ImmersionGroup2

Back to Activity1 ===Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.===

=__Scatterplot Questions__= ==1. Create a scatterplot using average MPG and another category that you feel may influence fuel efficiency. Answer the following questions.== Answer: We chose "weight" because we feel that weight would influence the efficiency of the miles per gallon that a car would get. Answer: Our X-axis is Weight, our y-axis is average MPG. We created our scatterplot in that order we feel the MPG will depend on the weight of the car. Answer: After seeing the relationship, we see that as the weight increases the MPG decreases, so there is a negative relationship between weight and MPG. Answer: There is a negative slope. So, in theory, the larger the car, the worse the gas mileage (MPG) and vice versa.
 * === Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot? ===
 * === Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order? ===
 * === Do you believe there is a relationship between the two categories? Why or why not? ===
 * === If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories? ===

=__Regression Questions__=

(What is Regression?)
==Create the linear regession equation in Excel, which Excel calls the trend line. Click the boxes to create both the equation and the r 2 value on the graph. Answer the following questions.== Answer: Regression equation: y = -0.0077x + 49.325 For approximately every 125 pounds of a car, the MPG efficiency decreases by one mile per gallon. Answer: r 2 value: R² = 0.7042 This is not an extremely strong correlation because it is not 1, but it is close.
 * === What is your regression equation? Explain what the equation means in words. ===
 * === What is your r 2 value? Is this a strong correlation? Why or Why not? If you are not sure, try searching the internet for supporting documents. Provide URL's for where you find your information ===

Answer: No. we believe based on our evidence and assumptions that weight influences the MPG in a negative relationship.
 * === Based on all the information you have, has your belief about the relationship of the two categories changes? Why or why not? ===

=__**Analysis**__= ==Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.== Answer: The closer the r squared value is to one.
 * === How would you determine which equation had the best relationship? ===

Answer: The 6th degree polynomial ( y = 9E-18x 6  - 2E-13x 5  + 1E-09x 4  - 4E-06x 3  + 0.009x 2  - 9.8885x + 4532.7 R² = 0.7772 ) than the power ( y = 21042x -0.84 R² = 0.7751) option for the trend line because the r squared was closer to one.
 * === Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why? ===

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=//**Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.**//=