Collect a sample of at least n = 20 pieces of bivariate data - for example, the ordered pair (6,9600) would represent a car that's six years old and sells for $9600.00. Identify the explanatory variable and the response variable. Do you think these two variables are positively or negatively associated? How strong of a relationship do you think this is? Show me the ads that you used and make a chart of your data. Calculate descriptive statistics for each of the two sets of data - the five number summary, plus mean and standard deviation - and comment on anything interesting that you notice. Create an appropriate graphical display for the price data. Be sure to include scales, labels, etc. Describe how you collected your data and how you tried to make the collection of data fair and unbiased Do not go overboard in complexity, but try to gather your data in a way that yields a good representation. Make a scatterplot of your data. Be sure to properly label the axes. Do the data appear to be linear? Give the correlation coefficient r for these data. Comment on this result: Are the strength and sign of r what you expected before the data were obtained? Why or why not? Give your answer in terms of how the plot in Step 4 looked. Does r seem to be a good indicator of the strength of the relationship between your two variables? Why or why not? Give the value of r and explain what is means in terms of the variables in this problem. (This is frequently missed by students; be sure to put some thought into this and get it right!) Calculate the equation of the least squares regression line. Draw this line on your scatterplot. What does the value of the slope in your equation represent? Explain. Do you think this is a fixed value for automobile selling prices or not? Explain. What does the value of the y intercept in your equation represent? Explain. Is this realistic? Use your regression equation to determine the asking price for Sheila's 2006 Civic (or the car that you targeted). How does this predicted value y(hat) compare to y, the average selling price in your data? Which of these two values is likely to be more accurate? Why? Is there a better way to model the data you collected? Make any final comments about the effectiveness of this procedure, any flaws you see, and any other matter that you consider germane