Graphic Designer Desk
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In the Woods

Northwind Hypothesis Tests

Microsoft's Northwind Dataset

For this project, I worked with the Northwind database--a free, open-source dataset created by Microsoft containing data from a fictional company. The goal of this project was to gather information from a real-world database and use my knowledge of statistical analysis and hypothesis testing to generate analytical insights that can be of value to the company. I tested four separate hypotheses and analyzed the results.


Data Exploration and Scrubbing

Explore Dataset

Our first step is to explore and understand the dataset we are working with. We need to make sure we know what all of the columns represent and how the computer is interpreting them. This step allows us to force the computer to understand the data the way we want it to.

Deal with Missing Data

We need to deal with rows that are missing data. The dataset is incomplete, which causes problems when running statistical analysis, so we have to determine how best to deal with missing data on a case by case basis.

Check Multicollinearity

We make sure that all of the predictors we are using describe price and not each other. If two of our predictors are highly correlated, it’s hard to determine which one is affecting the price.

Check Model Assumptions

We want to be sure that our data fulfills all the assumptions that are necessary to create a model. If our data does not satisfy all the assumptions, we need to transform our data appropriately so that we can build a statistically significant model.



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In the Woods

Test Our Model

We want to ensure that our model is actually predicting results, so we run several tests to protect us from creating a model that works on the data we currently have.

Playing on Tablet

Remove Inconsequential Predictors

We figure out which predictors don’t actually influence our model and remove them from the equation to keep everything as simple as possible.

Augmented Reality Glasses

Final Results

Our final Model explains 98.9% of the variations in our dataset.

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