Let the number of runs made on the home run be x, then for the <span>two 3-run home runs, we have 2x
Let the number of runs made in each hit be y, then for the 4 hits that each scored 2 runs, we have 4y.
Thus the algebraic expression to model the total score is 2x + 4y.
Because, there are 3 runs per home run, then x = 3 and because there are 2 runs per hit, then y = 2.
Therefore, the total score is given by 2(3) + 4(2) = 6 + 8 = 14.
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The horizontal distance between Carl and the rock at sea is approximately 60.62ft.
Data;
- Angle = 30 degree
- Opposite = 35
- Adjacent = x
<h3>Trigonometric Ratio</h3>
Given the angle of depression from his point to the sea, we can use trigonometric ratio to calculate for the horizontal distance from his location to the bottom of the sea.
SOHCAHTOA
Since we have the value of angle and opposite and we need to calculate the adjacent side of the right-angle triangle, we can use the tangent of the angle to this effect.

The horizontal distance between Carl and the rock at sea is approximately 60.62ft.
Learn more on trigonometric ratio here;
brainly.com/question/12172664
Here, Sequence : -2, -6, -18, -54
Common Ratio = a₂/a₁ = -6/-2 = 3
As the expression is in negative sign, it's sequence will be -3ˣ
Where x = number of terms in the sequence [ By this rule, you can calculate the unknown nth number]
In short, Option C is your answer!!!!
Let me know, if you have some doubt.
The length is 10.
Since the two points share the same y-coordinate, the segment rt is horizontal.
This means that the length of the side is simply the difference (in absolute value) between the x coordinates of the two points.
So, we have

Answer:
<em>In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.</em>