Answer:
The correct answer is 15 cm.
Step-by-step explanation:
Let the width of the required poster be a cm.
We need to have a 6 cm margin at the top and a 4 cm margin at the bottom. Thus total margin combining top and bottom is 10 cm.
Similarly total margin combining both the sides is (4+4=) 8 cm.
So the required printing area of the poster is given by {( a-10 ) × ( a - 8) } 
This area is equal to 125
as per as the given problem.
∴ (a - 10) × (a - 8) = 125
⇒
- 18 a +80 -125 =0
⇒
- 18 a -45 = 0
⇒ (a-15) (a-3) = 0
By law of trichotomy the possible values of a are 15 and 3.
But a=3 is absurd as a
4.
Thus the required answer is 15 cm.
Answer:
Your answer is D. 4/5 this is because of you had a whole pizza, and split it into five pieces, and somone takes 2 slices, there will be 3/5 of that pizza left, if they only took one, there would be 4/5, which is more pizza than a 3/5 of a pizza
Step-by-step explanation:
If 3 is the bold number then the answer is D. <span>#TeamAlvaxic</span>
Answer:
E IS THE CORRECT ANSWER
The R-squared is 0.64 and it means that the dependent value explains 64% of the independent value in the simple regression analysis
Step-by-step explanation:
R-Squared value is a very important indicator in a regression analysis.
What does it measure?
It measures how close to the line of best fit are the data points. How good the fitted line is can be indicated by the value of the r-squared.
The maximum value it can take is 1 and at this value, there is a direct and complete relationship between the independent variable x and the dependent variable y. The value 1 represents an 100% relationship between both parties.
The r-squared has a value of between 0 and 100%. The closer to 100, the better the model while the closer to 100, the more faulty the model is. In fact, a value of 0 indicates no relationship at all between the dependent and the independent variable.
With an R-squared value of 0.64, the regression model works above average to explain that the dependent variable explains 64% of the independent value in the simple regression analysis.