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+ Linear regression is a simple statistical process and so is easy to carry out.

+ Some non-linear relationships can be converted to linear relationships using simple transformations.

- The error structure may not be suitable for regression (independent, identically distributed).

- The regression model used may not be appropriate or an important variable may have been omitted.

- The residual error may be too large.

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Q: What are some of the advantages and disadvantages of making forecasts using regression methods?

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There are both advantages and disadvantages of data collection methods in statistics. The main advantages are the metrics and correlation one can draw from statistics. The disadvantages stem from sampling errors.

Since time-series forecasts are relatively easy and inexpensive to construct, they are used quite extensively.

There are clear advantages to the deductive method. These include that the person is more likely to learn and remember how they got to the answer in the future and also they remember the stages they have gone through. A major disadvantage is that it does take longer than other methods.

They make forecasts based on both present and past values of the variables. Stated in non-technical terms, they assume that somehow history repeats itself, i.e. that some patterns in the time series behavior of data are recurrent. As a consequence, the past is useful to predict the future.

urmmm .. shows cause and effecthas high control which lead to high reliability & validity

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