Answer:
in point from: (-4,0) in equation from: x=-4,y=0
Step-by-step explanation:
Answer: x = ¹/₂ ± √⁸¹
------------
2
Step-by-step explanation:
First write out the equation
x² - x - 20
Now we now write the equation by equating to 0
x² - x - 20 = 0
We now move 20 to the other side of the equation. So
x² - x = 20,
We now add to both side of the equation square of the half the coefficient of the (x) and not (x²) which is (1) . So, the equation now becomes
x² - x + ( ¹/₂ )² = 20 + ( ¹/₂ )²
x² - ( ¹/₂ )² = 20 + ¹/₄
( x - ¹/₂ )² = 20 + ¹/₄, we now resolve the right hand side expression into fraction
( x - ¹/₂ )² = ⁸¹/₄ when the LCM is made 4
Taking the square root of both side to remove the square,we now have
x - ¹/₂ = √⁸¹/₄
x - ¹/₂ = √⁸¹/₂
Therefore,
x = ¹/₂ ± √⁸¹
-----------
2
Answer:
Step-by-step explanation:
Given that a dairy scientist is testing a new feed additive. She chooses 13 cows at random from a large population of cows. She randomly assigns nold = 8 to get the old diet, and nnew = 5 to get the new diet including the additive.
From the data given we get the following
N Mean StDev SE Mean
Sample 1 8 43 5.1824 1.832
Sample 2 5 73 21.0832 9.429
df = 11
Std dev for difference = 13.3689
a) Yes the two are independent. The two sets of cows randomly chosen are definitely independent. Paired means equal number should be there and homogeneous conditions should be maintained.
b) Enclosed
c) Comparison of two means is the test recommended here. Because independent samples are used.\
d) Test statistic= -3.1233
(because of unequal variances we use that method)
95% confidence interval = ( -56.6676 , -3.3324 )
p value <0.05 our alpha
So reject null hypothesis.
The two means are statistically significantly different.
Answer:
a) 

b) From the central limit theorem we know that the distribution for the sample mean
is given by:
c)
Step-by-step explanation:
Let X the random variable the represent the scores for the test analyzed. We know that:

And we select a sample size of 64.
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Part a
For this case the mean and standard error for the sample mean would be given by:


Part b
From the central limit theorem we know that the distribution for the sample mean
is given by:
Part c
For this case we want this probability:

And we can use the z score defined as:

And using this we got:
And using a calculator, excel or the normal standard table we have that: