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29 Pages

### QMB3200 AnsChp 7

Course: ACCOUNTING 4101, Spring 2012
School: FIU
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Word Count: 2561

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7 Sampling Chapter and Sampling Distributions LEARNING OBJECTIVES The two main objectives for Chapter 7 are to give you an appreciation for the proper application of sampling techniques and an understanding of the sampling distributions of two statistics, thereby enabling you to: 1. 2. 3. Contrast sampling to census and differentiate among different methods of sampling, which include simple, stratified,...

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7 Sampling Chapter and Sampling Distributions LEARNING OBJECTIVES The two main objectives for Chapter 7 are to give you an appreciation for the proper application of sampling techniques and an understanding of the sampling distributions of two statistics, thereby enabling you to: 1. 2. 3. Contrast sampling to census and differentiate among different methods of sampling, which include simple, stratified, systematic, and cluster random sampling; and convenience, judgment, quota, and snowball nonrandom sampling, by assessing the advantages associated with each Describe the distribution of a samples mean using the central limit theorem, correcting for a finite population if necessary Describe the distribution of a samples proportion using the z formula for sample proportions CHAPTER TEACHING STRATEGY Virtually every analysis discussed in this text deals with sample data. It is important, therefore, that students are exposed to the ways and means that samples are gathered. The first portion of Chapter 7 deals with sampling. Reasons for sampling versus taking a census are given. Most of these reasons are tied to the fact that taking a census costs more than sampling if the same measurements are being gathered. Students are then exposed to the idea of random versus nonrandom sampling. Random sampling appeals to their concepts of fairness and equal opportunity. This text emphasizes that nonrandom samples are non-probability samples and cannot be used in inferential analysis because levels of confidence and/or probability cannot be assigned. It should be emphasized throughout the discussion of sampling techniques that as future business managers (most students will end up as some sort of supervisor/manager) students should be aware of where and how data are gathered for studies. This will help to assure that they will not make poor decisions based on inaccurate and poorly gathered data. The central limit theorem opens up opportunities to analyze data with a host of techniques using the normal curve. Section 7.2 begins by showing one population (randomly generated and presented in histogram form) that is uniformly distributed and one that is exponentially distributed. Histograms of the means for random samples of varying sizes are presented. Note that the distributions of means pile up in the middle and begin to approximate the normal curve shape as sample size increases. Note also by observing the values on the bottom axis that the dispersion of means gets smaller and smaller as sample size increases thus underscoring the formula for the standard error of the mean (). As the student sees the central limit theorem unfold, he/she begins to see that if the sample size is large enough sample means can be analyzed using the normal curve regardless of the shape of the population. Chapter 7 presents formulas derived from the central limit theorem for both sample means and sample proportions. Taking the time to introduce these techniques in this chapter can expedite the presentation of material in chapters 8 and 9. CHAPTER OUTLINE 7.1 Sampling Reasons for Sampling Reasons for Taking a Census Frame Random Versus Nonrandom Sampling Random Sampling Techniques Simple Random Sampling Stratified Random Sampling Systematic Sampling Cluster (or Area) Sampling Nonrandom Sampling Convenience Sampling Judgment Sampling Quota Sampling Snowball Sampling Sampling Error Nonsampling Errors 7.2 Sampling Distribution of Sampling from a Finite Population 7.3 Sampling Distribution of KEY TERMS Central Limit Theorem Cluster (or Area) Sampling Convenience Sampling Disproportionate Stratified Random Sampling Quota Sampling Random Sampling Sample Proportion Sampling Error Finite Correction Factor Frame Judgment Sampling Nonrandom Sampling Nonrandom Sampling Techniques Nonsampling Errors Proportionate Stratified Random Sampling Simple Random Sampling Snowball Sampling Standard Error of the Mean Standard Error of the Proportion Stratified Random Sampling Systematic Sampling Two-Stage Sampling SOLUTIONS TO PROBLEMS IN CHAPTER 7 7.1 a) i. ii. A union membership list for the company. A list of all employees of the company. b) i. ii. White pages of the telephone directory for Utica, New York. Utility company list of all customers. c) i. ii. Airline company list of phone and mail purchasers of tickets from the airline during the past six months. A list of frequent flyer club members for the airline. d) i. ii. List of boat manufacturer's employees. List of members of a boat owners association. e) i. ii. Cable company telephone directory. Membership list of cable management association. 7.4 Size of motel (rooms), age of motel, geographic location. b) Gender, age, education, social class, ethnicity. c) Size of operation (number of bottled drinks per month), number of employees, number of different types of drinks bottled at that location, geographic location. d) Size of operation (sq.ft.), geographic location, age of facility, type of process used. a) Under 21 years of age, 21 to 39 years of age, 40 to 55 years of age, over 55 years of age. b) Under \$1,000,000 sales per year, \$1,000,000 to \$4,999,999 sales per year, \$5,000,000 to \$19,999,999 sales per year, \$20,000,000 to \$49,000,000 per year, \$50,000,000 to \$99,999,999 per year, over \$100,000,000 per year. c) Less than 2,000 sq. ft., 2,000 to 4,999 sq. ft., 5,000 to 9,999 sq. ft., over 10,000 sq. ft. d) East, southeast, midwest, south, southwest, west, northwest. e) 7.5 a) Government worker, teacher, lawyer, physician, engineer, business person, police officer, fire fighter, computer worker. f) Manufacturing, finance, communications, health care, retailing, chemical, transportation. 7.6 n = N/k = 100,000/200 = 500 7.7 N = n k = 75(11) = 825 7.8 k = N/n = 3,500/175 = 20 Start between 0 and 20. The human resource department probably has a list of company employees which can be used for the frame. Also, there might be a company phone directory available. 7.9 a) i. ii. Counties Metropolitan areas b) i. ii. States (beside which the oil wells lie) Companies that own the wells c) i. ii. States Counties 7.10 Go to the district attorney's office and observe the apparent activity of various attorneys at work. Select some who are very busy and some who seem to be less active. Select some men and some women. Select some who appear to be older and some who are younger. Select attorneys with different ethnic backgrounds. 7.11 Go to a conference where some of the Fortune 500 executives attend. Approach those executives who appear to be friendly and approachable. 7.12 Suppose 40% of the sample is to be people who presently own a personal computer and 60% with people who do not. Go to a computer show at the city's conference center and start interviewing people. Suppose you get enough people who own personal computers but not enough interviews with those who do not. Go to a mall and start interviewing people. Screen out personal computer owners. Interview non personal computer owners until you meet the 60% quota. 7.13 = 50, = 10, n = 64 a) P(> 52): z = = 1.6 from Table A.5, Prob. = .4452 P( > 52) = .5000 - .4452 = .0548 b) P(< 51): z = = 0.80 from Table A.5 prob. = .2881 P(< 51) = .5000 + .2881 = .7881 c) P(< 47): z = = -2.40 from Table A.5 prob. = .4918 P(< 47) = .5000 - .4918 = .0082 d) P(48.5 < < 52.4): z = = -1.20 from Table A.5 prob. = .3849 z = = 1.92 from Table A.5 prob. = .4726 P(48.5 < < 52.4) = .3849 + .4726 = .8575 e) P(50.6 < < 51.3): z = = 0.48 from Table A.5, prob. = .1844 z= from Table A.5, prob. = .3508 P(50.6 < < 51.3) = .3508 - .1844 = .1644 7.14 = 23.45 = 3.8 a) n = 10, P( > 22): z= = -1.21 from Table A.5, prob. = .3869 P( > 22) = .3869 + .5000 = .8869 b) n = 4, P( > 26): z = = 1.34 from Table A.5, prob. = .4099 P( > 26) = .5000 - .4099 = 7.15 n = 36 = 278 .0901 P(< 280) = .86 .3600 of the area lies between = 280 and = 278. This probability is associated with z = 1.08 from Table A.5. Solving for : z= 1.08 = 1.08 = 2 = = 11.11 7.16 n = 81 = 12 P( > 300) = .18 .5000 - .1800 = .3200 and from Table A.5, z.3200 = 0.92 Solving for : z= 0.92 = 0.92 = 300 - 1.2267 = 300 - 7.17 a) = 300 - 1.2267 = 298.77 N = 1,000 n = 60 = 75 =6 P( < 76.5): z = = 2.00 from Table A.5, prob. = .4772 P(< 76.5) = .4772 + .5000 = .9772 b) N = 90 n = 36 = 108 = 3.46 P(107 < < 107.7): z= = -2.23 from Table A.5, prob. = .4871 z= = -0.67 from Table A.5, prob. = .2486 P(107 < < 107.7) = .4871 - .2486 = c) N = 250 P( > z= n = 100 = 35.6 .2385 = 4.89 36): = 1.05 from Table A.5, prob. = .3531 P( 36) = .5000 - .3531 = .1469 > d) N = 5000 n = 60 = 125 P( < 123): z= = -1.16 from Table A.5, prob. = .3770 P( < 123) = .5000 - .3770 = .1230 = 13.4 7.18 = 30 = 99.9 n = 38 a) P( < 90): z= = -2. 03 from table A.5, area = .4788 P( < 90) = .5000 - .4788 = .0212 b) P(98 < < 105): z= = 1.05 from table A.5, area = .3531 z= = -0.39 from table A.5, area = .1517 P(98 < < 105) = .3531 + .1517 = .5048 c) P( < 112): z= = 2.49 from table A.5, area = .4936 P(< 112) = .5000 + .4936 = .9936 d) P(93 < < 96): z= = -1.42 from table A.5, area = .4222 z= = -0.80 from table A.5, area = .2881 P(93 < < 96) = .4222 - .2881 = .1341 7.19 N = 1500 n = 100 = 177,000 = 8,500 P( > \$185,000): z= = 9.74 from Table A.5, prob. = .5000 7.20 P( > \$185,000) = .5000 - .5000 = .0000 = \$65.12 = \$21.45 n = 45 P( > ) = .2300 Prob. lies between and = .5000 - .2300 = .2700 from Table A.5, z.2700 = 0.74 Solving for : z= 0.74 = 2.366 = - 65.12 7.21 and = 11.8 = 50.4 = 65.12 + 2.366 = 67.486 n = 42 a) P( > 52): z = = 0.88 from Table A.5, the area for z = 0.88 is .3106 P(> 52) = .5000 - .3106 = .1894 b) P( < 47.5): z= = -1.59 from Table A.5, the area for z = -1.59 is .4441 P( < 47.5) = .5000 - .4441 = .0559 c) P( < 40): z= = -5.71 from Table A.5, the area for z = -5.71 is .5000 P( < 40) = .5000 - .5000 = .0000 d) 71% of the values are greater than 49. Therefore, 21% are between the sample mean of 49 and the population mean, = 50.4. The z value associated with the 21% of the area is -0.55 z.21 = -0.55 z= -0.55 = = 16.4964 7.22 p = .25 a) n = 110 z= P(< .21): = -0.97 from Table A.5, prob. = .3340 P(< .21) = .5000 - .3340 = .1660 b) n = 33 z= P( > .24): = -0.13 from Table A.5, prob. = .0517 P( > .24) = .5000 + .0517 = .5517 c) n = 59 z= P(.24 < < .27): = -0.18 from Table A.5, prob. = .0714 z = = 0.35 from Table A.5, prob. = .1368 P(.24 < < .27) = .0714 + .1368 = .2082 d) n = 80 P(> .30): z = = 1.03 from Table A.5, prob. = .3485 P( > .30) = .5000 - .3485 = e) n = 800 .1515 P( > .30): z = = 3.27 from Table A.5, prob. = .4995 P( > .30) = .5000 - .4995 = .0005 7.23 p = .58 n = 660 a) P(> .60): z = = 1.04 from table A.5, area = .3508 P(> .60) = .5000 - .3508 = .1492 b) P(.55 < < .65): z= = 3.64 from table A.5, area = .4998 z= = 1.56 from table A.5, area = .4406 P(.55 < < .65) = .4998 + .4406 = .9404 c) P( > .57): z = = -0.52 from table A.5, area = .1985 P( > .57) = .1985 + .5000 = .6985 d) P(.53 < < .56): z = = -1.04 z = = -2.60 from table A.5, area for z = -1.04 is .3508 from table A.5, area for z = -2.60 is .4953 P(.53 < < .56) = .4953 - .3508 = .1445 e) P( < .48): z = = -5.21 from table A.5, area = .5000 P( < .48) = .5000 - .5000 = .0000 7.24 p = .40 P( > .35) = .8000 P(.35 < < .40) = .8000 - .5000 = .3000 from Table A.5, z.3000 = -0.84 Solving for n: z= -0.84 = = 8.23 = n = 67.73 68 7.25 p = .28 n = 140 P( < ) = .3000 P( < < .28) = .5000 - .3000 = .2000 from Table A.5, z.2000 = -0.52 Solving for : z= -0.52 = -.02 = - .28 7.26 P(x > 150): n = 600 = .28 - .02 = .26 p = .21 x = 150 = = .25 z= = 2.41 from table A.5, area = .4920 P(x > 150) = .5000 - .4920 = .0080 7.27 p = .48 n = 200 a) P(x < 90): = = .45 z = = -0.85 from Table A.5, the area for z = -0.85 is .3023 P(x < 90) = .5000 - .3023 = .1977 b) P(x > 100): = = .50 z = = 0.57 from Table A.5, the area for z = 0.57 is .2157 P(x > 100) = .5000 - .2157 = .2843 c) P(x > 80): = = .40 z = = -2.26 from Table A.5, the area for z = -2.26 is .4881 P(x > 80) = .5000 + .4881 = .9881 7.28 p = .19 n = 950 a) P( > .25): z = = 4.71 from Table A.5, area = .5000 P( > .25) = .5000 - .5000 = .0000 b) P(.15 < < .20): z = = -3.14 z= = 0.79 from Table A.5, area for z = -3.14 is .4992 from Table A.5, area for z = 0.79 is .2852 P(.15 < < .20) = .4992 + .2852 = .7844 c) P(133 < x < 171): = = .14 = = .18 P(.14 < < .18): z = = -3.93 z= = -0.79 from Table A.5, the area for z = -3.93 is .49997 the area for z = -0.79 is .2852 P(133 < x < 171) = .49997 - .2852 = .21477 7.29 = 76, a) n = 35, = 14 P( > 79): z = = 1.27 from table A.5, area = .3980 P( > 79) = .5000 - .3980 = .1020 b) n = 140, P(74 < < 77): z = = -1.69 z = = 0.85 from table A.5, area for z = -1.69 is .4545 from table A.5, area for 0.85 is .3023 P(74 < < 77) = .4545 + .3023 = .7568 c) n = 219, z= P( < 76.5): = 0.53 from table A.5, area = .2019 P( < 76.5) = .5000 + .2019 = .7019 7.30 p = .46 a) n = 60 P(.41 < < .53): z = = 1.09 from table A.5, area = .3621 z = = -0.78 from table A.5, area = .2823 P(.41 < < .53) = .3621 + .2823 = .6444 b) n = 458 P(< .40): z = = -2.58 from table A.5, area = .4951 P( < .40) = .5000 - .4951 = .0049 c) n = 1350 P(> .49): z = = 2.21 from table A.5, area = .4864 P(> .49) = .5000 - .4864 = .0136 7.31 7.32 Under 18 18 25 26 50 51 65 over 65 p = .55 n = 600 250(.22) = 250(.18) = 250(.36) = 250(.10) = 250(.14) = n= 55 45 90 25 35 250 x = 298 = = .497 P(< .497): z = = -2.61 from Table A.5, Prob. = .4955 P(< .497) = .5000 - .4955 = .0045 No, the probability of obtaining these sample results by chance from a population that supports the candidate with 55% of the vote is extremely low (.0045). This is such an unlikely chance sample result that it would cause the researcher to probably reject her claim of 55% of the vote. 7.33 a) Roster of production employees secured from the human resources department of the company. b) Alpha/Beta store records kept at the headquarters of their California division or merged files of store records from regional offices across the state. c) Membership list of Maine lobster catchers association. 7.34 = \$ 17,755 P( = \$ 650 n = 30 N = 120 < 17,500): z = = -2.47 from Table A.5, the area for z = -2.47 is .4932 P( < 17,500) = .5000 - .4932 = .0068 7.35 Number the employees from 0001 to 1250. Randomly sample from the random number table until 60 different usable numbers are obtained. You cannot use numbers from 1251 to 9999. 7.36 = \$125 n = 32 = \$110 P( > \$110): z = = -3.70 from Table A.5, Prob.= .5000 P( > \$110) = .5000 + .5000 = 1.0000 2 = \$525 P( > \$135): z = = 2.47 from Table A.5, Prob.= .4932 P( > \$135) = .5000 - .4932 = .0068 P(\$120 < < \$130): z = = -1.23 z = = 1.23 from Table A.5, Prob.= .3907 P(\$120 < < \$130) = .3907 + .3907 = .7814 7.37 n = 1100 a) x > 810, p = .73 = z= = 0.48 from table A.5, area = .1844 P(x > 810) = .5000 - .1844 = .3156 b) x < 1030, p = .96, = = .9364 z = = -3.99 from table A.5, area = .49997 P(x < 1030) = .5000 - .49997 = .00003 c) p = .85 P(.82 < < .84): z = = -2.79 from table A.5, area = .4974 z = = -0.93 from table A.5, area = .3238 P(.82 < < .84) = .4974 - .3238 = .1736 7.38 1) 2) 3) 4) 5) 6) 7) 8) 9) The managers from some of the companies you are interested in studying do not belong to the American Managers Association. The membership list of the American Managers Association is not up-todate. You are not interested in studying managers from some of the companies belonging to the American Management Association. The wrong questions are asked. The manager incorrectly interprets a question. The assistant accidentally marks the wrong answer. The wrong statistical test is used to analyze the data. An error is made in statistical calculations. The statistical results are misinterpreted. 7.39 Divide the factories into geographic regions and select a few factories to represent those regional areas of the country. Take a random sample of employees from each selected factory. Do the same for distribution centers and retail outlets. Divide the United States into regions of areas. Select a few areas. Take a random sample from each of the selected area distribution centers and retail outlets. 7.40 N = 12,080 n = 300 k = N/n = 12,080/300 = 40.27 Select every 40th outlet to assure n > 300 outlets. Use a table of random numbers to select a value between 0 and 40 as a starting point. 7.41 p = .54 n = 565 a) P(x > 339): = = .60 z = = 2.86 from Table A.5, the area for z = 2.86 is .4979 P(x > 339) = .5000 - .4979 = .0021 b) P(x > 288): = = .5097 z = = -1.45 from Table A.5, the area for z = -1.45 is .4265 P(x > 288) = .5000 + .4265 = .9265 c) P( < .50): z = = -1.91 from Table A.5, the area for z = -1.91 is .4719 P( < .50) = .5000 - .4719 = .0281 7.42 = \$550 n = 50 = \$100 P( < \$530): z = = -1.41 from Table A.5, Prob.=.4207 P(x < \$530) = .5000 - .4207 = .0793 7.43 = 56.8 a) P( > 60): z = = 1.86 n = 51 = 12.3 from Table A.5, Prob. = .4686 P( > 60) = .5000 - .4686 = .0314 b) P( > 58): z = = 0.70 from Table A.5, Prob.= .2580 P( > 58) = .5000 - .2580 = .2420 c) P(56 < < 57): z= = -0.46 z = = 0.12 from Table A.5, Prob. for z = -0.46 is .1772 from Table A.5, Prob. for z = 0.12 is .0478 P(56 < < 57) = .1772 + .0478 = .2250 d) P(< 55): z = = -1.05 from Table A.5, Prob.= .3531 P( < 55) = .5000 - .3531 = .1469 e) P( < 50): z = = -3.95 from Table A.5, Prob.= .5000 P( < 50) = .5000 - .5000 = .0000 7.45 p = .73 n = 300 a) P(210 < x < 234): = = .70 z = = -1.17 = = .78 z = = 1.95 from Table A.5, the area for z = -1.17 is .3790 the area for z = 1.95 is .4744 P(210 < x < 234) = .3790 + .4744 = .8534 b) P(> .78): z = = 1.95 from Table A.5, the area for z = 1.95 is .4744 P(> .78) = .5000 - .4744 = .0256 c) p = .73 n = 800 P(> .78): z = = 3.19 from Table A.5, the area for z = 3.19 is .4993 P(> .78) = .5000 - .4993 = .0007 7.46 n = 140 P(x > 35): = = .25 p = .22 z = = 0.86 from Table A.5, the area for z = 0.86 is .3051 P(x > 35) = .5000 - .3051 = .1949 P(x < 21): = = .15 z = = -2.00 from Table A.5, the area for z = 2.00 is .4772 P(x < 21) = .5000 - .4772 = .0228 n = 300 p = .20 P(.18 < < .25): z = = -0.87 from Table A.5, the area for z = -0.87 is .3078 z = = 2.17 from Table A.5, the area for z = 2.17 is .4850 P(.18 < < .25) = .3078 + .4850 = .7928 7.47 By taking a sample, there is potential for obtaining more detailed information. More time can be spent with each employee. Probing questions can be asked. There is more time for trust to be built between employee and interviewer resulting in the potential for more honest, open answers. With a census, data is usually more general and easier to analyze because it is in a more standard format. Decision-makers are sometimes more comfortable with a census because everyone is included and there is no sampling error. A census appears to be a better political device because the CEO can claim that everyone in the company has had input. 7.48 p = .75 n = 150 x = 120 P(> .80): z = = 1.41 from Table A.5, the area for z = 1.41 is .4207 P( > .80) = .5000 - .4207 = .0793 7.49 a) Switzerland: n = 40 = \$ 30.67 =\$3 P(30 < < 31): from Table A.5, the area for z = 0.70 is .2580 the area for z = -1.41 is .4207 P(30 < < 31) = .2580 + .4207 = .6787 b) Japan: n = 35 = \$3 = \$ 20.20 P( > 21): from Table A.5, the area for z = 1.58 is .4429 P( > 21) = .5000 - .4429 = .0571 c) U.S.: n = 50 = \$ 23.82 =\$3 P( < 22.75): from Table A.5, the area for z = -2.52 is .4941 P(< 22.75) = .5000 - .4941 = .0059 7.50 a) b) c) d) 7.51 Age, Ethnicity, Religion, Geographic Region, Occupation, UrbanSuburban-Rural, Party Affiliation, Gender Age, Ethnicity, Gender, Geographic Region, Economic Class Age, Ethnicity, Gender, Economic Class, Education Age, Ethnicity, Gender, Economic Class, Geographic Location = \$281 n = 65 = \$47 P( > \$273): z = = -1.37 from Table A.5 the area for z = -1.37 is .4147 P( > \$273) = .5000 + .4147 = .9147
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COLLEGE ROSEThe first day of school our professor introduced himself and challenged us to get to knowsomeone we didn't already know. I stood up to look around when a gentle hand touchedmy shoulder. I turned around to find a wrinkled, little old lady be
FIU - ACCOUNTING - 4101
Thought of the WeekHOW DO YOU LIVE YOUR DASH?I read of a man who stood to speak,at the funeral of a friend.He referred to the dates on her tombstone,from the beginning.to the end.He noted that first came her date of birth,and spoke the following da
FIU - ACCOUNTING - 4101
ROCKSA philosophy professor stood before his class and had some items in front ofhim. When class began, wordlessly he picked up a large empty mayonnaisejar and proceeded to fill it with rocks right to the top, rocks about 2&quot;diameter.He then asked the
FIU - ACCOUNTING - 4101
SANDandSTONEA story tells that two friends were walking through thedesert. During some point of the journey they had anargument, and one friend slapped the other one in the face.The one who got slapped was hurt, but without sayinganything, wrote in t
FIU - ACCOUNTING - 4101
THE GIFTImagine there is a bank that credits your accout each morning with\$86,400. It carries over no balance from day to day. Every evening deleteswhatever part of the balance you failed to use during the day.What would you do? Draw out every cent, o
FIU - ACCOUNTING - 4101
Chapter 13 - Current Liabilities and ContingenciesChapter 13Current Liabilities andContingenciesEXERCISESExercise 13-21.Interest rate Fiscal year-end12%December 31\$400 million x 12% x 6/12 = \$24 millionInterest rateFiscal year-end2.10%Septe
FIU - ACCOUNTING - 4101
Chapter 13 - Current Liabilities and ContingenciesChapter 13Current Liabilities andContingenciesQUESTIONS FOR REVIEW OFTOPICSQuestion 13-1A liability entails the present, thefuture, and the past. It is a presentKEYresponsibility, to sacrifice ass
FIU - ACCOUNTING - 4101
Chapter 14 - Bonds and Long-Term NotesChapter 14NotesBonds and Long-TermQUESTIONS FOR REVIEW OF KEYTOPICSQuestion 14-1Periodic interest is calculated as the effective interest rate times the amount of the debtoutstanding during the period. This sa
FIU - ACCOUNTING - 4101
Chapter 14Bonds and Long-TermNotesAACSB assurance oflearning standards inaccounting and businesseducation requiredocumentation of outcomes assessment. Although schools, departments, and faculty may approachassessment and its documentation differen
FIU - ACCOUNTING - 4101
Chapter 15AACSB assurance of learning standards in accounting andbusiness education require documentation of outcomesassessment. Although schools, departments, and faculty mayapproach assessment and its documentation differently, one approach is to pr
FIU - ACCOUNTING - 4101
Chapter 16Accounting for IncomeTaxesAACSB assurance oflearning standards inaccounting and businesseducation requiredocumentation of outcomes assessment. Although schools, departments, and faculty may approachassessment and its documentation differ
FIU - ACCOUNTING - 4101
Chapter 16 - Accounting for Income TaxesChapter 16TaxesAccounting for IncomeQUESTIONS FOR REVIEW OF KEYTOPICSQuestion 16-1Income tax expense is comprised of both the current and the deferred tax consequences ofevents and transactions already recog
FIU - ACCOUNTING - 4101
Chapter 17Chapter 17Pensions and Other Postretirement BenefitsPensionsAACSB assurance of learning standards in accounting and business education requiredocumentation of outcomes assessment. Although schools, departments, and faculty may approachasse
FIU - ACCOUNTING - 4101
Chapter 17 - Pensions and Other Postretirement Benefit PlansChapter 17Pensions and Other Postretirement BenefitPlansPension plans aredesignedto provide income toQuestion 17-1individuals during theirretirement years. Funds are set aside during an
FIU - ACCOUNTING - 4101
Chapter 18ShareholdersEquityAACSB assurance of learningstandards in accounting and businesseducation require documentation ofoutcomes assessment. Although schools, departments, and faculty may approach assessment and itsdocumentation differently, o
FIU - ACCOUNTING - 4101
Chapter 18 - Shareholders EquityChapter 18EquityShareholdersQUESTIONS FOR REVIEW OF KEYTOPICSThe two primary sources of shareholders equity are amounts invested byshareholders in the corporation and amounts earned by the corporation on behalfQuest
FIU - ACCOUNTING - 4101
Chapter 19 - Share-Based Compensation and Earnings Per ShareChapter 19Share-Based Compensation and Earnings PerShareQUESTIONS FOR REVIEW OF KEY TOPICSQuestion 19-1Restricted stock refers to shares actually awarded in the name of an employee, althoug
FIU - ACCOUNTING - 4101
Chapter 19Chapter 19Share-Based Compensation AACSBlearningand Earnings per Shareandaccountingassuranceofstandardsinand businesseducation require documentation of outcomes assessment. Although schools, departments, andfaculty may approach asse
FIU - ACCOUNTING - 4101
Chapter 21 - Statement of Cash Flows RevisitedChapter 21RevisitedStatement of Cash FlowsQUESTIONS FOR REVIEW OF KEYTOPICSQuestion 21-1Every cash flow eventually affects the balance of one or more accounts on the balance sheet, andthe cash flows re
Lee - ACCT - 441
RE: Identify the six major ethical systems. Indicate which ethical system you subscribe toand defend your position with logical arguments. Reference the Bible, presentations, andother sources to support your conclusions.There are the six major ethical
Lee - ACCT - 441
RE: Censuses Report1) I have had great pleasure working in groups at Liberty. I enjoy working with others thatshare my faith. I have also worked with others who do not share my faith. I have hadgreat success with those group interactions as well. Some
Lee - ACCT - 441
RE: The prevailing ethical systems advocated for the accounting profession are ruledeontology and utilitarianism. Describe each system and indicate which system you believeis right for the accounting profession. Be sure to reference to the AICPA Code of
Lee - ACCT - 441
RE: Case 9-6 Depreciation AccountingA) There are vast differences between revenue and capital expenditures.a. Revenue expenditures are assets that are resell goods, raw materials, and any itembought to and then used for resale. In addition, any money u
Lee - ACCT - 441
RE: The Code of Professional ConductAllen has explain the position on how the AICPAs Professional Ethics ExecutiveCommittee (PEEC) is making it easy to access information (Allen, 2011). The committee isundergoing a major restructure to the AICPA. The c
Lee - ACCT - 441
Running Head: Auditors and EthicsAuditors and EthicsTable of Contents1. IntroductionAuditors and Ethics brief description containing the aspects of the paper.2. ComplianceAll Auditors have to follow compliance to ethics.3. DefianceThese are the co
Lee - ACCT - 441
IntroductionThe Committee of Sponsoring Organization of the Treadway Commission (COSO) wasstructure during 1985. There are five different professional organizations that collaborate onthree specialized areas: enterprise risk management (ERM), internal
Lee - ACCT - 441
1Running head: GROUP 2 CASE ANALYSIS 1Strategic Planning and Business Policy2GROUP 2 CASE ANALYSIS 1AbstractThis document represents Group Twos response to Group Case Analysis #1 from the LUO BUSI400 course textbook, Strategic Management Concept an
Lee - ACCT - 441
Monitor ActivitiesMonitor activities are involved in all five aspects of COSO framework. The monitoring isbroken into two different categories: ongoing and separate evaluations (COSO, 2011, p.108).They are then broken down into three more categories of
Lee - ACCT - 441
Monitor Activities Categories for Monitoring Activities Ongoing Evaluations Separate Evaluations Categories for Evaluations Compliance Operations ReportingMonitor ActivitiesOngoing EvaluationsDaily occurrenceManual reviewAutomated review (comp
Lee - ACCT - 441
Nike SWOT analysisS Internal StrengthsW Internal WeaknessesNike in known for its just do it slogan. It has manyfamous athletes that are known all over the worlddoing its commercials. Their reputation is known formoving one to be physically active. I
Lee - CJ - 200
RE: ClassificationI learned about the classifications that inmates go through. I learned about the history ofjails and prisons. The jails and prisons went through many different concepts before it wasdecided on what method would work. It is interesting
Lee - CJ - 200
RE: Final Appeal for ForgivenessA final appeal for forgiveness from an inmate sentenced to death and awaiting his or herexecution is not an easy manner to deal with. Being a Christian we all have been giving anappeal of forgiveness from God by Jesus dy
Lee - CJ - 200
a) Should judicial review be eliminated? Provide a detailed explanation for youranswer.Judicial review should always be used. Many individuals will use his or her power to helphim or her out. Not having judicial review could be where he or she would be
Lee - CJ - 200
RE: Law enforcementLaw enforcement has evolved tremendously over the last century. Organization, laws,and agencies are just to name a few of the changes that law enforcement has undergone. It willamaze even the youngest individual how much the law enfo
Lee - CJ - 200
Natural laws are the laws that God has made. God has determined that his people shouldlove everyone. He has determined that we should not murder each other. He has determined thatwe not lust one another. These are just some of the Ten Commandments that
Mount St. Mary's College, California - CHEM - 118 NURSIN
UNIT (5) SOLUTIONSA solution is a homogeneous mixture of two or more substances.5.1 Solution TerminologySolute and Solvent A simple solution has two components, a solute, and a solvent.The substance in smaller amount is called the solute, the substanc
Mount St. Mary's College, California - CHEM - 118 NURSIN
Unit 6: Thermochemistry, States of Matter and Intermolecular ForcesChemistryUnit 6: Thermochemistry, States of Matter and Intermolecular ForcesChapter 10: Causes of Change10.1: Energy TransferEnergy (E): - the ability to do work or produce heat.Ther
Mount St. Mary's College, California - CHEM - 118 NURSIN
Practice 8-1Give the IUPAC name of each of the following alcohols:OHOHb)a)CH3CH2CH3c)d)CH3 - CH2 - CH - CH2 - CH2OHOHAnswera) 4-methyl-4-heptanolb) cyclohexanolc) 3-ethyl-4-methyl-2-hexanold) 3-ethyl-1-pentanol8-1Practice 8-2Classify th
Mount St. Mary's College, California - CHEM - 118 NURSIN
GROUP PRESENTATION-SECTION 02GROUP 1GROUP 61. Emily Getz1. Alexis Gibson2. Samantha Chittenden2. Chloe Deranek3. Krista Ury3. Jane Blatt4. Meredith MurphyGROUP 21. Molly Levi2. Lauren Fox3. Christina HutchGROUP 31. Sarah Dapper2. Mary Verd
Mount St. Mary's College, California - CHEM - 118 NURSIN
Guidelines for Group Presentation1. Define the scientific terms used2. Provide the knowledge background of your topic3. Provide the environment which will lead learners to understand the source or origin of yourtopic4. Why your topic is important in
Mount St. Mary's College, California - CHEM - 118 NURSIN
Mount St. Mary's College, California - CHEM - 118 NURSIN
Mount St. Mary's College, California - CHEM - 118 NURSIN
L3: 2.4 Conditional Probability and Bayes Theorem Conditional Probability Independent Events Parallel and Serial Systems Bayes TheoremConditional ProbabilityLet A, B two events. The conditional probability of B givencondition A (given that A has oc
Mount St. Mary's College, California - CHEM - 118 NURSIN
Structure and Properties of Organic MoleculesReading: Wade chapter 2, sections 2-7- 2-14Study Problems: 2-35, 2-37, 2-39, 2-40, 2-41, 2-42,Key Concepts and Skills:Identify constitutional isomers and stereoisomers; identify polar and non-polarmolecule
Mount St. Mary's College, California - CHEM - 118 NURSIN
Chapter 8SolutionsMolarityLecturePLUS Timberlake 1Molarity (M)A concentration that expresses themoles of solute in 1 L of solutionMolarity (M) =moles of solute1 liter solutionLecturePLUS Timberlake 2Units of Molarity2.0 M HCl=2.0 moles HCl1