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The accompanying data on cube compressive strength (MPa) of concrete specimens appeared in the article 鈥淓xperimental Study of Recycled Rubber-Filled High-Strength Concrete鈥 (Magazine of Concrete Res., 2009: 549鈥556):

\(\begin{array}{l}112.3 97.0 92.7 86.0 102.0\\99.2 95.8 103.5 89.0 86.7\end{array}\)

a. Is it plausible that the compressive strength for this type of concrete is normally distributed?

b. Suppose the concrete will be used for a particular application unless there is strong evidence that true average strength is less than \(100MPa\). Should the concrete be used? Carry out a test of appropriate hypotheses.

Short Answer

Expert verified

(a) It is plausible.

(b) The concrete can be used.

Step by step solution

01

Define p-value in hypothesis testing.

The null hypothesis states that the population mean is equal to the value mentioned in the claim. If the null hypothesis is the claim, then the alternative hypothesis states the opposite of the null hypothesis.

\(\begin{array}{l}{H_0}:\mu = 0\\{H_a}:\mu \ne 0\end{array}\)

The formula for the value of the test statistic is given by, \(t = \frac{{\bar x - {\mu _0}}}{{s/\sqrt n }}\).

02

Assumption for test the appropriate hypothesis.

(a)

NORMAL PROBABILITY PLOT:

The horizontal axis shows the data values, while the vertical axis shows the standardised normal scores.

The standardised normal scores are the z-scores in the normal probability table in the appendix corresponding to an area of \(\frac{{j - 0.5}}{n}\) (or the closest area) with \(j \in \{ 1,2,3,...,n\} \) if the data comprises n data values. The least standardised score correlates to the smallest data value, and so on.

The population distribution is essentially normal if the pattern in the normal probability plot is roughly linear and does not contain strong curvature.

Because the normal probability plot has little curvature and is essentially linear, the compressive strength of this type of concrete is likely to be normally distributed.

03

Test the appropriate hypothesis.

(b)

The mean is the ration of sum of all values and the total number of values.

\(\begin{aligned}{c}\bar x &= \frac{{112.3 + 97 + 92.7 + ... + 103.5 + 89 + 86.7}}{{10}}\\ &= \frac{{964.2}}{{10}}\\ &\approx 96.42\end{aligned}\)

The square of the variance is the standard deviation.

\(\begin{aligned}{c}s &= \sqrt {\frac{{{{(112.3 - 96.42)}^2} + \ldots . + {{(86.7 - 96.42)}^2}}}{{10 - 1}}} \\ &\approx 8.2586\end{aligned}\)

Let the given be:

\(\begin{array}{l}n = 10\\\alpha = 0.05\end{array}\)

Claim that the true average strength is less than\(100MPa\).

The value of the test statistic:

\(\begin{aligned}{c}t &= \frac{{\bar x - {\mu _0}}}{{s/\sqrt n }}\\ &= \frac{{96.42 - 100}}{{8.2586/\sqrt {10} }}\\ &\approx - 1.371\end{aligned}\)

The P-value is the chance of getting the test statistic's result, or a number that is more severe. The P-value is the number (or interval) in the column header of the T table in the appendix that contains the t-value in the row\(\begin{array}{c}df = n - 1\\ = 10 - 1\\ = 9\end{array}\)for the student.

\(P > 0.10\)

As the P-value is smaller than the significance level, so the null hypothesis is rejected.

\(P > 0.05 \Rightarrow Fail to Reject {H_0}\)

The true average strength is less than \(100MPa\) is supported by appropriate evidence. Hence, the concrete can be used.

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