Answer:
The answer is "0.0023 and Choice a"
Step-by-step explanation:
In point a:
The central limit theorem:
In point b:
The chance is lower than 0.05. This means that it is very rare to see the average sample above 251.8 if the argument is valid, that is why the means of the sample is far away from the common claimed and casts doubt on the actual mean.
Answer:
1723
Step-by-step explanation:
10 x 70=700
Answer:
t = 9.57
Step-by-step explanation:
We can use trig functions to solve for the t
Recall the 3 main trig ratios
Sin = opposite / hypotenuse
Cos = adjacent / hypotenuse
Tan = opposite / adjacent.
( note hypotenuse = longest side , opposite = side opposite of angle and adjacent = other side )
We are given an angle as well as its opposite side length ( which has a measure of 18 ) and we need to find its adjacent "t"
When dealing with the opposite and adjacent we use trig ratio tan.
Tan = opp / adj
angle measure = 62 , opposite side length = 18 and adjacent = t
Tan(62) = 18/t
we now solve for t
Tan(62) = 18/t
multiply both sides by t
Tan(62)t = 18
divide both sides by tan(62)
t = 18/tan(62)
t = 9.57
And we are done!
Answer:
a. 11
Step-by-step explanation:
expand equation
select like terms
solve for x
Answer:
Kindly check explanation
Step-by-step explanation:
Given that :
Correlation Coefficient (r) = 0.989
alph=0.05
Number of observations (n) = 8
determine if there is a linear correlation between chest size and weight.
Yes, there exists a linear relationship between chest size and weight as the value of the correlation Coefficient exceeds the critical value.
What proportion of the variation in weight can be explained by the linear relationship between weight and chest size?
To determine the the proportion of variation in weight that can be explained by the linear regression line between weight and chest size, we need to obtain the Coefficient of determination(r^2) of the model.
r^2 = square of the correlation Coefficient
r^2 = 0.989^2 = 0.978121
Hence, about 0.978 (97.8%) of the variation in weight can be explained by the linear relationship between weight and chest size.
