<u>Answer:</u>
c= -1/2
<u>Step-by-step explanation:</u>
Let's solve your equation step-by-step.
4
/3 = −6c −
5/
3
Step 1: Simplify both sides of the equation.
4
/3 =−6c + −5
/3
Step 2: Flip the equation.
−6c + −5
/3 = 4/3
Step 3: Add 5/3 to both sides.
−6c + −5
/3 + 5
/3 = 4/3 + 5
/3 −6c
=3
Step 4: Divide both sides by -6.
−6c −6 = 3
− 6
c = −1
/2
Answer:
Yes you are correct :)
Step-by-step explanation:
Used a graphing calculator
Answer:
Here is the number of juice boxes
Step-by-step explanation:
No matter what source there is, you will always appear to end up finding out that there are precisely 69 juice boxes
Answer:
Step-by-step explanation:
Recall the formula for the sine of the double angle:
we know that 
, and that 
is in the interval between 0 and 90 degrees, where both the functions sine and cosine are non-negative numbers. Based on such, we can find using the Pythagorean trigonometric property that relates sine and cosine of the same angle, what 
is:
With this information, we can now complete the value of the sine of the double angle requested:
Answer:
Step-by-step explanation:
Correlation occurs when we can observe a trend between the response/dependent variable (y) and the explanatory/independent variable (x).
When comparing two sets of data, we may observe and use correlation to determine whether or not the data presented if significant or not and whether or not it supports our hypothesis. We may want to see if this is just a fluke and whether or not the trend causes causation.
An example of this would be if we thought that the weight of female mice determines how many kids they have in a month.. Let's say that mice that weigh up to one ounce have 6 baby mice per month.
Let's say that the x variable is the weight which is between 0.25 oz and 1.25 oz and the y-variable is the amount of kids between each month. If another laboratory conducts the same study with the same type of mice with the same weights as us, we would need to determine if there is correlation and if there is causation and try to use this information to determine if both sets of data are significant to our hypothesis.
