Answer:64
Step-by-step explanation:
2*2*2*2=4*4=16. 16*16*16=4,096. 2*2*2*2*2*2=4*4*4=64. 4,096/64=64.
Answer:
Step-by-step explanation:
Answer:
(32, 31) (33,30) (34, 29) (35, 28) (36,27) (37, 26)
Step-by-step explanation:
The pairs of whole numbers that add to 63 and have a difference less than 10 are (27, 36), (28, 35), (29, 34), (30, 33), and (31, 32).
This problem is based in the domain of basic algebra. Essentially, you are being asked to find pairs of whole numbers that, added together, equal 63, with the condition that the difference between these two numbers is less than 10.
Starting from the number 32 (as any larger number plus any number greater than 0 would exceed 63), you can begin to list pairs, subtracting one number from the total of 63 while simultaneously adding that same amount to the other half of the pair. This will ensure that the sum always equals 63.
Here are the pairs satisfying the given conditions: (27, 36), (28, 35), (29, 34), (30, 33), (31, 32). For these pairs, the difference between the two numbers in each pair is less than 10.
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What do I need to d
Answer: 219,185
-Multiply the place values to find the answer,
Answer:
r < 3/2 or r < 1.5
Step-by-step explanation:
-8r - 9 > 21
-8r > -21 +9
-8r > -12
divide both sides by -8
= r < 3/2 or 1.5
The value of x for the given equation is given as x = 50.
A linear equation can be solved by equating the LHS and RHS of the equation following some basic rules such as by adding or subtracting the same numbers on both sides and similarly, doing division and multiplication with the same numbers.
The given equation is as below,
3x = 2x + 50
It can be solved as follows,
3x = 2x + 50
=> 3x - 2x = 50
=> x = 50
Hence, the solution of the given linear equation is x = 50.
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this is the answer to your question x=50
Step-by-step explanation:
3x-2x=50
1x=50
Answer:
a)The data shows a downward trend pattern
Step-by-step explanation:
See attached images
In simple linear regression, the parameters for the line that minimizes the MSE can be found. To determine if a regression model suggests a goal is being met, compare predicted values to the goal value.
In simple linear regression analysis, the parameters for the line that minimizes the Mean Squared Error (MSE) can be found. The y-intercept (b0) and slope (b1) of the line can be calculated using the data. To determine if the regression model suggests that a goal is being met, you would compare the predicted values from the regression model to the goal value. If the predicted values are less than or equal to the goal value, then the goal is being met.
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