check the first picture below.
those are the table of values we get for both, at 1 through 5 seconds.
why are both functions equal between the 4th and 5th second?
h(t) is the distance from the ground, check the second picture below, that's pretty much the graph of h(t) usually.
g(t) is the height of the object from the ground.
whilst g(t) is ever increasing, h(t) goes up, reaches a peak point, the vertex, and then goes back down, on its way down, it drops to 69 and then to 0 at the 5th second, however, between 69 and 0, it dropped first to 31 at some time, and then 30, then 29, then 27, then 25.8 and so on till it got to 0.
one of those values between 69 and 0, will match a value between 25.8 and 31.
what does all that mean in this context?
it means that at some point the baseball was at a height predicted g(t) as well as by h(t).
Answer:
95% confidence interval for the population variance = (1.42 , 2.62).
Step-by-step explanation:
We are given that the weights of 83 randomly selected windshields were found to have a variance of 1.88.
<em>So, firstly the pivotal quantity for 95% confidence interval for the population variance is given by;</em>
P.Q. = 
~ 
where, 
= sample variance = 1.88
= population variance
n = sample of windshields = 83
So, 95% confidence interval for population variance, is;
P(58.85 < 
< 108.9) = 0.95 {As the table of 
at 82 degree of freedom
gives critical values of 58.85 & 108.9}
P(58.85 < < 108.9) = 0.95
P( 
< 
< 
) = 0.95
P( 
< < 
) = 0.95
<em><u>95% confidence interval for</u></em> = ( , )
= ( 
, 
)
= (1.42 , 2.62)
Therefore, 95% confidence interval for the population variance of the weights of all windshields in this factory is (1.42 , 2.62).
10 ...............................
Given the assumption of a normal distribution with a mean of 100 and a standard deviation of 15, a score of 130 represents a z-score of 2. In different mostly older handbooks of descriptive statistics you can find a ‘from z to percentile-table’. Nowadays it’s more easy to use the computer. Using R, a free and open source statistical environment (www.r-project.org), you can use the command pnorm (130, 100, 15) and it will give 0.9772. Because yo want the proportion above that score you use 1-pnorm (130, 100, 15). Another way of writing in R and with for example 3 IQ-scores:
perc = pnorm (c (70, 100, 130), 100, 15)
(1 - perc)
gives you the above-proportion of the IQ-scores of respectively 70, 100 and 130.
Divide both by 2:
2/2= 1
50/2= 25
1:25 would be the ratio, or "A".
I hope this helped!
