ImmersionGroup+5

== 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: The heavier a car is, the more energy (gas) it requires to move. Answer: Weight is the x-axis, mpg is the y-axis. Just as y depends on x, mileage will depend on weight. Answer: Yes because of basic laws of physics, more mass requires more energy to achieve a specific velocity. Answer: Negative slope because as weight increases mileage decreases; heavier cars get less mpg.
 * === 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 = -91.695x + 5432; As weight (x) increases by 1 lb, mileage decreases by about 92, but begins at 5432. Answer: 0.704; strong correlation would be closer to 1.0 Answer: no because the general idea is that the mileage decreases with increasing weight and that has proven true
 * === 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 ===
 * === 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: Whichever line passed through the most points. Answer: No it didn't pass through as many points as the logarithmic function. The log function passed through the most points. >
 * === 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.**//=