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Here are the meanings of these concepts: Linearity: There must be a linear relationship between the dependent variable and the independent variables. Scatterplots can show whether there is a linear or curvilinear relationship. Homoscedasticity: This assumption states that the variance of error terms is similar across the values of the independent variables. ...


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Look a the CV description I just posted at SE CV. In the first sentence there is a link to Kohavi, which explains bootstrap bias, or estimating error as a function of increasing sample size -- which is what you want. There's also a link to my blog "Help my Neural Network Does Not Work."


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