Answer:
(A) The odds that the taxpayer will be audited is approximately 0.015.
(B) The odds against these taxpayer being audited is approximately 65.67.
Step-by-step explanation:
The complete question is:
Suppose the probability of an IRS audit is 1.5 percent for U.S. taxpayers who file form 1040 and who earned $100,000 or more.
A. What are the odds that the taxpayer will be audited?
B. What are the odds against such tax payer being audited?
Solution:
The proportion of U.S. taxpayers who were audited is:
P (A) = 0.015
Then the proportion of U.S. taxpayers who were not audited will be:
P (A') = 1 - P (A)
= 1 - 0.015
= 0.985
(A)
Compute the odds that the taxpayer will be audited as follows:
Thus, the odds that the taxpayer will be audited is approximately 0.015.
(B)
Compute the odds against these taxpayer being audited as follows:
Thus, the odds against these taxpayer being audited is approximately 65.67.
Answer:
A VR car game and an iphone
Step-by-step explanation:
:)
Answer:
The consecutive odd integers are (15, 17, 19) or (-17, -15, -13).
Step-by-step explanation:
Let the three consecutive odd integers be: 
The condition given is:
![4[x-2+x+x+2]=3x(x+2)-765](https://tex.z-dn.net/?f=4%5Bx-2%2Bx%2Bx%2B2%5D%3D3x%28x%2B2%29-765)
Solve this for <em>x</em> as follows:
![4[x-2+x+x+2]=3x(x+2)-765\\4\times 3x=3x^{2}+6x-765\\3x^{2}-6x-765=0\\x^{2}-2x-255=0\\x^{2}-17x+15x-255=0\\x(x-17)+15(x-17)=0\\(x-17)(x+15)=0](https://tex.z-dn.net/?f=4%5Bx-2%2Bx%2Bx%2B2%5D%3D3x%28x%2B2%29-765%5C%5C4%5Ctimes%203x%3D3x%5E%7B2%7D%2B6x-765%5C%5C3x%5E%7B2%7D-6x-765%3D0%5C%5Cx%5E%7B2%7D-2x-255%3D0%5C%5Cx%5E%7B2%7D-17x%2B15x-255%3D0%5C%5Cx%28x-17%29%2B15%28x-17%29%3D0%5C%5C%28x-17%29%28x%2B15%29%3D0)
- If
then the value of <em>x</em> is 17.
The odd numbers are:
- If
then the value of <em>x</em> is -15.
The odd numbers are:
Thus, the consecutive odd integers are (15, 17, 19) or (-17, -15, -13).
There could be a strong correlation between the proximity of the holiday season and the number of people who buy in the shopping centers.
It is known that when there are vacations people tend to frequent shopping centers more often than when they are busy with work or school.
Therefore, the proximity in the holiday season is related to the increase in the number of people who buy in the shopping centers.
This means that there is a strong correlation between both variables, since when one increases the other also does. This type of correlation is called positive. When, on the contrary, the increase of one variable causes the decrease of another variable, it is said that there is a negative correlation.
There are several coefficients that measure the degree of correlation (strong or weak), adapted to the nature of the data. The best known is the 'r' coefficient of Pearson correlation
A correlation is strong when the change in a variable x produces a significant change in a variable 'y'. In this case, the correlation coefficient r approaches | 1 |.
When the correlation between two variables is weak, the change of one causes a very slight and difficult to perceive change in the other variable. In this case, the correlation coefficient approaches zero
