ImmersionGroup3

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: MPG and Horsepower......The greater the horsepower the lower the MPG.....linear
 * === Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot? ===

Answer: x-axis: horsepower and y-axis: MPG the MPG depends on the horsepower...x axis is the independent variable Answer: yes
 * === 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: positive correlation......greater the horsepower the lower the 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 = 0.0005x 2  - 0.2456x + 48.671 R² = 0.5811
 * === What is theregression equation? Explain what the equation means in words. ===

The greater the horsepower the lower the MPG is but there is a point at which it just stops because of limitations on horsepower.

Answer: see above.....not really.....
 * === 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...the car I drive has a hemi engine and has low gas mileage
 * === 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: By the r^2 value
 * === How would you determine which equation had the best relationship? ===

Answer: no...a polynomial equation of degree 6 is a better fit...the r^2 value is the highest.....
 * === Was the "Linear" option the optimal option? If not, what was the better equation and why? ===

> y = 7E-12x 6  - 8E-09x 5  + 3E-06x 4  - 0.0007x 3  + 0.0929x 2  - 6.1785x + 201.9 > R² = 0.6458

=//**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.**//=