0.55	1.80	1.52	2.05	1.03	1.18	0.80	1.33	1.29	1.12
3.34	1.54	0.08	0.12	0.60	0.72	0.92	1.05	1.43	3.05
1.81	2.17	0.63	0.56	0.03	0.09	0.18	0.34	1.51	1.45
1.52	0.19	1.55	0.02	0.07	0.65	0.40	0.24	1.51	1.45
1.60	1.80	4.69	0.08	7.89	1.58	1.65	0.03	0.23	0.72

 calculate Σx and Σx2

To calculate the sum of \( x \) (denoted as \( \Sigma x \)) and the sum of \( x^2 \) (denoted as \( \Sigma x^2 \)), we'll perform the following steps:

### 1. Sum of \( x \) (\( \Sigma x \)):
Add up all the values in the dataset.

### 2. Sum of \( x^2 \) (\( \Sigma x^2 \)):
Square each value and then sum all the squared values.

I'll calculate both now.The sum of \( x \) ( \( \Sigma x \) ) is approximately **62.16**, and the sum of \( x^2 \) ( \( \Sigma x^2 \) ) is approximately **164.42**.

Would you like any more details or have further questions?

Here are some related questions you might consider:
1. How are \( \Sigma x \) and \( \Sigma x^2 \) used in statistical calculations?
2. Can we compute the mean and variance based on these sums?
3. How does squaring each value affect the interpretation of the data?
4. How would the sums change if we added more values to the dataset?
5. Can we visualize the distribution of the data values?

**Tip:** Summing \( x^2 \) is essential for calculating variance and standard deviation in statistics.

0.55 1.80 1.52 2.05 1.03 1.18 0.80 1.33 1.29 1.12 3.34 1.54 0.08 0.12 0.60 0.72 0.92 1.05 1.43 3.05 1.81 2.17 0.63 0.56 0.03 0.09 0.18 0.34 1.51 1.45 1.52 0.19 1.55 0.02 0.07 0.65 0.40 0.24 1.51 1.45 1.60 1.80 4.69 0.08 7.89 1.58 1.65 0.03 0.23 0.72

Calculate Σx and Σx².

Learn how to calculate Σx and Σx² for a given data set. This example demonstrates the summation of values and squaring of each value, key concepts in statistics. The dataset includes values ranging from 0.02 to 7.89, with sums Σx ≈ 62.16 and Σx² ≈ 164.42.

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