Statistical complications please

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In summary, a roadway construction process uses a machine that pours concrete and measures its thickness to ensure it meets the required depth of 23 inches. Due to the machine's imperfection and high operating cost, the company sets the machine to produce an average thickness of 26 inches, with a standard deviation of 1.75 inches. To determine the percentage of concrete that is under the minimum depth, we need to calculate the probability of the random variable X being less than 23. To achieve the company's goal of having no more than 3% of output under the limit, they need to reduce the standard deviation to a value that satisfies P(Z ≤ z) = 0.03, where z is the number from the standard normal
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Prayerofhope
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A roadway construction process uses a machine that pours concrete onto the roadway and measures the thinckness of the concrete so the roadway will measure up to the required depth in inches. The concrete thickness needs to be consistent across the road, but the machine isn't perfect and it is costly to operate. Since there's a safety hazard if the roadway is thinner than the minimum 23 inches thickness, the company sets the machine to average 26 inches for the batches of concrete. They believe the thickness level of the machine's concrete output can be decribed by a normal model with standard deviation 1.75 inches. [show work]

a) What percent of the concrete roadway is under the minimum depth ?


b) The company's lawyers insist that no more than 3% of the output be under the limit. Because of the expense of operating the machine, they cannot afford to reset the mean to a higher value. Instead they will try to reduce the standard deviation to achieve the "only 3% under" goal. What SD must they attain?


c) Explain what achieving a smaller standard deviation means in this context.
 
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a) You have a random variable X that is normally distributed with mean 26 and standard deviation 1.75, and you want [tex] P(X < 23) [/tex]. If you have a computer program for these use it, otherwise get Z-scores and use the table.

b) Can you find the number from the standard normal distribution that satisfies [tex] P(Z \le z) = 0.03[/tex]? If so then, with
[tex] \sigma [/tex] as the new but unknown standard deviation, solve

[tex]
\frac{23-26}{\sigma} = z
[/tex]

for [tex] \sigma [/tex].

c) What does standard deviation indicate when you deal with measured quantities?
 

1. What is statistical complication?

Statistical complication refers to any challenges or issues that arise during the process of analyzing data using statistical methods. These complications can include issues with data collection, missing or incomplete data, outliers, and other factors that can affect the accuracy and validity of statistical analyses.

2. How do statistical complications affect research results?

Statistical complications can have a significant impact on research results. They can lead to biased or misleading conclusions, affect the generalizability of findings, and undermine the overall quality of the research. It is important for scientists to address and account for these complications in order to ensure the accuracy and reliability of their results.

3. What are some common statistical complications?

Some common statistical complications include non-normality of data, multicollinearity, and heteroscedasticity. Other complications can include measurement error, confounding variables, and sample size limitations. It is important for scientists to be aware of these potential issues and to take steps to address them in their analyses.

4. How can statistical complications be avoided?

While some statistical complications may be inevitable, there are steps scientists can take to minimize their impact. This can include careful planning and design of studies, proper data collection and cleaning procedures, and using appropriate statistical methods and techniques. It is also important for researchers to be transparent and report any potential complications or limitations in their findings.

5. Can statistical complications be overcome?

Yes, statistical complications can often be overcome with proper data analysis techniques and strategies. This may include using alternative statistical methods, implementing data transformations, or removing outliers from the dataset. In some cases, it may also be necessary to collect additional data or adjust the research design in order to address the complications and obtain more accurate results.

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