3.3125 you can find this answer by using a calculator
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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Okay so you need to start off by using the distributive property, meaning you are going to multiply -2 by both items within the parentheses. this gives you -2x + 10 = -14. from here you want to isolate x, so you’ll subtract both sides by 10 to move it to the other side. this gives you -2x=-24. then you’ll divide both sides by -2 to completely isolate x. this gives you x=12. does that make sense?
Answer:
its b
Step-by-step explanation:
Answer:
f(30)= 75
Step-by-step explanation:
3(30)-15
90-15
75
