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: Weight Answer: Y- Avg MPG X- Weight Answer: Yes. Heavier cars should use more fuel.
 * === 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? ===

Answer:Negative. This means heavier cars get lower MPG.
 * === 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= -.0077x+ 49.325 Approximate negative linear relationship between weight and MPG.
 * === What is your regression equation? Explain what the equation means in words ===

Answer: R squared = .7 Strong relationship. Answer: No, confirms what we hypothesized.
 * === 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. Raises r squared to .775 Answer: Power, it fits better and has better r squared. >
 * === 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.**//=