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Question: Refer to the Bulletin of Marine Science (April 2010) study of lobster trap placement, Exercise 6.29 (p. 348). Recall that the variable of interest was the average distance separating traps鈥攃alled trap-spacing鈥攄eployed by teams of fishermen. The trap-spacing measurements (in meters) for a sample of seven teams from the Bahia Tortugas (BT) fishing cooperative are repeated in the table. In addition, trap-spacing measurements for eight teams from the Punta Abreojos (PA) fishing cooperative are listed. For this problem, we are interested in comparing the mean trap-spacing measurements of the two fishing cooperatives.

BT Cooperative

93

99

105

94

82

70

86

PA Cooperative

118

94

106

72

90

66

98


Source: Based on G. G. Chester, 鈥淓xplaining Catch Variation Among Baja California Lobster Fishers Through Spatial Analysis of Trap-Placement Decisions,鈥 Bulletin of Marine Science, Vol. 86, No. 2, April 2010 (Table 1).

a. Identify the target parameter for this study.b. Compute a point estimate of the target parameter.c. What is the problem with using the normal (z) statistic to find a confidence interval for the target parameter?d. Find aconfidence interval for the target parameter.e. Use the interval, part d, to make a statement about the difference in mean trap-spacing measurements of the two fishing cooperatives.f. What conditions must be satisfied for the inference, part e, to be valid?

Short Answer

Expert verified

Answer

Measurement is a technique for determining an object's qualities by contrasting it to a standard amount.

Step by step solution

01

(a) Find the target parameter. 

The target parameter will be the population mean and population variance.

02

(b) Compute point estimate. 

For BT Cooperative

n1=7x1=89.91=11.6SEMean=4.4

For PA Cooperative

n2=8x2=99.62=27.4SEMean=9.7

The point estimate of the target parameter

=x1-x2=89.9-99.6=-9.7

03

(c) State the problem using the z statistic.

The sample size is less than , so we cannot use the normal (z) statistic to find a confidence interval for the target parameter.

04

(d) Find confidence interval.

The degree of freedom will be

=n1+n2-2=7+8-2=13

From the t-distribution table, the critical value at 0.10 thelevel of significance for 13 degrees of freedom is 1.7709 .

The pooled standard deviation is sp=n1-1s12+n2-1s22n1+n2-2

=7-111.62+8-127.427+8-2=6062.6813=21.595

The 90% confidence interval for means difference

=x1-x2t/2sp1n1+1n2=89.9-99.61.770921.59517+18=-9.719.79

Thus, the confidence interval for means difference is -29.49to 10.09.

05

(e) Make a statement about the difference in mean trap-spacing.

Based on the confidence interval, we can say that the difference between the mean trap spacing measurements of the BT cooperative and the mean trap spacing measurements of the PA cooperative is between -29.49to 10.09.

06

(f) State the conditions that must be satisfied for the inference.

The conditions to be satisfied for the inference in part (e) are:

  1. Each sample is taken from simple random sampling.
  2. Each sample is independent.
  3. Each sample size is small.
  4. Each sample is approximately normally distributed.

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Predicting software blights. Relate to the Pledge Software Engineering Repository data on 498 modules of software law written in 鈥淐鈥 language for a NASA spacecraft instrument, saved in the train. (See Exercise 3.132, p. 209). Recall that the software law in each module was estimated for blights; 49 were classified as 鈥渢rue鈥 (i.e., the module has imperfect law), and 449 were classified as 鈥渇alse鈥 (i.e., the module has corrected law). Consider these to be Arbitrary independent samples of software law modules. Experimenters prognosticated the disfigurement status of each module using the simple algorithm, 鈥淚f the number of lines of law in the module exceeds 50, prognosticate the module to have a disfigurement.鈥 The accompanying SPSS printout shows the number of modules in each of the two samples that were prognosticated to have blights (PRED_LOC = 鈥測es鈥) and prognosticated to have no blights (PRED_LOC = 鈥渘o鈥). Now, define the delicacy rate of the algorithm as the proportion of modules. That was rightly prognosticated. Compare the delicacy rate of the algorithm when applied to modules with imperfect law with the delicacy rate of the algorithm when applied to modules with correct law. Use a 99-confidence interval.

DEFECT*PRED_LOC crosstabulation


PRED_LOC
total
noyes

DEFECT False

True

total

440

29

429

49

20

69

449

49

498

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