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  2. Statistical model validation - Wikipedia

    en.wikipedia.org/wiki/Statistical_model_validation

    Residual plots plot the difference between the actual data and the model's predictions: correlations in the residual plots may indicate a flaw in the model. Cross validation is a method of model validation that iteratively refits the model, each time leaving out just a small sample and comparing whether the samples left out are predicted by the ...

  3. Partial residual plot - Wikipedia

    en.wikipedia.org/wiki/Partial_residual_plot

    Although they can often be useful, they can also fail to indicate the proper relationship. In particular, if X i is highly correlated with any of the other independent variables, the variance indicated by the partial residual plot can be much less than the actual variance. These issues are discussed in more detail in the references given below.

  4. Misleading graph - Wikipedia

    en.wikipedia.org/wiki/Misleading_graph

    For example, log scales may give a height of 1 for a value of 10 in the data and a height of 6 for a value of 1,000,000 (10 6) in the data. Log scales and variants are commonly used, for instance, for the volcanic explosivity index, the Richter scale for earthquakes, the magnitude of stars, and the pH of acidic and alkaline solutions.

  5. Regression validation - Wikipedia

    en.wikipedia.org/wiki/Regression_validation

    For example, the lack-of-fit test for assessing the correctness of the functional part of the model can aid in interpreting a borderline residual plot. One common situation when numerical validation methods take precedence over graphical methods is when the number of parameters being estimated is relatively close to the size of the data set.

  6. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals .

  7. Homoscedasticity and heteroscedasticity - Wikipedia

    en.wikipedia.org/wiki/Homoscedasticity_and...

    Plot with random data showing heteroscedasticity: The variance of the y-values of the dots increases with increasing values of x. In statistics , a sequence of random variables is homoscedastic ( / ˌ h oʊ m oʊ s k ə ˈ d æ s t ɪ k / ) if all its random variables have the same finite variance ; this is also known as homogeneity of variance .

  8. Aaron Paul Claims He Gets No ‘Breaking Bad’ Streaming ...

    www.aol.com/entertainment/much-actors-earn...

    Many working actors earn residual paychecks anytime that a TV show they appeared on air in reruns. Such sums even help stars such as Mandy Moore and Glen Powell pay their living expenses between jobs.

  9. Plot (graphics) - Wikipedia

    en.wikipedia.org/wiki/Plot_(graphics)

    Partial regression plots are also referred to as added variable plots, adjusted variable plots, and individual coefficient plots. Partial residual plot : In applied statistics, a partial residual plot is a graphical technique that attempts to show the relationship between a given independent variable and the response variable given that other ...