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grandymaker [24]
2 years ago
14

Question 6 2 pts

Mathematics
1 answer:
astraxan [27]2 years ago
5 0

Answer:

785 acres

Step-by-step explanation:

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".  

Let X represent the random variable amount of corn yield

Assuming the X follows a normal distribution  

X \sim N(\mu, \sigma)  

And we know that the average \bar X is dsitributed:

\bar X \sim N(\mu=189.3, \sigma_{\bar X}=23.5)

And we are interested on this probability:

P(\bar X>180)

For this case we can use the z score formula given by:

z=\frac{\bar X -\mu}{\sigma_{\bar X}}

If we apply this we got:

P(\bar X>180)=P(Z>\frac{180-189.3}{23.5})=P(Z>-0.396)=1-P(Z

And since we have a proportion estimated and we hava a total of 1200 acres the expected to yield more than 180 bushels of corn per acre would be:

r=1200*0.654=784.8

And if we round up this amount we got \approx 785 and that would be the best option for this case.

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oksano4ka [1.4K]

Answer:

a) For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assumin Normal distribution

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical dsitribution

The result is on the figure attached.

b) For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

Step-by-step explanation:

For this case we have the following data:

28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4

The quantile-quantile or q-q plot is a graphical procedure in order to check the validity of a distributional assumption for a data set. We just need to calculate "the theoretically expected value for each data point based on the distribution in question".

If the values are asusted to the assumed distribution, we will see that "the points on the q-q plot will fall approximately on a straight line"

For this case our distribution assumed is normal.

Part a

For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assuming Normal distribution (0,1)

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical distribution

The result is on the figure attached.

Part b

For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

4 0
3 years ago
Solve 3x + 2 = 15 for x using the change of base formula log base b of y equals log y over log b. −1.594 0.465 2.406 4.465
disa [49]

<u>Answer:</u>

The value in 3x + 2 = 15 for x using the change of base formula is 0.465 approximately and second option is correct one.

<u>Solution:</u>

Given, expression is 3^{(x+2)}=15

We have to solve the above expression using change of base formula which is given as

\log _{b} a=\frac{\log a}{\log b}

Now, let us first apply logarithm for the given expression.

Then given expression turns into as, x+2=\log _{3} 15

By using change of base formula,

x+2=\frac{\log _{10} 15}{\log _{10} 3}

x + 2 = 2.4649

x = 2.4649 – 2  = 0.4649

Hence, the value of x is 0.465 approximately and second option is correct one.

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