Question

What is the total sum of the squares?

Answer 1

Hint: Before getting into the tropic of the total sum of squares, we first learn about the sum of squares, the sum of squares is nothing but the measure of variation or deviation from the mean. There are three types of sum of squares; they are the sum of squares total, the sum of squares regression, and the sum of squares error.

Formula used:
$TSS = \sum\limits_{i = 1}^n {{{({y_{i - }}\overline y )}^2}} $
Where,
${y_i}$ - denotes each value in the set, $\overline y $ - denotes the mean value of a set, $\sum {} $ -denotes the sum, and$TSS$ - is the totals sum of squares

Complete step by step answer:
The total sum of squares is one of the types of the sum of the squares which is denoted as $TSS$ .
Generally, the total sum of squares denotes the variation of a dependent variable from the mean. In particular, the total sum of squares denotes the total variation in a sample.
The total sum of squares can also be called the sum of square total denoted by SST.
The total sum of squares is the squared difference between the observed dependent variable and the mean of the dependent variable.
The total sum of squares can be determined by the formula.
$TSS = \sum\limits_{i = 1}^n {{{({y_{i - }}\overline y )}^2}} $
Where,
${y_i}$ - indicates each value in the set
$\overline y $ - is the mean value of a set
$\sum {} $ -is the sum and
$TSS$ - is the totals sum of squares

Note:
In Statistics, the sum of squares is used to identify the dispersion of data. Since the sum of squares is calculated by finding the sum of the squared difference, it got its name as the sum of squares. Also, it is used how the data can fit the sample in the regression analysis. It is denoted by SST or TSS.