api-challenge's Introduction

python-api-challenge

Analysis

The strongest observable trend is the relationship between latitude and temperature. Max temperatures were greatest the closer we got to the equator, and lower the further away the location is. This applies to both hemispheres with the arctic regions in the North and Antarctica in the South as extreme examples to support the cold temperatures toward the poles (away from the equator).
The metric for cloudiness in our dataset seems to inadequatley measure or act as a defining piece of weather data. If not for the linear regression in the southern hemisphere, there isn't a clear sort of trend that can be observed from the data: it's too neat across the board. The data spread hits many levels of cloudiness at most latitudes. Cloudiness may not be a useful technique for discerning a region's weather if it does/does not impact another component, like temperatures or humidity. Clouds can lower temperatures with shade, but they can also bring rain and humidity. Humidity in term can raise temperatures.
A larger sample, perhaps targeted at one hemisphere or the other, could have shown a stronger relationship between latitude and wind speed. Both graphs seem to indicate weak correlation, but they appear more indicative of a relationship than the combined data. The potential trend is lower wind speeds nearer the equator, but there aren't as many data points on the farther latitudes to support the trend.

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