Answer:
what
Step-by-step explanation:
Answer:
1,600,000
Step-by-step explanation:
120,000,000 divided by 75 is 1,600,000
Answer:
Step-by-step explanation:
so we see the two line of the same length of 14, which tells us that the lengths of 5x and 7x-8 have to be the same length. so we know we can set them equal to each other. Often times that's the key to math problems , finding where you can set things equal.
5x = 7x-8 ( subtract 5x from both sides and then add 8 to both sides )
8 = 2x ( divide both sides by 2 )
4 = x
nice this looks right too , both sides add to 20 :)
In order to divide $306 into the ratio of 9 to 5 to 3, first you have to make each section of the ratio represented by a variable, and put it into an equation.
9x + 5x + 3x = 306
Our next step is to simplify the left side of the equation by combining all of the like terms, the ones that contain variables.
17x = 306
Finally, to solve this equation, you have to divide both sides by 17 to isolate the variable x on the left side of the equation.
x = 18
However, this is not our answer as it isn't in a ratio format and doesn't really make sense. To find our ratio, we have to multiply each of our initial numbers (9, 5, and 3) by our variable, x.
9 * 18 = 162
5 * 18 = 90
3 * 18 = 54
You can verify that these numbers are correct because if you add them together you get 306.
Your final ratio is 162:90:54.
Hope this helps! :)
If you have a separate specific question about pre-tax prices and PMed me the link, I'd be happy to help you.
Type I error says that we suppose that the null hypothesis exists rejected when in reality the null hypothesis was actually true.
Type II error says that we suppose that the null hypothesis exists taken when in fact the null hypothesis stood actually false.
<h3>
What is
Type I error and Type II error?</h3>
In statistics, a Type I error exists as a false positive conclusion, while a Type II error exists as a false negative conclusion.
Making a statistical conclusion still applies uncertainties, so the risks of creating these errors exist unavoidable in hypothesis testing.
The probability of creating a Type I error exists at the significance level, or alpha (α), while the probability of making a Type II error exists at beta (β). These risks can be minimized through careful planning in your analysis design.
Examples of Type I and Type II error
- Type I error (false positive): the testing effect says you have coronavirus, but you actually don’t.
- Type II error (false negative): the test outcome says you don’t have coronavirus, but you actually do.
To learn more about Type I and Type II error refer to:
brainly.com/question/17111420
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