Step-by-step explanation:
A,true
doesn't interfere with any function restriction
Answer:
The correct answer is - 3, 4, and 5.
Step-by-step explanation:
Given:
distance = 5 miles
time to cover 5 miles = 10 minutes
If his claim is true then,
additional Distance = 15 miles
total distance will be = 15+5 = 20 miles
additional time = 30 minutes
total time = 10+30 = 40 minutes
Solution:
The speed of the vehicle can be calculated by the formula:
V = d/t
where V is speed or velocity
d = distance
t = time
Putting the final or total values in formula =
V = 20/40
= 1/2 (5th statement is true)
In an hour where 60 minutes are there, 1/2 = 30 miles per hour.
Thus, 3rd and 4th statement are true.
Is RS perpendicular to DF? Select Yes or No for each statement. R (6, −2), S (−1, 8), D (−1, 11), and F (11 ,4) R (1, 3), S (4,7
guajiro [1.7K]
I'll do the first one to get you started.
Find the slope of the line between R (6,-2) and S (-1,8) to get
m = (y2-y1)/(x2-x1)
m = (8-(-2))/(-1-6)
m = (8+2)/(-1-6)
m = 10/(-7)
m = -10/7
The slope of line RS is -10/7
Next, we find the slope of line DF
m = (y2 - y1)/(x2 - x1)
m = (4-11)/(11-(-1))
m = (4-11)/(11+1)
m = -7/12
From here, we multiply the two slope values
(slope of RS)*(slope of DF) = (-10/7)*(-7/12)
(slope of RS)*(slope of DF) = (-10*(-7))/(7*12)
(slope of RS)*(slope of DF) = 10/12
(slope of RS)*(slope of DF) = 5/6
Because the result is not -1, this means we do not have perpendicular lines here. Any pair of perpendicular lines always has their slopes multiply to -1. This is assuming neither line is vertical.
I'll let you do the two other ones. Let me know what you get so I can check your work.
Answer:
A. No, the student is not right. The central limit theorem says nothing about the histogram of the sample values. It deals only with the distribution of the sample means.
Step-by-step explanation:
No, the student is not right. The central limit theorem says nothing about the histogram of the sample values. It deals only with the distribution of the sample means. The central limit theorem says that if we take a large sample (i.e., a sample of size n > 30) of any distribution with finite mean 
and standard deviation 
, then, the sample average is approximately normally distributed with mean and variance 
.
