1 kg = 0.001 metric tons..so 14327 kg = (14327 * 0.001) = 14.327 metric tons
and if 1 metric tons saves 17 trees....then 14.327 tons saves : 14.327 * 17 = 243.559 saved trees...and since u cant have 0.559 trees, u would round to 243 trees
The first step u should do is (3+2)
Answer: 95% confidence interval = 20,000 ± 2.12

( 19228.736 , 20771.263 ) OR ( 19229 , 20771 )
Step-by-step explanation:
Given :
Sample size(n) = 17
Sample mean = 20000
Sample standard deviation = 1,500
5% confidence
∴
= 0.025
Degree of freedom (
) = n-1 = 16
∵ Critical value at ( 0.025 , 16 ) = 2.12
∴ 95% confidence interval = mean ± 


Critical value at 95% confidence interval = 20,000 ± 2.12

( 19228.736 , 20771.263 ) OR ( 19229 , 20771 )
Answer:
Answer;
-38 %
Step-by-step explanation:
Explanation;
-Budget busters are the large potential problem areas that can destroy a budget. Failure to control even one of these problem areas can result in financial disaster.
-Housing takes about 38 percent of your monthly budget. Housing decisions should be based on need and financial ability, not on internal or external pressure.
-Food takes 12 percent of your monthly budget. The reduction of a family's food bill requires quantity and quality planning.
-Transportation (purchase and maintenance), takes 15 percent of your monthly budget, Debts takes 5 percent of Net Spendable Income, Insurance takes 5 percent of Net Spendable Income assuming an employer provides medical insurance, Recreation/Entertainment takes 5 percent of Net Spendable Income, Clothing takes 5 percent of Net Spendable Income, Medical and dental takes 5 percent of Net Spendable Income and Savings takes 5 percent of Net Spendable Income
Answer:
Correct option: (a) 0.1452
Step-by-step explanation:
The new test designed for detecting TB is being analysed.
Denote the events as follows:
<em>D</em> = a person has the disease
<em>X</em> = the test is positive.
The information provided is:

Compute the probability that a person does not have the disease as follows:

The probability of a person not having the disease is 0.12.
Compute the probability that a randomly selected person is tested negative but does have the disease as follows:
![P(X^{c}\cap D)=P(X^{c}|D)P(D)\\=[1-P(X|D)]\times P(D)\\=[1-0.97]\times 0.88\\=0.03\times 0.88\\=0.0264](https://tex.z-dn.net/?f=P%28X%5E%7Bc%7D%5Ccap%20D%29%3DP%28X%5E%7Bc%7D%7CD%29P%28D%29%5C%5C%3D%5B1-P%28X%7CD%29%5D%5Ctimes%20P%28D%29%5C%5C%3D%5B1-0.97%5D%5Ctimes%200.88%5C%5C%3D0.03%5Ctimes%200.88%5C%5C%3D0.0264)
Compute the probability that a randomly selected person is tested negative but does not have the disease as follows:
![P(X^{c}\cap D^{c})=P(X^{c}|D^{c})P(D^{c})\\=[1-P(X|D)]\times{1- P(D)]\\=0.99\times 0.12\\=0.1188](https://tex.z-dn.net/?f=P%28X%5E%7Bc%7D%5Ccap%20D%5E%7Bc%7D%29%3DP%28X%5E%7Bc%7D%7CD%5E%7Bc%7D%29P%28D%5E%7Bc%7D%29%5C%5C%3D%5B1-P%28X%7CD%29%5D%5Ctimes%7B1-%20P%28D%29%5D%5C%5C%3D0.99%5Ctimes%200.12%5C%5C%3D0.1188)
Compute the probability that a randomly selected person is tested negative as follows:


Thus, the probability of the test indicating that the person does not have the disease is 0.1452.