then sum the squared values.

Given the data:

$x = [22.4, 30.3, 54.8, 66.5, 72.4, 88.9]$

Now, we square each value:

$22.4^2 = 501.76$ $30.3^2 = 918.09$ $54.8^2 = 3007.04$ $66.5^2 = 4422.25$ $72.4^2 = 5243.76$ $88.9^2 = 7905.21$

Now, summing them up:

$\Sigma x^2 = 501.76 + 918.09 + 3007.04 + 4422.25 + 5243.76 + 7905.21 = 24098.11$

Thus,

$\Sigma x^2 = 24098.11$

Would you like to explore further or have any questions about the process?

Here are 5 related questions for you:

1. How do you calculate the mean of the dataset?
2. How do you find the variance and standard deviation from this data?
3. How would you calculate the sum of $x$ and $x^2$ for a larger dataset?
4. How does squaring the data affect the calculation of the mean?
5. How can you use the sum of squares in regression analysis?

Tip: When dealing with squared values, it's a good idea to use them in variance and standard deviation calculations to understand data spread and dispersion better.