I have data with low variances. I fit a linear regression model, and I expect to get high R2 because it is a good fit. But I get very low R squared indicating that I have big variances. So I wonder what is wrong here.
This is my x and y data:
x= [ 0 0 0 0.0136 0.0136 0.0136 0.0304 0.0304 0.0304 0.0938 0.0938 0.0938 0.1505 0.1505 0.1505];
y=[90.0000 91.0000 89.0000 89.3268 87.8679 88.9533 93.7081 87.8315 91.1389 88.8301 90.6050 88.6748 90.5922 89.4413 90.8470];
f1 = fitlm( x,y)
Rsquared=f1.Rsquared.Ordinary
figure; plot(x,y,'or'); ylim([80 100])
___________________________________________
f1 =
Linear regression model:
y ~ 1 + x1
Estimated Coefficients:
Estimate SE tStat pValue
________ _______ _______ __________
(Intercept) 89.722 0.58035 154.6 1.3066e-22
x1 2.304 7.192 0.32036 0.75378
Number of observations: 15, Error degrees of freedom: 13
Root Mean Squared Error: 1.57
R-squared: 0.00783, Adjusted R-Squared: -0.0685
F-statistic vs. constant model: 0.103, p-value = 0.754
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