Answer:
Typist A can type faster
Step-by-step explanation:
Replace the t in w = 50t with any numbers and graph the points you get
You can see that typist A can type 50 words per minute when typist B can only type 40
Answer:I can help you round 206834 and 194268 to its nearest thousands place. 207000 would be the estimate for the first number and 194000 would be the estimate for the second number.
Use the distributive property to multiply y by -6y^3 and -8. This gives you -6y^4 - 8y + y^4. Add the like terms and you get -5y^4 - 8y.
Therefore, the simplest form of this expression is -5y^4 - 8y.
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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Answer: The answer is number 4
Step-by-step explanation:
