Answer:
The distance between the given points (2,10) and (-6, 4) on the coordinate plane is 10units
Therefore distance s=10 units
Step-by-step explanation:
Given points are (2,10) and (-6, 4) on the coordinate plane
To distance between the given points :
The distance formula is 
units
Let 
,
be the given points (2,10) and (-6, 4) respectively
Now substituting the values in the distance formula we get
Therefore s=10 units
The distance between the given points (2,10) and (-6, 4) on the coordinate plane is 10units
Notice how on both sides of the equation there is one coefficient (number being multiplied by x) and a constant (just a plain number). You want all constants to be on one side and all coefficients on the other. He’s how do to that:
2x+14=-21-5
I am choosing that all constants will be on the right side, so I will do the inverse operation of any constant on the left side to remove it. Here, a constant on the left side is 14. I will subtract 14 from both sides of the equation. Positive 14 minus 14 is zero, so it cancels out and removes it.
2x+14-14 = -21-5x
2x = -35-5x
See how the 14 is now gone? The equation looks much simpler now. Okay next, you can see that there is a coefficient on the “constant side” that I’ve chosen, so I am going to remove that. Negative 5 plus positive 5 equals zero. Do this on both sides of the equation.
2x+5x = -35-5x+5x
7x = -35
Now the equation is just two numbers. All that is left to do is divide by the last coefficient, 7, in this case.
7x/7 = -35/7
x = -5
just use the standart recursive for geometric sequence
this question is incomplete, the complete question is:
1. is this model effective
2. what is the correlation coefficient for this data.
3. for a student with a bmi of 25, what is the predicted number of hours under the influence.
Step-by-step explanation:
1. first of all this model is not effective because we have r² as 0.134. this tells us that only 13.4 percent of the of the variations that exist in this data has been explained by the model
1. we get the correlation coefficient by
the regression slope coefficient has a negative sign. this is what we would use in calculating the correlation coefficient.
= -√0.134
= -0.366
therefore the correlation coefficient is -0.366
2. to get the number of hours under the influence with a bmi of 25
the equation is
49.2-1.15bmi
= 49.2-1.15(25)
= 49.2-28.75
= 20.45
