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Using Multiple Linear Regression to estimate the highest value mountain bicycles

Using Multiple Linear Regression to estimate the highest value mountain bicycles


Author : Aarush Shintre

Millennium National School, 18 Hillside, Karvenagar, Pune, India


Abstract

Mountain bikes can have varied pricing: from beginner mountain bikes costing around 300$ to the high-end boutique mountain bikes used by professional riders nearing the 15 '000$ mark. One of the most difficult aspects of buying a new bike is attempting to grasp the primary cost drivers for bikes, or understanding why mountain bike prices vary so much even within the same model range . Not only this, but lists of highest value mountain bikes published by mountain bike media outlets to help consumers decide which bike is a good value sometimes seem incorrect, and the amount of bikes tested by media outlets tend to be limited, which means that the list might not include the particular bike a consumer is looking for. This study focuses on predicting mountain bike selling prices using multiple linear regression and comparing these to their actual selling price to determine the bike's "value," as well as examining the best value bikes on the market. The paper statistically identifies which mountain bicycles on the market give the highest value to the consumer, depending on the bike's parts, frame material and selling price, and compares the results with the highest value mountain bike lists published by popular mountain bike media outlets.



Using Multiple Linear Regression to estimate the highest value mountain bicycles
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