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Dafna11 [192]
3 years ago
5

The practice of intentionally targeting borrowers in poor or underserved areas with expensive high-cost loans is known as:

Business
1 answer:
Yanka [14]3 years ago
4 0

Answer:

Reverse redlining

Explanation:

Reverse redlining means the practice that target the neighborhood specially the non-white for the greater prices or lended the non-fair terms like lending used for predatory with respect to the subprime mortgage

So as per the given situation, it is the reverse redlining as it is targetted to the borrowers or the areas having high cost loans

So, the same should be considered

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Highlight four ways of improving productivity of labour as a factor of production​
UNO [17]

Answer:

Following are the four ways for improving the productivity of the labour is given below

Explanation:

  • If we give the proper resource assignment to each worker at the proper time we will increase the productivity of the labor .
  • Increasing the skills by guiding the labor to the latest technology so they  know the latest or the current technology they will increase the productivity .
  • If we enhancing the physical capital it means if we dependent on the machinery they will increase the productivity .
  • Giving the incentive to labor at the particular interval of time we will increase the productivity.
3 0
3 years ago
Coke and Pepsi are examples of
zaharov [31]

Coke and Pepsi are examples of soft drinks.

Hope this helps!

Have a great day!

7 0
3 years ago
Read 2 more answers
As a researcher, what would you do if you set to reject the null and found the null to be true?
soldi70 [24.7K]
Set the significance level of the probability of making a type error to be small 0.01, 0.05, or 0.10 compare the P value to if the P value is less than or equal to reject the no hypothesis in favor of the alternative hypothesis.
4 0
3 years ago
Consider the following linear program: Min s.t. 8X + 12Y 1X + 3Y >= 9 2X + 2Y >= 10 6X + 2Y >= 18 A, B >= 0 a. Use t
mihalych1998 [28]

Answer: Graph of (A) (B) and {D) are attached accordingly.

Explanation:

A)

The critical region of the constraints can be seen in the following diagram -

(0,9) (0,5) (0,3) (0,0) (3,0) (5,0) (9,0) The feasible region is shown in white

The intersection points are found by using these equations -

Vertex Lines Through Vertex Value of Objective

(3,2) x+3y = 9; 2x+2y = 10 48

(9,0) x+3y = 9; y = 0 72

(2,3) 2x+2y = 10; 6x+2y = 18 52

(0,9) 6x+2y = 18; x = 0 108

So, we can see the minimum value of the objective function occurs at point (3,2) and the minimum value of the objective function is = 48.

------------------------------------------------------------------------------------------------------------------------------------------------------------------

B)

When we change the coefficients of the variables in the objective function, the optimal solution may or may not change as the weights (coefficient) are different for each constraints for both the variabls. So, it all depends on the coefficient of the variables in the constraints.

In this case, the optimal solution does not change on changing the coefficient of X from 8 to 6 in the objective function.

The critical region would remain same (as shown below) as it is defined by the constraints and not the objective function.

(0,9) (0,5) (0,3) (0,0) (3,0) (5,0) (9,0) The feasible region is shown in white

However, the optimal value of the objective function would change as shown below-

Vertex Lines Through Vertex Value of Objective

(3,2) x+3y = 9; 2x+2y = 10 42

(9,0) x+3y = 9; y = 0 54

(2,3) 2x+2y = 10; 6x+2y = 18 48

(0,9) 6x+2y = 18; x = 0 108

So, we can see that the minimum value now has become 42 (which had to change obviously).

-------------------------------------------------------------------------------------------------------------------------------------------------------

C)

Now, when we change the coefficient of the variable Y from 12 to 6, again the critical region would remain same as earlier. But in this case, the optimal solution changes as shown below -

Vertex Lines Through Vertex Value of Objective

(3,2) x+3y = 9; 2x+2y = 10 36

(9,0) x+3y = 9; y = 0 72

(2,3) 2x+2y = 10; 6x+2y = 18 34

(0,9) 6x+2y = 18; x = 0 54

We can see that the minimum value now occurs at (2,3) which is 34, so both the optimal solution and optimal value have changed in this case.

----------------------------------------------------------------------------------------------------------------------------------------------------------

D)

When we limit the range of the variables as -

4 \leq X \leq 8 \:\: and\:\: 12\leq Y \leq 24,

the critical region now becomes -

So, the new critical points are (4,12), (4,24), (8,24) and (8,12).

So, the values of the objective function at these points can be calculated as -

Vertex Value of Objective

(4,12) 8*4+12*12 = 176

(4,24) 8*4+12*24 = 320

(8,24) 8*8+12*24 = 352

(8,12) 8*8+12*12 = 208

So, the new optimal solution is (4,12) and the optimal value is 176.

if we knew the range of the variables in the part B and C earlier, we could have just said that the optimal solution will not change as the value would have been no longer depended on the coefficients of variables in the constraints.

7 0
4 years ago
A company begins a review of ordering policies for its continuous review system by checking the current policies for a sample of
Blizzard [7]

Answer:

a. The EOQ for this item is 184 units

b. The desired safety stock is 47 units

c. The reorder point is 377 units.

d.  Holding co is 1,288.

The Ordering co isst 1,291

Explanation:

According to the given data we have the following:

Annual Demand (d) = 48*110 = 5280 units

a) Therefore,  EOQ = sqrt(2*D*S/H) = sqrt(2*5280*45/14) = 184 units

b) Safety Stock = Z*SD*sqrt(LT)

For Service level of 90%, Z value is 1.28

SD = 21

Lead Time (LT) = 3

Therefore, Safety Stock = 1.28*21*sqrt(3) = 47 units

c) Reorder point is D*LT + Z*SD*sqrt(LT)

= 110*3 + 47 = 377 units

d) If currently Q = 300, R = 370

Holding cost = Q/2*H = 300/2*14 = 2100$

Ordering cost = D/Q*S = 5280/300*45 = 792$

Total = 2,892$

For Q=EOQ=184 & R = 377

Holding cost = 184/2*14 = 1,288

Ordering cost = 5280/184*45 = 1,291

Total = 2,579$

8 0
3 years ago
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