The scatter plot shows the number of strawberries that have been picked on the farm during the month of February: A scatter plot with points showing an upward trend with points that are moderately spread out from a line of best fit. The y axis is labeled Number of Strawberries and the x axis is labeled Days in February Part A: Using computer software, a correlation coefficient of r = 0.01 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 strawberries picked and the day in February, write a scenario that would be a causal relationship for strawberries picked on the farm. (5 points)

Part A: No. Based on the scatter plot, r = 0.01 is not an accurate value for this data, because 0.01 means pratically no corrlation, while the points on the graph are moderately correlatedPart B: you must describe a scenary where a variable is the cause of the number of strawberries picked. See below, the example of rainfall.

Explanation:

1. Part A:

Since the points in the scatter plot show a moderated upward trend, you must expect a correlation coefficient close to ± 0.5. A correlation coefficient of 0.01 is too close to 0, which would mean that the points are almost not correlated at all.

The correlation coefficients, r, measure the strength of the correlation of two variables and they can have values from - 1 to  1.

r = - 1 is  a perfect negative correlation: the points will adjust perfectly to a line with negative slope.r =  + 1 is a perfect positive correlation: the points will ajdust perfectrly to a line with positive slope.r = 0 means that the variables are not correlated at all.

Hence, the closer to zero, the worse the correlation is, and that is what r = 0.01 means, but the graph shows other thing.

Therefore, the calculated value of r = 0.01 is not accurate for t his scatter plot.

2. Part B.

A causal relationship means that the expllanatory variable (the independent variable) is the cause of the dependent variable.

Hence, you must find a reasonable variable that can be the cause of the number of strawberries picked.

I could imagine the amount of rainfall the week before of harvest.

Thus, the scenario could be:

Amout of rainfall in mm (x)         Number of strawberries picked (y)

        100                                                     20

          50                                                     40

          25                                                     80

          12.5                                                  160

          6.25                                                 320

The amount of rainfall is the cause of the number of strawberries picked, and you could find a model (function) that relates both variables.