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In Exercise \(11\) suppose \({\overline x _{3.}} = 427.5.\) now which true average spreading rates differ significantly from one another? Be sure to use the method of underscoring to illustrate your conclusion, and write a paragraph summarizing your results.

Short Answer

Expert verified

illustrate your conclusion, and write a paragraph summarizing your results.

The T Method for Identifying Significantly Different 渭i 鈥檚

Find value \({Q_{\alpha ,}}I,{I_{\left( {j - 1} \right)}}\)at the Table A.10. in the appendix of the book for given \(\alpha \).

The conclusion is that there are no significant differences between pairs brand \(3\) and\(1;1\) and \(4,2\) and\(5;\) however there is a significant differences between brands\(3\,\,and\,\,4;\) between brand\(2\) and brands\(3,1\) and \(4;\) between \(5\) and brand \(3,1\) and\(4\).

Step by step solution

01

definition of mean

Two are more numbers of mathematical average called mean.

The T Method for Identifying Significantly Different 渭i 鈥檚

Find value \({Q_{\alpha ,}}I,{I_{\left( {j - 1} \right)}}\)at the Table A.10. in the appendix of the book for given \(\alpha \).

Compute and list the sample means in increasing order. Calculate

\(w = {Q_{\alpha ,}}I,{I_{\left( {j - 1} \right)}}.\sqrt {\frac{{MSE}}{J}} \)

and underline pairs of the sample means that differ by less than W . The pair of sample which are not underscored by the same line corresponding of population or treatment means that they are significantly different.

From the mentioned table, and\(\alpha = 0.05\)

\(\begin{aligned}{l}{Q_{\alpha ,}}I,{I_{\left( {j - 1} \right)}} = {Q_{0.5,5,15,}}\\ = 4.37\end{aligned}\)

The value of

\(MSE = 278.8\)

Compute the w value as

\(w = {Q_{\alpha ,}}I,{I_{\left( {j - 1} \right)}}.\sqrt {\frac{{MSE}}{J}} = 4.37..\sqrt {\frac{{272.8}}{4}} = 36.09\)

First order the sample means

\({\overline x _{3.}} < {\overline x _{1.}}\,\,\, < {\overline x _{4.}}\,\, < {\overline x _{2.}} < {\overline x _{5.}}\)

The following table

Brand, i

Sample mean

\({\overline x _{i.}} - {\overline x _{3.}}\)

\({\overline x _{i.}} - {\overline x _{1.}}\)

\({\overline x _{i.}} - {\overline x _{4.}}\)

\({\overline x _{i.}} - {\overline x _{2.}}\)

\(3\)

\(427.5\)

\(1\)

\(462\)

\(34.5\)\(41.8\)

\(4\)

\(469.3\)

\(41.8\)

\(7.3\)

\(2\)

\(515.8\)

\(88.3\)

\(53.8\)

\(46.5\)

\(5\)

\(532.1\)

\(104.6\)

\(70.1\)

\(62.8\)

\(16.3\)

The bold values are smaller than w

Start with mean third sample

\({\overline x _{1.}} - {\overline x _{3.}} = 462 - 427.5 = 34.5 < w = 36.09,\)

Indicated that pair \(\left( {3,1} \right)\)underline as pair:

\(\begin{aligned}{l}\,\,\,\,{\overline x _{3.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{1.}}\\\underline {\,\,\,427.5\,\,\,\,\,\,\,462\,\,\,\,\,\,\,\,} \,\end{aligned}\)

Do the same for all pairs

\({\overline x _{4.}} - {\overline x _{3.}} = \,469.3 - 427.5 = 41.8 > w = 36.09\)

So the pair \(\left( {4,3} \right)\) underline as a pair:

\({\overline x _{4.}} - {\overline x _{1.}} = \,469.3 - 462 = 7.3 < w = 36.09\)

SO The pair \(\left( {1,4} \right)\) underline together

\(\begin{aligned}{l}\,\,{\overline x _{1.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{4.}}\\\underline {\,\,\,462\,\,\,\,\,\,\,469.3\,\,\,\,\,\,\,\,} \end{aligned}\)

Next pair is \(\left( {1,2} \right)\)

\({\overline x _{2.}} - {\overline x _{1.}} = 515.8 - 462 = 53.8 > w = 36.09\)

The pair\(\left( {1,2} \right)\) should not be underline together.

02

first difference

Look at the first difference

\({\overline x _{2.}} - {\overline x _{4.}} = 525.8 - 469.3 = 46.5 > w = 36.09\)

So the pair \(\left( {4,2} \right)\) underline together

\({\overline x _{5.}} - {\overline x _{2.}} = 532.1 - 515. = 16.3 < w = 36.09\)

So the pair \(\left( {2,5} \right)\) underline together

\(\begin{aligned}{l}\,\,{\overline x _{2.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{5.}}\\\underline {\,\,\,515.8\,\,\,\,\,\,\,532.1\,\,\,\,\,\,\,\,} \end{aligned}\)

The connection can be represented

\(\begin{aligned}{l}\,\,{\overline x _{3.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{1.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{4.}}\,\,\,\,\,\,\,\,\,\,\\\underline {\,\,\,437.5\,\,\,\,\,\,\,462.0\,\,\,\,\,\,\,\,\,\,\,\,469.3\,\,\,\,\,\,\,\,} \end{aligned}\)\(\begin{aligned}{l}\,\,{\overline x _{2.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{5.}}\\\underline {\,\,\,515.8\,\,\,\,\,\,\,532.1\,\,\,\,\,\,\,\,} \end{aligned}\)

Hence,

\(\begin{aligned}{l}\,\,{\overline x _{3.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{1.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{4.}}\,\,\,\,\,\,\,\,\,\,\\\underline {\,\,\,437.5\,\,\,\,\,\,\,462.0\,\,\,\,\,\,\,\,\,\,\,\,469.3\,\,\,\,\,\,\,\,} \end{aligned}\)\(\begin{aligned}{l}\,\,{\overline x _{2.}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,{\overline x _{5.}}\\\underline {\,\,\,515.8\,\,\,\,\,\,\,532.1\,\,\,\,\,\,\,\,} \end{aligned}\)

The conclusion is that there are no significant differences between pairs brand \(3\) and\(1;1\) and \(4,2\) and\(5;\) however there is a significant differences between brands\(3\,\,and\,\,4;\) between brand\(2\) and brands\(3,1\) and \(4;\) between \(5\) and brand \(3,1\) and\(4\).

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Most popular questions from this chapter

Reconsider the axial stiffness data given in Exercise ANOVA output from Minitab follows:

Analysis of variance for stiffness

Source DF SS MS F p

Length \(4\) \(43993\) \(10998\) \(10.48\) \(0.000\)

Error \(30\) \(31475\) \(1049\)

Total \(34\) \(75468\)

Level \(N\) Mean St Dev

\(4\) \(7\) \(333.21\) \(36.59\)

\(6\) \(7\) \(368.06\) \(28.57\)

\(8\) \(7\) \(375.13\) \(20.83\)

\(10\) \(7\) \(407.36\) \(44.51\)

\(12\) \(7\) \(437.17\) \(26.00\)

Pooled st Dev \( = 32.39\)

Tukey鈥檚 pairwise comaparisons

Family error rate \( = 0.0500\)

Individual error rate\( = 0.00693\)

Critical value\( = 4.10\)

Intervals for (column level mean)-(row level mean)

\(4\) \(6\) \(8\) \(10\)

\(6\) \(\begin{aligned}{l} - 85.0\\15.4\end{aligned}\)

\(8\) \(\begin{aligned}{l} - 92.1\\\,\,\,\,8.3\end{aligned}\) \(\begin{aligned}{l} - 57.3\\43.1\end{aligned}\)

\(10\) \(\begin{aligned}{l} - 124.3\\\, - 23.9\end{aligned}\) \(\begin{aligned}{l} - 89.5\\10.9\end{aligned}\) \(\begin{aligned}{l} - 82.4\\18.0\end{aligned}\)

\(12\) \(\begin{aligned}{l}\, - 15.2\\\, - 53.8\end{aligned}\) \(\begin{aligned}{l} - 119.3\\ - 18.9\end{aligned}\) \(\begin{aligned}{l} - 112.2\\ - 11.8\end{aligned}\) \(\begin{aligned}{l} - 80.0\\20.4\end{aligned}\)

  1. Is it plausible that the variances of the five axial stiffness index distributions are identical?Explain

b. Use the output(without references to our F table)to test the relevant hypotheses.

c.Use the Tukey intervals given in the output to determine which means differ,and construct the corresponding underscoring pattern.

Suppose that \({X_{ij}}\) is a binomial variable with parameters n and \({P_i}\) (so approximately normal when \(n{p_i} \ge 10\)and \(n{q_i} \ge 10\)).Then since \({\mu _i} = n{p_i}\), \(V({X_{ij}}) = \sigma _i^2 = n{p_i}(1 - {p_i}) = {\mu _i}(1 - {\mu _i}/n)\).How should the \({X_{ij}}\)鈥檚 be transformed so as to stabilize the variance?(Hint: \(g({\mu _i}) = {\mu _i}(1 - {\mu _i}/n)).\)

A study of the properties of mental plate 鈥揷onnected trusses used for roof support (鈥淢odeling joints made with Light-Gauge metal connector plates,鈥 Forest products J., 1979:39-44) yielded the following observations on axial-stiffness index(kips/in.) for plate lengths \(4,6,8.10,12\)in:

\(\begin{aligned}{l}4:309.2\\6:402.1\\8:392.4\\10:346.7\\12:407.4\end{aligned}\) \(\begin{aligned}{l}409.5\\347.2\\366.2\\452.9\\441.8\end{aligned}\) \(\begin{aligned}{l}311.0\\361.0\\351.0\\461.4\\419.9\end{aligned}\) \(\begin{aligned}{l}326.5\\404.5\\357.1\\433.1\\410.7\end{aligned}\) \(\begin{aligned}{l}316.8\\331.0\\409.9\\410.6\\473.4\end{aligned}\) \(\begin{aligned}{l}349.8\\348.9\\367.3\\384.2\\441.2\end{aligned}\) \(\begin{aligned}{l}309.7\\381.7\\382.0\\362.6\\465.8\end{aligned}\)

Does variation in plate length have any effect on true average axial stiffness? state and test the relevant hypotheses using analysis of variance with Display your results in an ANOVA table.(Hint : \({\sum x ^2}_{ij.} = 5,241,420.79.)\)

Four types of mortars-ordinary cement mortar(OCM), polymer impregnated mortar(PIM), resin mortar(RM), and polymer cement mortar(PCM)- were subjected to a compression test to measure strength (MPa). Three strength observations for each mortar type are given in the article 鈥淧olymer Mortar Composite Matrices For Maintance- Free Highly Durable Ferrocement鈥漚nd are reproduced here. Construct an ANOVA table. Using a \(.05\)significance level , determine whether the data suggests that the true mean strength is not the same for all the four mortar types. If you determine that the true mean strengths are not all equal, use Turkey鈥檚 method to identify the significant differences.

\(\begin{aligned}{*{20}{c}}{OCM}&{32.15}&{35.53}&{34.20}\\{PIM}&{126.32}&{126.80}&{134.79}\\{RM}&{117.91}&{115.02}&{114.58}\\{PCM}&{29.09}&{30.87}&{29.80}\end{aligned}\)

Repeat Exercise supposing that \(\,\,{\overline x _{2.}} = 502.8\)in addition to\(\,\,{\overline x _{3.}} = 427.5.\)

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