ImmersionGroup+7

= = 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 to compare average MPG and weight because we thought that the heavier the car the less the MPG would be. Answer: X-axis is weight; y-axis is Average MPG. Weight is the independent variable and average MPG would be the variable that would be affected by the weight, thus the dependent variable. Answer: Yes because as the weight increases, the average MPG is generally decreasing. Answer: Negative slope. This means that as one category increases the other category decreases.
 * === 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 = -0.007x + 49.32; The equation shows that as weight increases the average MPG decreases, more specifically the average MPG decreases .007 for every 1 pound increase in weight. The y-intercept, 49.32, is the average MPG if the car weighed 0 pounds. Answer: 0.704, not a strong correlation because the closer the number is to positive or negative one is a better correlation, thus .7 is not close to 1. Answer: No because we still believe that as the weight increases the MPG decreases. There is not possibly as strong of a relationship as we had thought, but there is a relationship.
 * === 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: Look at the graph of the data points and see which trendline was the most "in the middle" of the data points. Answer: The linear option was NOT the optimal option. The better equation would be the power equation because it better approximates more of the data points. (Power Equation: y = 21042x^-.84) >
 * === 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. **//=