Sum of Deviations: Proving $\sum_{i=1}^Nv_i(v_i - \langle v \rangle) = 0$

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In summary: You can do this by grouping and factoring the terms to show that they all cancel out, leaving the sum equal to ##0##.
  • #1
kubaanglin
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Homework Statement


The average value of N measurements of a quantity ##v_i## is defined as
$$ \langle v \rangle \equiv \frac {1}{N} \sum_{i=1}^Nv_i = \frac {1}{N}(v_1 + v_2 + \cdots v_N)$$
The deviation of any given measurement ##v_i## from the average is of course ##(v_i - \langle v \rangle)##. Show mathematically that the sum of all the deviations is zero; i.e. show that
$$\sum_{i=1}^Nv_i(v_i - \langle v \rangle) = 0$$

Homework Equations


##?##

The Attempt at a Solution


I understand that this is simply describing an average, but I am not sure how to express this mathematically. It makes sense to me that the sum of the deviations would be zero.
 
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  • #2
kubaanglin said:

Homework Statement


The average value of N measurements of a quantity ##v_i## is defined as
$$ \langle v \rangle \equiv \frac {1}{N} \sum_{i=1}^Nv_i = \frac {1}{N}(v_1 + v_2 + \cdots v_N)$$
The deviation of any given measurement ##v_i## from the average is of course ##(v_i - \langle v \rangle)##. Show mathematically that the sum of all the deviations is zero; i.e. show that
$$\sum_{i=1}^Nv_i(v_i - \langle v \rangle) = 0$$
Your formula above is incorrect, as it has an extra ##v_i##.
The sum of the deviations is
$$\sum_{i = 1}^N (v_i - \bar{v})$$
Here ##\bar{v}## is the mean of the measurements ##v_i##.
kubaanglin said:

Homework Equations


##?##

The Attempt at a Solution


I understand that this is simply describing an average, but I am not sure how to express this mathematically. It makes sense to me that the sum of the deviations would be zero.
Simply expand the summation.
 
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  • #3
$$((v_1 - \langle v \rangle) + (v_2 - \langle v \rangle) + \cdots (v_N - \langle v \rangle))$$
$$((v_1 - \frac {v_1 + v_2 + \cdots v_N}{N}) + (v_2 - \frac {v_1 + v_2 + \cdots v_N}{N}) + \cdots (v_N - \frac {v_1 + v_2 + \cdots v_N}{N}))$$
Is this sufficient to "show mathematically" that the sum of all the deviations is zero? I am just not sure what I am being asked to do.
 
  • #4
kubaanglin said:
$$((v_1 - \langle v \rangle) + (v_2 - \langle v \rangle) + \cdots (v_N - \langle v \rangle))$$
$$((v_1 - \frac {v_1 + v_2 + \cdots v_N}{N}) + (v_2 - \frac {v_1 + v_2 + \cdots v_N}{N}) + \cdots (v_N - \frac {v_1 + v_2 + \cdots v_N}{N}))$$
Is this sufficient to "show mathematically" that the sum of all the deviations is zero? I am just not sure what I am being asked to do.

You are being asked to show that the summation you wrote above evaluates to ##0## for any possible inputs ##v_1, v_2, \ldots, v_N##.
 

Related to Sum of Deviations: Proving $\sum_{i=1}^Nv_i(v_i - \langle v \rangle) = 0$

1. What is the significance of the sum of deviations in data analysis?

The sum of deviations is a measure of how much the individual data points deviate from the mean or average value of the dataset. It is useful in assessing the spread or variability of the data, and can also be used to identify outliers or extreme values.

2. How is the sum of deviations calculated?

The sum of deviations is calculated by subtracting the mean or average value of the dataset from each individual data point, and then adding up all of these deviations. Mathematically, it can be represented as $\sum_{i=1}^Nv_i(v_i - \langle v \rangle)$, where $v_i$ represents the individual data points and $\langle v \rangle$ represents the mean or average value.

3. What does it mean if the sum of deviations is equal to zero?

If the sum of deviations is equal to zero, it means that the data points are evenly distributed around the mean or average value. This suggests that there is no significant variability in the data and all data points are close to the mean.

4. How can the sum of deviations be used to compare datasets?

The sum of deviations can be used to compare datasets by looking at the magnitude of the sum. A larger sum of deviations indicates a larger spread or variability in the data, while a smaller sum suggests a more tightly clustered dataset. This can help in identifying which dataset has a larger range of values.

5. Can the sum of deviations be negative?

Yes, the sum of deviations can be negative. This occurs when the data points are predominantly below the mean or average value, and the deviations are negative. However, when the negative deviations are added to the positive deviations, the sum will still be equal to zero.

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