Part A: The relationship between the temperature of the city and the number of cups of hot chocolate sold seems to be somewhat scattered and not completely linear. There is a general trend that as the temperature increases, the number of cups of hot chocolate sold tends to decrease, but there are some fluctuations in the data points.
Part B: To make the line of best fit, we can first calculate the slope using the formula:
slope = (nΣ(xy) - ΣxΣy) / (nΣ(x^2) - (Σx)^2),
where n is the number of data points, Σ represents the sum of the values, and (x,y) are the data points. Using the data points provided, we get:
n = 11
Σx = 465
Σy = 145
Σxy = 7820
Σ(x^2) = 24195
Substitute these values into the formula, we get:
slope = (11*7820 - 465*145) / (11*24195 - 465^2) = 0.136
Next, we can calculate the y-intercept using the formula:
y-intercept = (Σy - slope*Σx) / n,
Substitute the values we have:
y-intercept = (145 - 0.136*465) / 11 = 2.909
Therefore, the approximate slope of the line of best fit is 0.136 and the y-intercept is 2.909.