The two strands of Decorative light will shine at same time after every 15 seconds.
<h3>What is defined as the Highest common factor?</h3>
- The greatest of all their common factors is the Highest Common Factor (HCF) of more than one number.
- As a result, it is also known as the greatest common factor (GCF).
- There isn't an exact formula for determining the greatest common factor of two numbers.
- However, when considering prime factorization, we can write a statement that aids in evaluating the greatest common factor of more than one number.
The stated condition are-
The light strand one change color every 30 seconds.
The light strand second change color every 45 seconds.
Prime factorization of both number are;
30 = 2×3×5
45 = 3×3×5
The numbers with highest power are;
HCF(30, 45) = 3×5 = 15.
Thus, the time after which both the light strands will change colour at the same time is 15 seconds.
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Answer:
$189
Step-by-step explanation:
Let their total amount of spending money be x
given that $135 they spent is 5/7 of their total spending money,
mathematically:
135 = (5/7) x (multiply both sides by 7)
135 (7) = 5x
945 = 5x
5x = 945 (divide both sides by 5)
x = 945 / 5
x = 189
Answer: 3 1/2 ( mixed number)
Step-by-step explanation:
- First you have to put 3.5 into na fraction
- In a fraction it is 35/10
- as a mixed number, it would be: 3 5/10 ( a mixed number )
- Bur we still have to simplify so simplified would be: 3 1/2 ( mixed number)
33x I think sorry if I am wrong
Answer:
B) The sum of the squared residuals
Step-by-step explanation:
Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).
Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.
Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.
Therefore, option B gives the correct answer.
