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)

Explanation:

A correlation coefficient of r = 0.01 is not an accurate value for the data on the scatter plot, because it is very close to 0, which means that the data are not linearly correlated, while the figure, on the contrary, shows that the data moderately follow a linear pattern.

A pattern as the one shown in the figure is closer to 0.5 (may be between 0.4 and 0.6).

When the data are perfectly linearly correlated r is equal to +1 (positive correlation) or - 1 (negative correlation). The minimum absolute value for r is 0 and it means that the data are not correlated at all. Hence, r = 0.01, being very close to 0, means that the data are not linearly correlated, which contradicts the graph.

A causal relationship is when the independent variable not only is correlated with the dependent variable but it causes it.

Is the day the cause of the number of strawberrys picked? Hardly. It must be another third variable, which is somewhat associated with the day in February but not a 100%, and that is directly responsible for the number of strawberries picked.

What variable might it be? It could be the state of the road to the harvesting place.

That would be the case if the road is repaired and every day the road is a bit better, letting the workers arrive earlier to the place and spend more hours harveting.

900 degrees

step-by-step explanation:

< xcy ≅ < xby

step-by-step explanation:

in δcax and δbax,

so, δcax ≅ δbax (side-angle-side or sas)

since δcax ≅ δbax, we can conclude < acx ≅ < abx

in δacy and δaby,

so, δacy ≅ δaby (side-angle-side or sas)

since δacy ≅ δaby, we can conclude < acy ≅ < aby

now,

< acy ≅ < aby

=> < acx + < xcy ≅ < abx + < xby

=> < acx + < xcy ≅ < acx + < xby   (since < acx ≅ < abx already proved above)

subtracting < acx from both sides, we get

< acx + < xcy -< acx ≅ < acx + < xby -< acx

cancelling out < acx and -< acx from both sides, we get

< xcy ≅ < xby (proved)