<span><span>9<span>(6j+2+j)</span></span><span>9<span>(6j+2+j)</span></span></span>Add <span><span>6j</span><span>6j</span></span> and <span>jj</span> to get <span><span>7j</span><span>7j</span></span>.<span><span>9<span>(7j+2)</span></span><span>9<span>(7j+2)</span></span></span>Apply the distributive property.<span><span>9<span>(7j)</span>+9⋅2</span><span>9<span>(7j)</span>+9⋅2</span></span>Multiply <span>77</span> by <span>99</span> to get <span>6363</span>.<span><span>63j+9⋅2</span><span>63j+9⋅2</span></span>Multiply <span>99</span> by <span>22</span> to get <span>1818</span>.<span>63j+<span>18</span></span>
Answer:
C. 8
Step-by-step explanation:
The average rate of change is the slope of the line between the two points of interest. It is given by the formula ...
m = (y2 -y1)/(x2 -x1)
For points (1, 2) and (3, 18), the average rate of change is ...
m = (18 -2)/(3 -1) = 16/2 = 8
The average rate of change between x=1 and x=3 is 8.
Answer:
1)2400
is the result of rounding 2376.458 to the nearest 100.
2) 2376
is the result of rounding 2376.458 to the nearest integer.
3)2376.46
is the result of rounding 2376.458 to the nearest 0.01
I hope this is right and it helps !!!!!!!!!!!!!!!!
Independent variable is the predictor variable which is the height and dependent variable is the response variable which is weight in this scenario.
The square of correlation coefficient gives the coefficient of determination. It is denoted by R² (R squared).
We are given:
R = 0.75
So,
R² = 0.75²
R² = 0.5625
R² = 56.25 %
The coefficient of determination tells how much of the trend of dependent data can be explained by the independent data using the linear regression model. So in the given case, Height can explain 56.25% of the trend in the weight.
Answer:
both the first one
Step-by-step explanation:
use unit rate
