Answer:
C (the third graph)
Step-by-step explanation:
This graph's function has a domain and range that both exclude one value, which is 0. The x and y values are never 0 in the function, as it approaches 0 but never meets it.
Answer:
Regular Deal
Step-by-step explanation:
<em>Pay as you go</em>
Pay only $6 each time you work out
<em>Regular Deal</em>
Pay $50 a month and $2 each time you work out
<em>All-in-one price! </em>
Pay just $100 per month for unlimited use of our great facilities
1. Carlo thinks he will go to the gym about 20 times a month. Which of these options is the least expensive for Carlo? Show how you determined your answer.
For 20 visits to the gym:
<em></em>
<em>Pay as you go:</em>
20 × $6 = $120
<em>Regular Deal</em>
$50 + 20 × $2 = $50 + $40 = $90
<em>All-in-one price! </em>
$100
<u><em>Answer:</em></u>
The best deal is for 20 visits per month is: <em>Regular Deal</em>
Answer:
x < 0.382
Step-by-step explanation:
because we know that 34x is smaller than 13. therefore after division we can find out that x must be smaller than 0.382
40/10 = 4
24/6 = 4
56/15 = 3.73
<span>The two figures are not similar
</span>
answer
No
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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