ImmersionGroup1

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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 discussed the bigger the engine (more horsepower) the less the fuel efficiency. Answer: X-axis is the MPG and Y-axis is the horsepower. That was the way it was in order on the spreadsheet Answer: Yes, it appears that the higher the MPG the lower the horsepower. Answer: It appears to be negative.
 * === 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: y = -6.3623x + 307.5. As you increase MPG, the horsepower decreases.
 * === What is your regression equation? Explain what the equation means in words. ===

Answer: R² = 0.4301 No, it is not very close to 1. Answer: No, but today's car manufacturers are doing a better job of increasing fuel efficiency.
 * === 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 ===
 * === 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: Power Answer: no, the power equation's R value was closer to 1 >
 * === How would you determine which equation had the best relationship? ===
 * === Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why? ===

=//**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.**//=