Part A: Using computer software, a correlation coefficient of r 0.51 was calculated. Based on the scatter plot, is that an accurate value for this data? Why or why not? (5 points)
Part B: Instead of comparing the number of pumpkins picked and the day in October, write a scenario that would be a causal relationship for pumpkins picked on the farm.

Step-by-step explanation:

For each of the following data sets, a. Create a scatterplot. b. Use LinReg (ax + b) to determine the best fit line and r.  Does the line seem to accurately describe the pattern in the data? c. For each of the different choices listed in the above chart, find the equation of the best fit curve and its associated r2 value. Of all of the curves, which seems to provide the best fit? Note: The r2 -value reported in each case is NOT the linear correlation coefficient reported when running LinReg(ax+B) Rather, the value will typically change depending on the curve. The reason why is that each time, the  -value is measuring how accurate the fit is between the data and that type of curve. A value r2 of  close to 1 still corresponds to a good fit with whichever curve you are fitting to the data.

40% off the regular price is the correct answer.

step-by-step explanation: