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marin [14]
2 years ago
9

1. Jillian and Harold both leave the mall to go home headed in different directions. The angle between their paths towards home

is 57(degrees). Harold lives 3.7km from the mall and Jillian lives 2.3km. Determine how far apart these two friends live from each other.
Mathematics
1 answer:
mr_godi [17]2 years ago
5 0

Answer:

ok mlol

Step-by-step explanation:

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Nancy walked 516 yards in 6 minutes and then another 368 yards in 4 minutes. What was Nancy's average speed? * A. 89 yd/min B. 8
Rina8888 [55]
Average speed = (total distance covered) / (time to cover the whole distance)
 
Total distance = (516 + 368) = 884 yards
Total time  = (6 + 4) = 10 minutes  

Average speed = (884 yards) / (10 minutes)

Average speed = 88.4 yards/minute  (C)


Other ways to write the same speed:

-- 4.42 feet/second
-- 265.2 feet/minute
-- 53.04 inches/second
-- 5,304 yards/hour
-- 3.014 mph (rounded)
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7 0
3 years ago
The sum of two numbers is 52. The greater number is 4 more than the smaller number. Which equation can be used to solve for the
MA_775_DIABLO [31]

Answer:Let's assume x be the smaller number.

Given that, The greater number is 4 more than the smaller number.

So, greater number= x+4.

It's also given that, The sum of two numbers is 52. Which means sum of x and x+4 must be equa to 52. Hence,

x + (x + 4) = 52

Step-by-step explanation:

6 0
2 years ago
Help me please express the simplest radical form
Lelechka [254]

Answer:

8\sqrt{2}

Step-by-step explanation:

6 0
2 years ago
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Consider the equation x^2= -36, what are the solutions?
Kruka [31]
The correct answer is:  [D]:  "no real solutions" .
_____________________________________________________
   
The only "answers" would be:  " <span>± 6i " ;  
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7 0
3 years ago
A set of bivariate data was used to create a least squares regression line. Which of the following is minimized by the line
77julia77 [94]

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.

5 0
2 years ago
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