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 categories that you feel may influence fuel efficiency. Answer the following questions.== Answer: We chose horsepower and weight as the factors that would have the greatest impact on fuel economy. It seems apparent that the the more powerful an engine the more fuel it will require. Also a heavier vehicle will require more energy to move...thus will require more fuel Answer: We chose to use fuel economy as the y-axis data since it is the quantity that represents the dependent variable. Fuel economy represents the effect. Weight and horsepower were the independent variables so they were placed on the xaxis. Answer: Horsepower: Yes, as the power increases there a noticeable decrease in fuel economy. Weight: Yes, as the weight increases there a noticeable decrease in fuel economy. Answer: Both have a negative or inverse realtionship. This means as weight or horsepower increases the fuel economy will decrease.
 * === Identify the two categories you chose and why you thought there might be a relationship between the two 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__= ==Create the linear regession equation in Excel. Include both the equation and the r 2 value on the graph. Answer the following questions.== Answer: y= -0.0676x + 35.448 (horsepower) This means that for an increase of 1 horsepower there is a decrease of 0.0676 mpg. y=-0.0077x + 49.325 Answer: This means there is definitely an inverse relationship between horsepower and fuel economy but the relationship is not as strong as the relationship between weight and fuel economy. Answer: Weight has a stronger correlation than horsepower on fuel economy of the vehicle.
 * === What is your regression equation? Explain what the equation means in relation to the categories. ===
 * === What is your r 2 value? Is this a strong correlation? Why or Why not? ===
 * === Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not? ===

=__**Analysis**__= ==Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.== Answer: Answer: >
 * === 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.**//=