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PinkR1ver
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@ -31,9 +31,9 @@ cov(X, Y) & = \frac{\sum(x-\overline{x})(y-\overline{y})}{n-1} \\
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\end{split}
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\end{equation}
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$$
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Covariance is hard to **interpret** because it is sensitive to the **scale**
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@ -43,7 +43,7 @@ To solve the scale effect, here's the correlation:
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## Correlation
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We can quantify the strength of the relationship with correlation (**Pearson’s correlation**)
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@ -61,13 +61,13 @@ $corr(X, Y)$ is between -1 to 1
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> NOTE: When we’re talking about correlation, we’re only talking about using **straight line**
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For correlation, we usually use **p-value** to **quantify the confidence** of the straight line relationship. **The more samll p-value, the more confident we say they are straight line relationship**; Like the figure:
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About P-value, you have better know what's [significance test](math/Statistics/significance_test/whats_the_significance_test.md)
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@ -221,7 +221,7 @@ $$
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# Application
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* [Period Detection by Autocorrelation](signal_processing/advanced_statistic/autocorrelation/period_detection.md)
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* [Period Detection by Autocorrelation](signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md)
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# Reference
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* https://pinkr1ver.notion.site/Autocorrelation-Analysis-Power-Spectral-Density-330755770347472989062c6b31f18a21?pvs=4
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@ -43,8 +43,8 @@ date: 2024-03-18
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## Autocorrelation
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* [Autocorrelation in Signal Processing](signal_processing/advanced_statistic/autocorrelation/autocorrelation.md)
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* [Period Detection by Autocorrelation](signal_processing/advanced_statistic/autocorrelation/period_detection.md)
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* [Autocorrelation in Signal Processing](signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md)
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* [Period Detection by Autocorrelation](signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md)
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## Empirical Mode Decomposition
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