1 pound = 16 ounces
6 ounces each pie
36 mini pies to make
36 × 6 = 216 ounces needed to make 36 mini pies
216 ÷ 16 = 13.5 pounds
if each can of pumpkin is 2 pounds,
13.5 ÷ 2 = 6.75 so that would be 7 cans of pumpkin
Answer:

Step-by-step explanation:
Given that:

for 
That means, angle
is in the 3rd quadrant.
To find:
Value of cot(t)
Solution:
First of all, let us recall what trigonometric ratios are positive and what trigonometric ratios are negative in 3rd quadrant.
In 3rd quadrant, tangent and cotangent are positive.
All other trigonometric ratios are negative.
Let us have a look at the following identity:

here, 
So, 

But, angle
is in 3rd quadrant, so value of

Answer:
The probability that a randomly chosen tree is greater than 140 inches is 0.0228.
Step-by-step explanation:
Given : Cherry trees in a certain orchard have heights that are normally distributed with
inches and
inches.
To find : What is the probability that a randomly chosen tree is greater than 140 inches?
Solution :
Mean -
inches
Standard deviation -
inches
The z-score formula is given by, 
Now,





The Z-score value we get is from the Z-table,


Therefore, the probability that a randomly chosen tree is greater than 140 inches is 0.0228.
Answer:
C) 
Step-by-step explanation:
Line of best fit (trendline) : a line through a scatter plot of data points that best expresses the relationship between those points.
All the given options for the line of best fit are linear equations.
Therefore, we can add the line of best fit to the graph (see attached), remembering to have roughly the same number of points above and below the line.
Linear equation: 
(where
is the slope and
is the y-intercept)
From inspection of the line of best fit, we can see that the y-intercept (where x = 0) is approximately 8. So this suggests that options C or D are the solution.
We can also see that the slope (gradient) of the line of best fit is approximately -0.5 (as the rate of change (y/x) is -1 unit of y for every +2 units of x).
Therefore, C is the solution, and the closet approximation to the line of best fit is 