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3. Naturalists have long known that you can use the rate at which crickets chirp to predict the temperature. Below are some data. Enter them in your trusty T Chirps in 1 minutes 882 1188 | 1104864 1200 ) 1032 1960 9oo Temperature (F69.7 93.3 84.3 76.3 88.6 82.6 716 79.6 a) What would be a good window for a scatter plot? b) Using that window, sketch the scatter plot. c). Find the equation of the regression bie d). Is the line a very good fit for the data? Explain. e). Suppose a cricket is chirping at 1000 chirps per minute. What do you predict the temperature to be?

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a. Excel

b.

Scatter plot 95 90 85 U 80 75 70 65 850 900 950 1000 1050 1100 1150 1200 1250 Chirps in minutes

c.

Chirps in minutes Temperature (OF SUMMARY OUTPUT 882 1188 1104 864 1200 1032 960 900 69.7 93.3 84.3 76.3 88.6 82.6 71.6 79.6 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.873678865 0.76331476 0.72386722 4.269509134 ANOVA MS Significance F Regression Residual Total 352.7277505 352.7278 19.35012 0.004573912 109.3722495 18.22871 7 462.1 Coefficients i Standard Error t Stat P-value Lower 95% Upper 95% Intercept X Variable 1 27.62835082 0.052272226 12.17015949 2.270172 0.063653 2.150956658 0.023195369 57.4076583 0.081349082 0.011883083 4.398877 0.0045740Let X = Chips in 1 minutes. Y =Temperature (0F) Regression equation of Y on X is Y - 27.62840.0523X d. R2 = 07633 ie. 76.33%

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