Answer:
see below
Step-by-step explanation:
You can determine the correct function by looking at the function and graph values at x = 1.
For some constant k, the function is ...
(g·h)(x) = g(x)·h(x) = (-3^x)(kx) = -kx·3^x
For x=1, the graph shows (g·h)(1) = 6. Using this in our expression for (g·h)(x), we have ...
(g·h)(1) = 6
-k(1)(3^1) = 6 . . . . use the expression for (g·h), filling in x=1
k = -2 . . . . . . . . . divide by -3
The function h(x) is ...
h(x) = -2x
Answer:
Step-by-step explanation
We have given that equation of line a+4b=0
⇒4b= -a
⇒
slope of line =
⇒ is negative therefore it lies into 4th quadrant
By, Pythagoras theorem
by putting value of P= -1 & B=4
the value of
with the given condition
i.e. ∈ (3π/2, 2π)
now,
Answer:
a)
b)
c)
Step-by-step explanation:
Previous concepts
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".
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".
The complement rule is a theorem that provides a connection between the probability of an event and the probability of the complement of the event. Lat A the event of interest and A' the complement. The rule is defined by:
Let X the random variable that represent the variable of interest on this case, and for this case we know the distribution for X is given by:
And let represent the sample mean, by the central limit theorem, the distribution for the sample mean is given by:
a. What are the mean and variance of the sampling distribution for the sample means?
b. What is the probability that x overbarxgreater than>101
First we can to find the z score for the value of 101. And in order to do this we need to apply the formula for the z score given by:
If we apply this formula to our probability we got this:
And we want to find this probability:
On this last step we use the complement rule.
c. What is the probability that x bar 98less than
First we can to find the z score for the value of 98.
And we want to find this probability: