Answer: y = -1x + 5
Step-by-step explanation:
1. Plug the coordinates into the slope formula (y2 - y1)/ (x2 - x1)
(0 - 6)/ (5 -(-1))
2. Solve for slope
-6/6
= -1
3. Plug the first coordinate and slope into the point slope form equation (y - y1) = slope(x - x1) to find the rest of the equation of the line
(y -(-1)) = -1( x - 6)
4. Solve for y
y + 1 = -1x + 6 -----> y = -1x + 5
Answer:

Step-by-step explanation:
We can solve this system just by summing each side of the equation:
So, both left sides will be sum, and both right sides too.
The resulting expression will be:

Ordering and solving both sides:

Hence, the value to the system is 
It's important to combine both equation, because the exercise is asking for the solution of the system.
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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<em> </em><em>the</em><em> </em><em>answer</em><em> </em><em>is</em><em> </em><em>1</em><em>5</em><em>.</em><em> </em><em>Hope</em><em> </em><em>this</em><em> </em><em>will</em><em> </em><em>help</em><em> </em><em>u</em><em>.</em><em>.</em><em>.</em><em>.</em><em>.</em>
Answer:
10.89
Step-by-step explanation: