Hi there! So the situation that we are talking about for this is compounding, because the population is growing.. The formula for compounding something is P(1 + r)^t, with P representing the initial amount, r representing the rate, and t representing the time in years. First off, let's add the rate into 1. 1.2% is 0.012 in decimal form. 1 + 0.012 is 1.012. Now, because we are looking for the population after 4 years. We raise 1.012 to the 4th power. 1.012^4 is 1.04887093274. It's a long decimal, but do not delete this number from your calculator. Now, we multiply that number by 38,300 in order to get 40,171.7567238 or 40,172 when rounded to the nearest whole number. There. The population of Huntersville will be 40,172 people in 4 years.
The answer would be A. When using Cramer's Rule to solve a system of equations, if the determinant of the coefficient matrix equals zero and neither numerator determinant is zero, then the system has infinite solutions. It would be hard finding this answer when we use the Cramer's Rule so instead we use the Gauss Elimination. Considering the equations:
x + y = 3 and <span>2x + 2y = 6
Determinant of the equations are </span>
<span>| 1 1 | </span>
<span>| 2 2 | = 0
</span>
the numerator determinants would be
<span>| 3 1 | . .| 1 3 | </span>
<span>| 6 2 | = | 2 6 | = 0.
Executing Gauss Elimination, any two numbers, whose sum is 3, would satisfy the given system. F</span>or instance (3, 0), <span>(2, 1) and (4, -1). Therefore, it would have infinitely many solutions. </span>
Hi!
Divide 63 by 3. You will get 21. That is the boys age. His father’s age is 42. When combined their age is 63.
Answer:
to solve the equation, you must multiply both sides by 4 to remove the fraction and get n by itself. so it'll be
n = -48
The LinReg line of best fit for this data set is ŷ = -1.24X + 0.66
<h3>What is regression line?</h3>
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable.
Given:
(−5, 6.3),
(−4, 5.6),
(−3, 4.8),
(−2, 3.1),
(−1, 2.5),
(0, 1.0),
(1, −1.4)
Sum of X = -14
Sum of Y = 21.9
Mean X = -2
Mean Y = 3.1286
Sum of squares (SSX) = 28
Sum of products (SP) = -34.6
Regression Equation,
ŷ = bX + a
b = SP/SSX = -34.6/28 = -1.23571
a = MY - bMX = 3.13 - (-1.24*-2) = 0.65714
ŷ = -1.23571X + 0.65714
ŷ = -1.24X + 0.66
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