Answer:
First look at the number of bricks alone.
Going from 50 bricks to 60 bricks is more work, thus it will require more people. The number of people would be the ratio of the 2. Since the number must be larger, you know the numerator must be the larger of the 2 numbers, so you get 60/50
Next look at the time alone.
Going from 30 minutes to 20 minutes is more work, thus it will require more people. The number of people would be the ratio of the 2. Since the number must be larger, you know the numerator must be the larger of the 2 numbers, so you get 30/20
Now you can just multiply everything.
= 5*60/50*30/20
= 5*6/5*3/2
= 90\10
= 9.
The answer is 10 cm
Imagine cement cover as a rectangle which volume is 660,000,000 cm3. So this rectangle has width (w = 60m), length (l = 110m), and height (h = ?). The height of the rectangle is actually a thickness of the cement layer. So, we will use the formula for the volume (V) of the rectangle to calculate the thickness:
V = w · l · h
It is given:
V = 660,000,000 cm³
w = 60 m = 60 · 100 cm = 6,000 cm
l = 110 m = 110 · 100 cm = 11,000 cm
h = ?
Using the formula: V = w · l · h
660,000,000 = 6,000 · 11,000 · h
660,000,000 = 66,000,000 · h
⇒ h = 660,000,000 ÷ 66,000,000 = 10 cm
Answer:
Yes, the probability distribution is valid.
Step-by-step explanation:
Given :
Income range ___ midpoint(x) ___ %
5 - 15 __________ 10 _____20%
15 - 25 _________ 20 _____13%
25- 35 _________ 30 _____ 21%
35 - 45 _________40 _____ 17%
45 - 55_________ 40 ____ 20%
55 or more _______ 50 _____9%
Yes, we do have a valid probability distributuon ; as the summation of the percentage values is equal to 100%
Answer:
a. There is no blocking variable, and incentive plans will be randomly assigned to the workers.
Step-by-step explanation:
The Randomized Complete Block Design (RCBD) is a standard experimental design where experimental units or subjects are grouped as blocks (also known as replicates). In RCBD, subjects within each block are randomly assigned to the experimental units within a block. RCBD is a type of design that reduces variability by controlling variation within each treatment, thereby enhancing the estimation of the treatment effects (combinations of the factor levels of the different factors).
