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Find Volume of 1 tennis ball:
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Volume of 1 tennis ball = 4/3 x 3.14 x (2.5 ÷ 2)³
Volume of 1 tennis ball = 8.18 in³
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Find Volume of 3 tennis balls:
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Volume of 3 tennis balls = 8.18 x 3 = 24.54 in³
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Find Volume of teh cylindrical canister:
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Volume of the cylindrical canister = 3.14 x 1.5² x (2.5 x 3)
Volume of the cylindrical canister = 52.99 in³
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Find unoccupied space:
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Unoccupied space = 52.99 - 24.54
Unoccupied space = 28.45 in³
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Answer: 28.45 in³
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4/133. As you remove each ball from the bucket, the total number of balls goes down by 1.
the equation should be
(number of balls of targeted color / all balls in the bucket)
And as you take 4 times, you have to do this for 4 times
Answer:
C
Step-by-step explanation:
We can use process of elimination
D is incorrect because the roots are 3 and -4 and there are no negative roots visible
B is wrong because the roots -3 and -6 are both negative
You can factor A into (x-2)(x-3) and the roots are 2 and 3 but the roots on the graph look closer to 3 and 6
For C it can be factored as (x-6)(x-3) so the roots are 3 and 6 which look accurate
Answer:
ggggggg
Step-by-step explanation:
Answer:
The regression line having minimum residuals, actual values closest to estimated regression line values : depicts the most reasonable data model.
Step-by-step explanation:
Regression is a statistical tool depicting cause effect relationship between independent variable(s) (X) , dependent variable. (Y)
Population Regression Function is the conditional expectation of Yi, based on given Xi.
E (Yi / Xi ) =
; where Y's value is based on given X values
Sample Regression Function is estimated relationship between Y & X, based on sample study.
y = b0 + b1x1 ; where y is a estimate of Y, b0 & b1 estimates of
.
In estimating through SRF: there are residuals, i.e differences between actual & estimated values. The most reasonable regression model (regression line) is which minimises the residual values, i.e actual values are closest possible to regression estimated values.
For this matter, classical linear regression model uses 'Ordinary Least Squares' regression, which minimises the residual's squared values.