NEED HELP PLEASE AHHH! A system of equations and it’s solution are given below
1 answer:
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Question:
James has 14,500g of sand in his sandbox. He brings home another 7,400g of sand from the beach to add to his sandbox. How many kilograms of sand does James have in his sandbox now?
Answer:
The quantity of sandbox James has is 21.9kg
Step-by-step explanation:
Given
Initial quantity of sandbox = 14,500 g
Additional quantity = 7,400 g
Required
Total quantity of sandbox in kg
Tp get the total quantity of sandbox, we simply add in the initial and additional quantity of sandbox together;
This is represented mathematically as follows;
Total = Initial Quantity + Additional Quantity
Total = 14,500 g + 7,400 g
Total = 21,900 g
But the question says the answer should be in kg
1000 g is equivalent to 1 kg
21,900 g will be equivalent to
Hence, the quantity of sandbox James has is 21.9kg
Answer:
1970.5 thousand
Step-by-step explanation:
Given
Vehicle sales in 2009 = 10,002 thousand
Vehicle sales in 2013 = 17,884 thousand
Required
Determine the average rate of change;
From the given parameters, it can be seen that the number of vehicles is a function of year
Let x represent the years and y represent the number of vehicles;
Such that;
The rate of change is calculate as follows;
Hence, the average rate of change is 1970.5 thousand
The computer regression output includes the R-squared values, and adjusted R-squared values as well as other important values
a) The equation of the least-squares regression line is
b) The correlation coefficient for the sample is approximately 0.351
c) The slope gives the increase in the attendance per increase in wins
Reasons:
a) From the computer regression output, we have;
The y-intercept and the slope are given in the <em>Coef</em> column
The y-intercept = 10835
The slope = 235
The equation of the least-squares regression line is therefore
b) The square of the correlation coefficient, is given in the table as R-sq = 12.29% = 0.1229
Therefore, the correlation coefficient, r = √(0.1229) ≈ 0.351
The correlation coefficient for the sample, r ≈ <u>0.351</u>
c) The slope of the least squares regression line indicates that as the number of attending increases by 235 for each increase in wins
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