ImmersionGroup+6

= = 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 horsepower to see how it affected the MPG. We weren't sure if there was a relationship, so we decided to use that one!
 * === Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot? ===

Answer: x-axis is "Horsepower" and y-axis is "MPG". Looking for a relationship between MPG and Horsepower. We thought that the MPG would depend on the Horsepower, so it would have to be on the y-axis.
 * === Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order? ===

Answer: Yes, there appears to be some relationship because as Horsepower increases, the MPG decreases.
 * === Do you believe there is a relationship between the two categories? Why or why not? ===

Answer: Negative slope. This means that as Horsepower increases, MPG decreases.
 * === 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 = -0.0676x + 35.448. This means that there is a slight negative correlation between horsepower and MPG. Cars with no horsepower receive an average of 35 MPG.
 * === What is your regression equation? Explain what the equation means in words. ===

Answer: 0.4301. There is some correlation, but it is not necessarily a strong one. The value is about a 50% correlation.
 * === 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 thought there should be some correlation between the two categories and we found that there was!
 * === 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 higher the r 2 value, the better the relationship.
 * === How would you determine which equation had the best relationship? ===

Answer: No, the polynomial equation has the strongest correlation because the r 2 value was the highest.
 * === 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. M**////**ake sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.**//=