Step-by-step explanation:
i hope you will get the answer
Hello!
First we will solve the absolute value, giving us 6+8-18. Then we combine giving us 14-18. This gives us -4.
The answer is B) -4
Hope this helped!
Answer:
Step-by-step explanation:
First, the acceleration of gravity is -9.8m/s^2. This still works out since the formula uses 1/2 of it.
Hopefully you see where the other parts come from. Anyway, standard form of a quadratic is ax^2+bx+c=0. So this is almost there. You just need to subtract that 2.1 from both sides.
-4.9t^2+7.5t+-.3=0
Now with this, since it has taken into account the height that the ball was caught with that 2.1, you just need to find the 0s, which is what the quadratic equation does.
The quadratic equation is (-b±sqrt(b^2-4ac))/(2a) and we have a = -4.9, b = 7.5 and c = -.3. Remember you want to keep the signs. Now we just plug in.
(-b±sqrt(b^2-4ac))/(2a)
(-7.5±sqrt((-7.5)^2-4*-4.9*-.3))/(2*-4.9)
(-7.5±sqrt(56.25-5.88))/(.9.8)
(-7.5±sqrt(50.37))/(-9.8)
(-7.5±7.097)/(-9.8)
The plus or minus means there are two equations.
(-7.5+7.097)/(-9.8) and (-7.5-7.097)/(-9.8) So we will solve for both of these.
.04112 and 1.4895. That means these two times are when the ball is at 2.1 meters. One time on the way up and one time on the way down. We can safely assume that the other player catches the ball on the way down, so we want to use the second time, so 1.4895 seconds.
Answer:
C. The degree of dependence among the observation is negligible.
Step-by-step explanation:
The conditions for performing a t-procedure when constructing a t-interval are;
1) The samples observation should be independent of each other
2) The use of a random sample or experiment for the procedure
3) Ensure the normality of the data of the dependent variable
Given that surveys usually involve sampling without replacement from a population which is finite can not be taken as independent Bernoulli trials however, it is allowable to still consider samples independent when they are less than 10% the size of population
Therefore, the reason for the condition is to ensure that the population to sample size ratio is large enough and that the degree of dependence among the observation is negligible.
