The Owner's Equity for commercial banks in 2017-2018 is $0.4 billion.
The given is,
Borrowings = $0.10 Billion
Owner's Equity = $0.40 Billion
<h3 /><h3>What is the formula for the total liabilities?</h3>
Total liabilities = deposits + borrowings
So we have Borrowings = total liabilities - deposits
Borrowings in 2017
= $14.60 - 11.90
= $2.70 billion
Borrowings in 2018
= $14.80 - $12.20
= $2.60 billion
Borrowings from 2017-2018
= 2.60 - 2.70
= $0.10 billion
Owner's Equity= total assets - total liabilities
Owner’s equity in 2017
= $16.2 - $14.6
= $1.6 billion
Owner’s equity in 2018
= $16.8 - $14.8
= $2 billion
Owner's Equity from 2017-2018
= 2 - 1.6
= $0.4 billion
To learn more about the Borrowings visit:
brainly.com/question/15948713
Answer:
8/4
Step-by-step explanation:
because 8 is x and x goes first and y is 4 and y goes last
hope this helps!
Answer:
0.064
Step-by-step explanation:
( 0.4) ^3
Solution :
( 0.4) ^3
= 0.4 x 0.4 x 0.4
= 0.064
Answer:
<h3>A reflection across the line x=3, a reflection across the x-axis and a dilation with a scale factor of 2, because each side is double.</h3><h3>
Step-by-step explanation:</h3>
We know that the first transfomration is a rotation 90° clockwise.
Notice that vertex R is at the same horizontal coordinate than vertex C, which means the second transformation must include a reflection across the line x=3, a reflection across the x-axis and a dilation with a scale factor of 2, because each side is double.
Answer:
The probability that the sample mean would differ from the population mean by more than 2.6 mm is 0.0043.
Step-by-step explanation:
According to the Central Limit Theorem if we have a population with mean μ and standard deviation σ and appropriately huge random samples (n > 30) are selected from the population with replacement, then the distribution of the sample means will be approximately normally distributed.
Then, the mean of the distribution of sample mean is given by,
And the standard deviation of the distribution of sample mean is given by,
The information provided is:
<em>μ</em> = 144 mm
<em>σ</em> = 7 mm
<em>n</em> = 50.
Since <em>n</em> = 50 > 30, the Central limit theorem can be applied to approximate the sampling distribution of sample mean.
Compute the probability that the sample mean would differ from the population mean by more than 2.6 mm as follows:
*Use a <em>z</em>-table for the probability.
Thus, the probability that the sample mean would differ from the population mean by more than 2.6 mm is 0.0043.
