Answer:
52 62 72 82 92 102 112 120 121 122 123 124 125 126 127 128 129 132 142
there are 19.
Step-by-step explanation:
Answer:
Ba0.12 if b have x plus 5.10 need to be A
Answer:
Graph A
Step-by-step explanation
The problem: y=2x^2+8x+3
1) You will need to determine the direction of the parabola, If a > 0 then the parabola opens upward, if a < 0 then the parabola opens downward.
2) If when doing this you get an imaginary solution, you would have no x intercepts. The first thing you need to do is find the x intercepts, you can do this by plugging in 0 for y, so this would give you 0=2x^2+8x+3. Once you have this you start by subtracting 3 from both sides, dividing 8 from both side, etc. (If you would like me to elaborate don't hesitate to ask!)
3) To find the graphs y intercept you plug in 0 for x, which gives you y=2(0)^2+8(0)+3, you do the same thing you did during the previous step for y. (Once again, I am willing to elaborate further if you need!)
4) You then need to find the vertex (h,k)
To find h: h= -b/2a
To find k: k = a(h)2 + b(h) + c
5) Once you've worked through this you can graph the point!
Hope this helped!
Answer:
Step-by-step explanation:
Their are 18 medical professionals in total. Their are 3 doctors and 15 nurses. This means that only 17% of the medical professionals are doctors. 10 of the medical professionals are female, leaving the fact that 8 are male. This means that 44% of the Medical professionals are males. If we add these values it would be a total of 61%. We don't need a fractional value because we can use process of elimination. It is more than 1/2 so we can eliminate the 1st and the second answer. In addition to this all these values are out of 18 people therefore the answer would be 8/18
Answer:
Finding the error for each observation, squaring the error and minimizing the sum
Step-by-step explanation:
The regression line is a straight line which gives a description of how the dependent variable y changes as changes occur in the independent variable X. It gives a prediction of the value of Y for any given value of X.
The method of least squares is used for squaring the error and also used for minimising the sum. Therefore, when you calculate the regression line, you would be Finding the error for each observation, squaring the error and minimizing the sum.
