Prediction based on best fit linear regression model

Hi,

I have the below data(train_data, test_data), and I want to do following:

1. In train set some of point or outliears, and I want evaluate best fit model based on mean squared error
2. Prediction based on best fit model.

Train_data:
x   y
1   1
2   3
6   6
8   2
11  15
15  9
21  11
25  14
Test set:

x 
5
12
21
23

my desired output(i.e, y:)
4
7
12
13

Answer 1

[the cyclist]

It looks like you have the Statistics and Machine Learning Toolbox. I would use fitlm to fit the model on the training data.

Then you can use the predict method to make a prediction on the test set.

[Mekala balaji]

Sir, I can use fitlm. But sir, there are some outliers due which the rsquare is very poor, I want to evaluate by taking how many input data (drop others) such that we get higher rsquare.

[the cyclist]

You can do "robust" fitting (with fitlm, or other MATLAB functions). This is a common way of handling outliers. The documentation page I linked has the details on how to do this.

This Wikipedia page discusses robust methods.