Answer: False
Step-by-step explanation: Skinfold measurements is one of the oldest ways of measuring a person's fat percentage,it's usually taken in specific areas of the body where there are Skinfolds,while taking this measurements it is expected that the person taking it does the average of 2or more repeated measurements in order to ensure that the actual thickness of that area of the body is correctly entered.
It is specifically taken from the right side of the body,where the person pinches out the Skinfolds away from the body by attaching a caliper ,this is to ensure that only the fatty laters are considered, it is mainly presented in percentage.
Answer:
I need the expressions
Step-by-step explanation:
(I don't have the experresions but I think it might be helpful to simplify)
- f - 5 ( 2 f - 3)
−f−10f+15
−f−(10f−15)
(−f−10f)+15
<u>−11f+15
</u>
<u />
<u>Hope this helped</u>
The monthly expenses are found below:
Mortgage - $752 Tim's Chevron - $23Johnny's Allowance - $8Electric - $176Jenny-babysitting - $12Movie - $14Dry Cleaning - $41Tithe - $85,Credit Card - $150Food - $101Phone - $45Water - $16 Car - $272
Adding all the expenses, will give us a total of $1695.
To get the percent which is allocated for movie and the baby sitting:
Add the amount for the two then divide that to the total then multiply it by 100%
= 12 + 14 / 1695 x 100%
= 26 / 1695 x 100%
= 0.015339233 x 100%
=1.5%
The answer is 1.5%
There appears to be a positive correlation between the number of hour spent studydng and the score on the test.
When identifying the independent and dependent quantities, we think about what would cause the other to change. The score on the test would not cause the number of hours spent studying to change; rather, the number of hours spent studying would cause the score to change. This means that the number of hours studying would be the independent quantity and the score would be the dependent quantity.
Plotting the graph with the time studying on the x-axis (independent) and the score on the y-axis (dependent) gives you the graph shown. You can see in the image that there seems to be a positive correlation; the data seem to generally be heading upward.