# Can we trust this model?

You've built a model... but can you trust it? There are many statistical tests you can run to validate your model, but they've all got confusing-sounding names. What's most important when checking if your model is accurate?

## Based on Experience

Vetted industry professionals, that have experience working with these companies and others, consulted in an advisory capacity to ensure the content of this course was realistic.

This is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

How do you answer the question *" Can we trust this model?"* – learn how

**Model Accuracy**can help you find the answer!

##### Model Accuracy

Can you trust the accuracy of your model? This is a surprisingly existential question, covering a lot of fundamental statistics, which you absolutely need at least a basic working knowledge of. Read More

6 Chapters

55 Sections

19 Exercises

Completed in:

1 hour, 48 minutes

## Course curriculum

Chapter | Sections | Exercises | |
---|---|---|---|

1. When the client questions the model | A key client has questioned the accuracy of your model – how do you prove that your model is valid and can be trusted when making decisions? | 6 | |

2. The four assumptions of regression | Linear regression is only reliable as a method if certain conditions are met. What assumptions are we making with this model? | 13 | |

3. Does this model make sense? | Marketing mix modeling required common sense. Review your model's metrics to see if they match your expectations. | 9 | |

4. Interpreting the standard statistical tests | F-Statistic, Skewness, Durbin Watson... what do these terms mean? Let's walk through each statistical test in the standard model output. | 7 | |

5. Running your own statistical tests | There are many more tests available outside of the standard output. Let's implement three of the more popular ones and learn what they test for. | 10 | |

6. Predicting data we haven't seen before | Our model might be valid, but is it useful? The only way we can know for sure, is if we can use it to accurately predict future values. | 10 |

## Frequently Asked Questions

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