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Flooring Installation Project

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Prior to my wife and I moving into our home in 2013, we wanted to price out flooring for our house. We went to several flooring installation stores in an attempt to find the best quality and service, for the cheapest price. I honestly could not remember the exact prices for all of the estimates we received, however, the information can still be used in creating an example of linear regression. We can use the equation of the estimated regression line in this example; which is exactly how I calculated the final cost for our flooring. While receiving our estimates, the common factor was old flooring removal. This cost was constant at around $90.00 per room. My wife and I were only inquiring about one room, so this constant of $90.00 (90) …show more content…

Also, notice that the positive slope of $1.59 per square foot indicates that the total cost of flooring installation for a room goes up as the total square footage of the room increases, reflecting a direct relationship between the two variables. We actually do not have to stop there either. We can conduct a multiple regression analysis as well. Say, for example, there was an outside entry way that we wanted to have as tile flooring in the room as well. We can use the same information above, with estimating that the cost of tile flooring to be $0.99 per square foot. Now, let’s re-write our equation as such: Y = a + b1X1 + b2X2 Where, a = Y-Intercept = $90 b1 = Cost of Hardwood Per Square Foot = $1.59 X1 = Square Footage of Hardwood Flooring Area b2 = Cost of Tile Per Square Foot = $0.99 X2 = Square Footage of Tile Flooring Area Y = Total Cost of Flooring Installation for Room Hence, Y = 90 + 1.59X1 + 0.99X2 Now, let us say that we would like to have 225 square feet of hardwood flooring and 25 square feet of tile flooring for the room. When we input our numbers, we find the following: Y = 90 + 1.59(225) + 0.99(25) = 90 + 357.75 + 24.75 = $472.50 Therefore, the total cost of having both hardwood and tile flooring in the room would cost (before taxes) $472.50. In the real world, linear regression is used to help

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