ImmersionGroup4

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 horsepower because we thought that gas milage is always affected by how much power the car uses.
 * === Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot? ===

Answer: horsepower is our independent variable and MPG is our dependent variable.Because there is a direct relationship between horsepower and MPG.
 * === Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order? ===

Answer: yes, the higher the horsepower the less the miliage. Answer: It has a negative slope. As the horsepower increases the MPG decreases.
 * === 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=-0.0676x + 35.448 For every horsepower increase the MPG drops by 0.0676 MPG.
 * === What is your regression equation? Explain what the equation means in words. ===

Answer: r-squared is 0.4301. This is a very weak correlation because our r-squared is below .5
 * === 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. ===

> === l the information you have, has your belief about the relationship of the two categories changes? Why or why not? === Answer: Yes but we now think it is exponetial in relation and not linear.
 * === Based on al ===

=__**Analysis**__= ==Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.== Answer: The equation with the r-squared value closest to 1.
 * === How would you determine which equation had the best relationship? ===

Answer: No, r-squared value for linear equation is very small. The quartic equation is the better option with almost 0.65 r-squared value.
 * === 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**//= === === =//**.**//=