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Unit test
Problem
The graph displays a residual plot that was constructed after running a least-squares regression on a set of bivariate numerical data (x,y)(x,y)left parenthesis, x, comma, y, right parenthesis.
A residual plot has horizontal axis, x, which ranges from 0 to 10, in increments of 1. The vertical axis is labeled, residual, and ranges from negative 1 to 1, in increments of 0.5. 10 points are plotted as follows: (1, negative 0.4), (2, 1.1), (3, negative 1.1), (4, 0), (5, 0.6), (6, negative 0.3), (7, 0.1), (8, 0.2), (9, 0.1), and (10, negative 0.3). All values estimated.


What can you conclude from this graph?

Sagot :

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Based on the distribution of the points on the residual plot, where the points are randomly dispersed and shows no obvious trend or pattern, then we can conclude that a Least Square Regression model is appropriate for the data .

  • The graph of a residual plot is used to Analyse the the appropriateness of a linear regression to model a given data set.

  • The residual values are plotted against the independent variable in other to make a residual plot of the data.

  • The arrangement of the points on the residual graph is key to making a decision on the model.

Therefore, since the residual plot produced shows a random distribution of the residual values, then the least square regression model is appropriate for the data.

Learn more :https://brainly.com/question/18405415

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khaosthunder08

Answer:

Based on the distribution of the points on the residual plot, where the points are randomly dispersed and shows no obvious trend or pattern, then we can conclude that a Least Square Regression model is appropriate for the data .

The graph of a residual plot is used to Analyse the the appropriateness of a linear regression to model a given data set.

The residual values are plotted against the independent variable in other to make a residual plot of the data.

The arrangement of the points on the residual graph is key to making a decision on the model.

Therefore, since the residual plot produced shows a random distribution of the residual values, then the least square regression model is appropriate for the data.

Step-by-step explanation:

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