ImmersionGroup3

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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 categories that you feel may influence fuel efficiency. Answer the following questions.==
 * === Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot? ===

Answer: We choose Avg MPG and Horsepower. We thought the avg mpg and horsepower would have the greatest impact on fuel efficiency. Answer: Horsepower is X axis and avg mpg is Y axis. Answer: Yes - the higher the horsepower the lower the fuel efficiency Answer: Negative slope - as horsepower increases the fuel efficiency decreases
 * === 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__= ==Create the linear regession equation in Excel. Include both the equation and the r 2 value on the graph. Answer the following questions.== Answer: y=0.067x + 35.44 Answer: 0.430 Answer: There is some correlation but not a strong correlation between these 2 categories. The closer it is to 1 the stronger the correlation.
 * === What is your regression equation? Explain what the equation means in relation to the categories. ===
 * === What is your r 2 value? Is this a strong correlation? Why or Why not? ===
 * === Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not? ===

=__**Analysis**__= ==Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.== Answer: Watch the R2 value - the polynomial was slightly better. Answer: Based on R2 - the polynomial was a better choice. >
 * === 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.**//=