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The final step to be performed in the mathematical expression, (Σx)2 is square the sum of the scores
What Is the Sum of Squares?
Sum of squares is a statistical method for calculating the dispersion of data points in regression analysis. By deviating the least from the data, the sum of squares can be used to identify the function that fits the data the best. The purpose of a regression analysis is to assess how well a data series can be fitted to a function that could provide insight into the process by which the data series was created. In the world of finance, the sum of squares can be used to calculate the variation in asset values.
A statistical measure of departure from the mean is the sum of squares. Variation is another name for it. The squared differences of each data point are added together to calculate it. The sum of squares is calculated by squaring the separation between each data point and the line of best fit, then adding the results. The line of greatest fit will reduce this value to the minimum.
We first find the sum, and then square the sum.
ΣX = 1 + 0 + 2 + 4 = 7
So, ΣX + 1 = 8
Mean = ( 4+6+14 )/3 = 8
Mean = ΣX*f / Σf = (5*2 + 4*1 + 3*3 + 2*2 + 1*2) / (2+1+3+2+2) = 29/10
Subtract 3 points from the score X=3
Changing the lowest score to a lower one will increase the range.
To learn more about the, Sum of Squares visit:
https://brainly.com/question/19426263
#SPJ4
The final step to be performed in the mathematical expression, (Σx)2 is square the sum of the scores
What Is the Sum of Squares?
Sum of squares is a statistical method for calculating the dispersion of data points in regression analysis. By deviating the least from the data, the sum of squares can be used to identify the function that fits the data the best. The purpose of a regression analysis is to assess how well a data series can be fitted to a function that could provide insight into the process by which the data series was created. In the world of finance, the sum of squares can be used to calculate the variation in asset values.
A statistical measure of departure from the mean is the sum of squares. Variation is another name for it. The squared differences of each data point are added together to calculate it. The sum of squares is calculated by squaring the separation between each data point and the line of best fit, then adding the results. The line of greatest fit will reduce this value to the minimum.
We first find the sum, and then square the sum.
ΣX = 1 + 0 + 2 + 4 = 7
So, ΣX + 1 = 8
Mean = ( 4+6+14 )/3 = 8
Mean = ΣX*f / Σf = (5*2 + 4*1 + 3*3 + 2*2 + 1*2) / (2+1+3+2+2) = 29/10
Subtract 3 points from the score X=3
Changing the lowest score to a lower one will increase the range.
To learn more about the, Sum of Squares visit:
brainly.com/question/19426263
#SPJ4
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