2.88 Characteristics of a rockfall. Refer to the EnvironmentalGeology (Vol. 58, 2009) study of how far a block fromROCKS a collapsing rock wall will bounce, Exercise 2.61 (p. 61).The rebound lengths (meters) for a sample of 13 rockbounces are reproduced in the table below.10.94 13.71 11.38 7.26178311.9211.875.44 13.354.90 5.855.106.77Based on Paronuzzi, P."Rockfall-induced block propagation on a soilslope, northern Italy." Environmental Geology, Vol. 58, 2009 (Table 2).a. Compute the range of the 13 rebound lengths. Give theunits of measurement of the range.b. Compute the variance of the 13 rebound lengths. Givethe units of measurement of the variance.c. Compute the standard deviation of the 13 reboundlengths. Give the units of measurement of the standarddeviation.

For the given data Find Range = ? Variance = ? Standard deviation = ?

Answered: 2.88 Characteristics of a rockfall.…

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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Gender Alcohol Exercise SQI GPA Male Yes 9 15.5 3.85 Male Yes 11 11.0 5.46 Female No 5 11.1 3.28 Female No 6 13.9 2.37 Female Yes 8 9.9 4.63 Male Yes 11 14.5 4.58 Male Yes 9 10.6 4.96 Male Yes 11 15.1 1.02 Female No 8 12.4 4.33 Female Yes 10 11.5 3.96 Male No 8 10.6 5.83 Male Yes 7 11.5 6.12 Male Yes 10 10.8 2.20 Male Yes 11 12.5 2.93 Male Yes 9 16.4 4.52 Female Yes 2 14.1 4.50 Male Yes 8 15.3 1.63 Female No 6 18.0 3.09 Female No 5 9.7 3.92 Male Yes 11 10.5 3.10 Female No 8 13.7 5.42 Male Yes 8 14.9 2.03 Female No 7 10.9 5.90 Male No 10 14.5 3.25 Male Yes 7 11.5 5.21 Female No 9 11.3 3.31 Male Yes 9 11.6 2.51 Male No 10 15.6 3.88 Male Yes…
6:18 ul LTE O MATH 1122 Homework 13.pdf •.. 5 of 6 30 regular and double stuff Oreos were sampled and their filling weighed. Table 1. Example frosting mass data. Filling Masses of Regular Oreos (s) 3.00 2.90 3.20 3.20 3.20 3.12 3.19 3.31 3.39 3.18 3.15 3.15 3.14 3.01 3.01 3.17 3.06 3.16 3.13 3.23 2.94 3.05 3.14 3.30 3.09 3.10 3.11 3.24 3.10 3.15 Filling Masses of Double Stuff Oreos (g) 6.11 6.19 6.27 6.18 6.34 6.21 6.05 6.18 6.34 6.23 6.08 6.04 6.13 6.65 6.20 6.12 6.46 6.37 6.19 6.01 6.21 6.46 6.27 6.25 6.15 6.15 6.35 6.24 6.32 6.03 https://www.amstat.org/ASA/Education/STEW/home.asp The mean of a sample measures the middle of all of the numbers. a) Calculate the mean of each sample. Along with the middle, we would like to measure how spread out the numbers are. The standard deviation measures how spread out the numbers are. A quick and dirty estimation of the standard deviation is one-sixth of the range of the data. That is, get the distance between the min and max and then divide that…
The following data was reported on total Fe for four types of iron formation (1 = carbonate, 2 = silicate, 3 = magnetite, 4 = hematite). 1: 20.6 28.1 27.8 27.0 27.9 25.2 25.3 27.1 20.5 31.2 2: 26.5 24.0 26.2 20.2 23.6 34.0 17.1 26.8 23.7 24.8 3: 30.2 34.0 27.5 29.4 28.3 26.2 29.9 29.5 30.0 35.8 4: 36.6 44.2 34.1 30.3 32.0 33.1 34.1 32.9 36.3 25.2 Carry out an analysis of variance F test at significance level 0.01. State the appropriate hypotheses. H0: ?1 ≠ ?2 ≠ ?3 ≠ ?4Ha: at least two ?i's are equalH0: ?1 = ?2 = ?3 = ?4Ha: all four ?i's are unequal H0: ?1 ≠ ?2 ≠ ?3 ≠ ?4Ha: all four ?i's are equalH0: ?1 = ?2 = ?3 = ?4Ha: at least two ?i's are unequal Summarize the results in an ANOVA table. (Round your answers two decimal places.) Source df Sum ofSquares MeanSquares f Treatments Error Total Give the test statistic. (Round your answer to two decimal places.)
EXAMPLE A civil engineer is interested in determining whether four different methods of estimating flood flow frequency pro- duce equivalent estimates of peak discharge when applied to the same watershed. Each procedure is used six times on the watershed, and the resulting discharge data (in cubic feet per second) are shown Estimation Method Observations 1 0.34 0.12 1.23 0.70 1.75 0.12 2 0.91 2.94 2.14 2.36 2.86 4.55 3 6.31 8.37 9.75 6.09 9.82 7.24 4 17.15 11.82 10.95 17.20 14.35 16.82
An experimental diet to induce weight loss was followed for one week by a randomly selected group of 12 students with the following results. Loss in Pounds Student 1 2 3 5 6 7 8 9 10 11 12 2.5 2.9 0.4 1.7 0.0 1.5 5.5 3.5 3.9 3.5 1.1 2.9 (a) Find a point estimate for the average amount lost after one week on this diet. 2.55 ]x Is this an unbiased estimate of the population mean? Explain. Yes, this an unbiased estimate of the population mean, since E(X) = µ. Yes, this an unbiased estimate of the population mean, since E(X) + μ. O No, this is not an unbiased estimate of the population mean, since E(X) * μ. O No, this is not an unbiased estimate of the population mean, since E(X) = μ. O No, this is not an unbiased estimate of the population mean, since the sample size is too small. (b) Find a point estimate for the variance of the amount lost on this diet. (Round your answer to four decimal places.) 2.3264 x Is this an unbiased estimate of the population variance? Explain. Yes, this an…
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SECTION 5.4 13. Normal cattle The Virginia Cooperative Extension reports that the mean weight of yearling Angus steers is 1152 pounds. Sup- pose that weights of all such animals can be described by a Normal model with a standard deviation of 84 pounds. What percent of steers weigh a) over 1250 pounds? b) under 1200 pounds? c) between 1000 and 1100 pounds?
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Consider the accompanying data on flexural strength (MPa) for concrete beams of a certain type. 5.5 7.2 7.3 6.3 8.1 6.8 7.0 7.1 6.8 6.5 7.0 6.3 7.9 9.0 8.9 8.7 7.8 9.7 7.4 7.7 9.7 7.9 7.7 11.6 11.3 11.8 10.7 The data below give accompanying strength observations for cylinders. 6.5 5.8 7.8 7.1 7.2 9.2 6.6 8.3 7.0 8.4 7.1 8.1 7.4 8.5 8.9 9.8 9.7 14.1 12.6 11.5 Prior to obtaining data, denote the beam strengths by X,, . Xm and the cylinder strengths by Y,, . Y. Suppose that the X,'s constitute a random sample from a distribution with mean u, and standard deviation o, and that the Y's form a random sample (independent of the X's) from another distribution with mean H2 and standard deviation (a) Use rules of expected value to show that X – Y is an unbiased estimator of µ, - µ. E(X – Y) E(X) – E(Y) = H1 - H2 nm E(X - Y) (E) – E(Y)* = l1 - 42 E(X - Y) = nm( E(X) – E(Yn) = H1 - H2 E(X – Y) E(X - Y) = V E(X) – E(Y) = µ1 – H2 E(X) – E(Y) = µ1 - 42 Calculate the estimate for the given data.…
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