The article "Withdrawal Strength of Threaded Nails" (D. Rammer, S. Winistorfer, and D.Bender, Journal of Structural Engineering, 2001:442–449) describes an experimentcomparing the withdrawal strengths for several types of nails. The data presented in thefollowing table are consistent with means and standard deviations reported in the article forthree types of nails: annularly threaded, helically threaded, and smooth shank. All nails haddiameters within 0.1 mm of each other, and all were driven into the same type of lumber.NailWithdrawal Strength (N/mm)TypeAnnularly 36.57 29.67 43.38 26.94 12.03 21.66 41.79 31.50 35.84 40.81threadedHelically 14.66 24.22 23.83 21.80 27.22 38.25 28.15 36.35 23.89 28.44threadedSmooth12.61 25.71 17.69 24.69 26.48 19.35 28.60 42.17 25.11 19.98shankConstruct an ANOVA table. You may give a range for the P-value.Can you conclude that the mean withdrawal strength is different for different nailtypes?a.b.

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Answered: The article "Withdrawal Strength of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The deflection temperature under load for two different types of plastic pipe is being investigated. Two random samples of 15 pipe specimens are tested, and the deflection temperatures observed are as follows (in oF): Type 1: 206, 188, 205, 187, 194, 193, 207, 185, 189, 213, 192, 210, 194, 178, 205. Type 2: 177, 197, 206, 201, 180, 176, 185, 200, 197, 192, 198, 188, 189, 203, 192. 1. Determine the coefficient of dispersion of each type of plastic pipe. 2. Interpret the results. Compare the SDUS of the 10th observation in each type. What conclusion can you make?
a) which for these data is 32.1470, 42.3520, or 10.0674 c) that minimizes the error sum of squares, total sum of squares, or regression sum of squares…for which these data is 32.1470, 42.3520, or 10.0674
Given the linear correlation coefficientr and the sample size n, determine the critical values of r and use your finding to state whether or not the given r represents a significant linear correlation. Use a significance level of 0.05. r = 0.353, n = 15 O A) Critical values: r = +0.532, no significant linear correlation B) Critical values: r = 0.514, no significant linear correlation C) Critical values: r = ±0.514, significant linear correlation D) Critical values: r = ±0.514, no significant linear correlation
3
е. Sample region 6. Question: In a hypothesis test, what does the values of T(0) signify? a. Smallest level of significance for rejection of Null Hypothesis. b. Largest level of significance for rejection of Null Hypothesis. c. Smallest level of significance for acceptance of Null Hypothesis d. Largest level of significance for acceptance of Null Hypothesis 3
Three different methods for assembling a product were proposed by an industrial engineer. To investigate the number of units assembled correctly with each method, 30 employees were randomly selected and randomly assigned to the three proposed methods in such a way that each method was used by 10 workers. The number of units assembled correctly was recorded, and the analysis of variance procedure was applied to the resulting data set. The following results were obtained: SST =10,870; SSTR =4580. a. Set up the ANOVA table for this problem (to 2 decimals but p-value to 4 decimals, if necessary). Source of Variation Sum of Squares Degrees of Freedom Mean Square F p-value Treatments 4580 Error 6290 Total 10870 b.Use a = 0.05 to test for any significant difference in the means for the three assembly methods. Calculate the value of the test statistic (to 2 decimals). The p-value is - Select your answer - What is your conclusion? Select your answer -
Consider the following 29 years worth of observed annual rainfall data (in inches) for a certain weather station shown. Treat the data as samples only. (Note: The data are arranged in chronological manner.) 43.30 53.02 63.52 45.93 48.26 50.51 49.57 43.93 46.77 59.12 54.49 47.38 40.78 45.05 50.37 54.91 51.28 40.01 53.29 67.59 58.71 42.96 55.77 41.31 58.83 48.21 44.67 67.72 43.11 (a) Make a time-series line graph showing the trend of observed rainfall data through time. (For year, just denote it as Year 1, Year 2 etc.). Draw the line graph by hand. (b) Make a grouped data representing the given rainfall data. Use the following class intervals: 40<=X<45, 45<=X<50, 50<=X<55, 55<=X<60, 60<=X<65, and 65<=X<70.
The milk yield from two breeds of goats, A and B, is categorised as low, medium or high. One hundred goats in a random sample are classified according to breed and milk yield. The results are summarised in the table below. i. Explain the difference between a test of independence and a test of homogeneity ii. Perform a chi squared test, at the 95% of significance level, to determine whether there is an association between the breed of goats and the milk yield.
A nutritionist in the FDA wants to compare the caloric content of medium French fries sold by Wendonald (Population 1) and McKing (Population 2) fast-food chains, to see if there is any difference between them. To test this, random samples from each chain is taken and the caloric contents of French fries are measured. The finding is summarized in the table on next page. Conduct an independent-samples t test to test whether there is any difference between the caloric content of French fries sold by the two chains, following the steps below. Wendonald McKing = 32 = 30 = 390 calories = 365 calories = 30 calories = 38 calories Write down the null and alternative hypotheses using proper notation Calculate (Write all steps) What is the value of for α = 0.05 with a two-tailed test?
Is there any significant difference among the treatment means (Plot)? Test at alpha = 0.01. i) State the null hypothesis j) State the alternative hypothesis k) F distribution value for Decision rule?
To test for any significant difference in the number of hours between breakdowns for four machines, the following data were obtained. Machine Machine 2 1 6.4 7.9 5.3 7.6 8.4 7.6 8.6 7.3 Ha: M₁ M₂ # 9.4 10.2 9.3 9.8 Ho: M₁ = M₂ = M3 = μ4 M3 ‡ M4 # Ho: M₁ M₂ # M3 ‡ M4 На: M1 = M2 = M3=Ма Machine 3 10.8 Ho: M₁ = M₂ = M3 = M4 Ha: 10.1 9.5 10.2 8.9 8.7 Machine 4 9.7 12.8 12.1 (a) At the α = 0.05 level of significance, what is the difference, if any, in the population mean times among the four machines? State the null and alternative hypotheses. 10.7 11.1 11.4 Ho: At least two of the population means are equal. H₂: At least two of the population means are different. Not all the population means are equal. Ho: Not all the population means are equal. На: M1 = M2 = M3 = M4

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