a. How do the slopes and intercepts of the two best fit lines compare? Are they

Question

a. How do the slopes and intercepts of the two best fit lines compare? Are they similar? Do you think the two data sets come from the same species of crickets?

b. Are the straight lines a good fit for the data (from looking at the graph), or does it look like a different function might give a better fit? If you think a straight line is not a good fit, you can try some of the other types of trendlines and compute the squared error. If you do this, then be aware that you must adjust the formulas that compute the model values.

c. Which data set has the larger squared error? Can you think of reasons why it may have a larger error? (Hint: What impact might the number of data values have on the squared error?)

Temperature versus Chirp Rate y 0.2512x + 35.78 60 B&B; data 40 LeMone data Linear (B&B; data) Linear (LeMone data) y = 0.2229x + 40.025 20 2 60 110 160 Chirps per minute

Explanation / Answer

a)
Based on the information given in graph , the slopes are 0.2512 and 0.2229 . The values are quite similar however the intercepts differ

It is likely that the 2 datasets come form the same species as the line fits are alomost similar

b)
yes , we see that the data points follow a linear pattern . hence straight lines represent this data quite well.

c)
Just by looking at the dtaa points it is difficult to say which would have higher squared error. BUt the error term is directly related to the distance of the data points from the fitted line . so if the data points are scattered away from the fitted line , then the error term for the model would be high as compared to a line whose data points are closer to the fitted line

Here in this case , apparently LeMone data would have higher squared errors

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