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iCloud

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What's the difference between the 2?

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In summary: Your question is too broad to have a meaningful answer. There are many different methods for regression that each have their own limits and assumptions.

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iCloud

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What's the difference between the 2?

Thanks

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BvU

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Maybe you want to be a bit more specific: forecasting is difficult, especially when it concerns the future. From a brief search (I can't really pinpoint time series decomposition -- it seems to be pretty general) I suspect regression analysis when applied to temporal data is one form of time series deco.

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BvU said:

Maybe you want to be a bit more specific: forecasting is difficult, especially when it concerns the future. From a brief search (I can't really pinpoint time series decomposition -- it seems to be pretty general) I suspect regression analysis when applied to temporal data is one form of time series deco.

Well I just started with regression, and there was this general question without specifics. I am just at the basics of the regression and multiple-regression now.

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MarneMath

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iCloud

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Thank you both, that helps.

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chiro

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Time series looks at a variety of correlation values in a very structured way while regression typically [and I say typically] doesn't include that information.

When you do statistics you do inference on parameters and in time series, the parameters you use typically involve some sort of correlation vector where-as regression looks at estimating coefficients for some model in terms of a population expectation [so a mean as a function of the values of different variables].

Regression analysis is a statistical method used to analyze and model the relationship between a dependent variable and one or more independent variables. It is commonly used to make predictions and identify patterns in data.

Time series decomposition is a technique used to break down a time series data into its different components, such as trend, seasonality, and irregularity. This helps to better understand the underlying patterns and trends in the data and make more accurate forecasts.

Simple regression analysis involves only one independent variable, while multiple regression analysis involves two or more independent variables. Multiple regression allows for a more complex relationship between the dependent and independent variables to be modeled.

The significance of regression coefficients is determined by looking at the p-value. A p-value less than 0.05 indicates that the regression coefficient is statistically significant, meaning that it is unlikely to have occurred by chance.

Autocorrelation is a measure of the linear relationship between a time series and a lagged version of itself. It is commonly used to check for patterns and dependencies in the data, such as seasonality or trends.

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