Answer:
x3+7x-3
Step-by-step explanation:
Answer: B) Demand will most likely be elastic
Place yourself in the shoes of the employer. To them, demand is them needing/wanting workers. Specifically we call this "labor demand". The supply is the potential or current worker providing the service and/or making the product.
If the price goes up, then this means the worker earns higher wages. This in turn causes labor demand to fall. So the employer will be less likely to hire more workers if the wages increase. It's similar to how if the price of an item goes up in a store, then less people are probably going to buy it.
Demand is elastic because a small change in price causes a large change in demand. The company is going to be sensitive to wage changes. The company sees that it is approaching the diminishing returns, so it is likely to scale back on labor to save costs. It's all about trying to minimize costs and maximize revenue. Often, revenues can't be changed very much since customers are themselves sensitive to price changes (assuming there are substitutes in the market), so the company will turn to trying to reduce costs as much as possible leading to maximum profit.
Answer:
I think this could be the answer- x^ +2x-15
Answer:
y=1/2x+6
Step-by-step explanation:
for the equation you start with finding the rate of change (wich is what y changes by as x 9minths) in creases by 1) and then add the Y-intercept, so it would be Y=1/2x+6
Formula:
Y=△y•x+b(y-int)
To find y intercept find what <em>y </em>is when x is 0, or in this case you know it comes out 6 feet tall, so that is what the Y int. is
Answer:
(a) Shown below
(b) There is a positive relation between the number of assemblers and production.
(c) The correlation coefficient is 0.9272.
Step-by-step explanation:
Let <em>X</em> = number of assemblers and <em>Y</em> = number of units produced in an hour.
(a)
Consider the scatter plot below.
(b)
Based on the scatter plot it can be concluded that there is a positive relationship between the variables <em>X</em> and <em>Y</em>, i.e. as the value of <em>X</em> increases <em>Y</em> also increases.
(c)
The formula to compute the correlation coefficient is:
![r=\frac{n\sum XY-\sum X\sum Y}{\sqrt{[n\sum X^{2}-(\sum X)^{2}][n\sum Y^{2}-(\sum Y)^{2}]}} }](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%5Csum%20XY-%5Csum%20X%5Csum%20Y%7D%7B%5Csqrt%7B%5Bn%5Csum%20X%5E%7B2%7D-%28%5Csum%20X%29%5E%7B2%7D%5D%5Bn%5Csum%20Y%5E%7B2%7D-%28%5Csum%20Y%29%5E%7B2%7D%5D%7D%7D%20%7D)
Compute the correlation coefficient between <em>X</em> and <em>Y</em> as follows:
![r=\frac{n\sum XY-\sum X\sum Y}{\sqrt{[n\sum X^{2}-(\sum X)^{2}][n\sum Y^{2}-(\sum Y)^{2}]}} }=\frac{(5\times430)-(15\times120)}{\sqrt{[(5\times55)-15^{2}][(5\times3450)-120^{2}]}} =0.9272](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%5Csum%20XY-%5Csum%20X%5Csum%20Y%7D%7B%5Csqrt%7B%5Bn%5Csum%20X%5E%7B2%7D-%28%5Csum%20X%29%5E%7B2%7D%5D%5Bn%5Csum%20Y%5E%7B2%7D-%28%5Csum%20Y%29%5E%7B2%7D%5D%7D%7D%20%7D%3D%5Cfrac%7B%285%5Ctimes430%29-%2815%5Ctimes120%29%7D%7B%5Csqrt%7B%5B%285%5Ctimes55%29-15%5E%7B2%7D%5D%5B%285%5Ctimes3450%29-120%5E%7B2%7D%5D%7D%7D%20%3D0.9272)
Thus, the correlation coefficient is 0.9272.
