ImmersionGroup+8

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:The two categories we chose are Avg. MPG to Horsepower.We thought there was a relationship based on the more power the less mpg the vehicle used.(Common sense). Answer:x axis is avg mpg. and y is # of horses. The avg. mpg is dependent upon the independent horsepower. Answer:Absolutely, there is a correlation. as the avg. mpg goes down the number of horsepower goes up. Answer:negative slope, (see above)
 * === 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=-.0676x+35.448 Answer:.4301 There is a correlation, but not as accurate or as much of an indicator as could be. Answer:Yes, you can draw conclusions. As the horsepower goes up the avg. mpg goes down. There is a correlation, but not as strong as it could be.
 * === 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:We based it on the R value. Answer:Not based on our reasoning for choosing a best type of line. The polynomial was the best. >
 * === 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.**//=