Despite its many advantages, analysis can be difficult to master. In the process, mistakes can result in incorrect outcomes that have grave consequences. It is important to avoid these mistakes and recognize them in order to maximize the benefits of data-driven decisions. Most of these errors result from mistakes or misinterpretations. These can be easily rectified by setting clearly defined objectives and promoting accuracy over speed.

Another common mistake is to think that an individual variable is in an average distribution when it does not. This can lead to models being overor under-fitted, and thereby compromising confidence levels and prediction intervals. It could also result in leakage between the training and test set.

When choosing when choosing an MA method, it’s important to choose one that suits the needs of your trading style. For instance, an SMA will be best for markets that are trending, while an EMA is more reactive (it removes the lag which exists in the SMA by putting priority on the most recent data). Furthermore, the parameter of the MA should be chosen with care based on whether you are seeking a short-term or long-term trend (the 200 EMA would suit more time).

Also, it’s essential to always double check your work before you submit it for review. This is particularly important when dealing with large amounts information, since errors are more likely to occur. It is helpful to have a manager or colleague take a look at your work may assist you in identifying any errors that you may have missed.

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