I don't see the answer choices, but 2 equations you could use to get eighteen would be:
9 × 2 = 18
OR
9 + 9 = 18
See? Twice a number is eighteen. You can use any of my equations to get eighteen
↑ ↑ ↑ Hope this helps! :D
Answer: Option A: 500.
Step-by-step explanation:
If 95% of the students are present in a school, then the number of absent students is 5% of the total number of students. We are given that the number of absent students is 25, so we can set up an equation:
0.05x = 25
Solving for x, we get:
x = 25 / 0.05 = 500
Therefore, the total number of students in the school is 500.
Therefore, the correct answer is Option A: 500.
Please let me know if there’s anything else I can help you with!
What is the value of x?
Your Final Answer would be 5
Hope this helps!
For this case we have the following equation:
Rewriting the equation we have:
Then, we must factor the equation:
We are looking for two numbers that are equal to 7 and multiplied equal to -18.
We have then:
Answer:
An equation that could be used to solve by factoring is:
B. a number greater than 4
C. a multiple of 3
D. an even number
A. It must be between -1 and 1.
B. It must be positive.
C. It indicates the percentage of points that lie on the regression line.
D. The correlation coefficient tells you how strong the residuals are.
E. If correlation is positive, the slope of the regression line is also positive
This is about knowledge between Correlation and Regression.
Option A and E are correct.
Option A; Under different types of correlation coefficient they tend to make use of different range for their definitions but it is always between minimum of -1 and maximum of 1. Thus, the statement is correct.
Option B; It must not be positive because as stated in option A above negative values can be used.
Option C; No, it doesn't indicate percentage of points because in the definition i gave earlier, there is nothing like that. Besides, this is in regression and not correlation.
Option D; This is incorrect as the definition doesn't support it.
Option E; This is correct because positive correlation means that the two variables increase at the same time or decrease at the same time. Thus, it means the regression line will be positive as long as correlation is positive.
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