Essay on Regression Analysis

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Our regression analysis was done on OMNITRANS fuel consumption. This has been an ongoing issue for OMNITRANS where there seems to be an inconsistency with there CNG fuel consumption. There continues to be variance in what is consumed each day compared to the amount of miles driven. This issue is very important to OMNITRANS because it makes it very difficult to plan for future use with the CNG industry. OMNITRANS wants to have a consistency with CNG use so they can plan for budgeting purposes and new contracts that are connected with the CNG usage. We are going to establish what we believe will be a good analysis for OMNITRANS to look at and establish what is needed in order to improve there fuel consumption within the
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Instead of taking an average of all fuel consumed for a month by using the 5 different days we are able to look at a better sample of what is actually happening with fuel consumption. Fuel Consumed Miles Driven
When X Y
Day1 2904.2 8712.6
Day6 2807.1 8421.3
Day11 4128.3 12384.9
Day16 3489.3 10467.9
Day21 2424.3 7272.9 Coefficient of Determine = 1 Fuel Consumed Miles Driven
2500 7500
3500 10500 Next was the issue of establishing the Y factor. With this we ran into an issue of how many miles for each day. Even though OMNITRANS has set schedules for there buses there seemed to be an issue of establishing a breakdown of each bus bases on the different routes. Some buses drive only10 miles a day while another bus may drive up to 400 in single work day. What we have done is taken the average miles per gallon and multiplied by the amount of fuel consumed in gallons. We found that this is the best way of showing the correlation between the X and Y.
The opportunity for this is that if we can establish a norm of what should be happening with fuel consumption we would have a better way of establishing what is going on with the OMNITRANS buses. Below you will find our regression analysis for the data that was explained between the X and Y. Interpret Results
By the data above it will show that there is a direct relationship between fuel consumed and miles driven. As fuel

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