Degrees Of Freedom


In Data Science, Degrees of Freedom refers to the number of independent values or observations in a sample that can vary without violating any constraints or assumptions. In other words, it is the number of values in the final calculation of a statistic that are free to vary. Degrees of Freedom is an important concept in statistical hypothesis testing, as it determines the critical values of the test statistic and the p-value. The higher the degrees of freedom, the more reliable the statistical inference. Degrees of Freedom is also used in machine learning algorithms, such as linear regression and ANOVA, to estimate the parameters of the model and to assess the goodness of fit.


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