Statistics 简明教程

Statistics - Residual Sum of Squares

在统计学中，残差平方和 (RSS) 也称为平方残差和 (SSR) 或预测平方误差和 (SSE)，它是残差的平方和（数据实际经验值与预测值的偏差）。

In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared errors of prediction (SSE), is the sum of the squares of residuals (deviations of predicted from actual empirical values of data).

残差平方和 (RSS) 的定义和计算方式如下：

Residual Sum of Squares (RSS) is defined and given by the following function:

Formula

其中——

Where −

  1. ${X, Y}$ = set of values.

  2. ${\alpha, \beta}$ = constant of values.

  3. ${n}$ = set value of count

Example

Problem Statement:

Problem Statement:

考虑两个总体群体，其中 X = 1,2,3,4 和 Y = 4, 5, 6, 7，一致性值为 ${\alpha}$ = 1，${\beta}$ = 2。找出两个总体群体的残差平方和 (RSS) 值。

Consider two populace bunches, where X = 1,2,3,4 and Y = 4, 5, 6, 7, consistent worth ${\alpha}$ = 1, ${\beta}$ = 2. Locate the Residual Sum of Square (RSS) values of the two populace bunch.

Solution:

Solution:

给定，

Given,

排列：

Arrangement:

将给定的质量代入公式中，得到残差平方和公式。

Substitute the given qualities in the recipe, Remaining Sum of Squares Formula