Latitudinal Gradient of Species Diversity Essay -- Geography Geology N

Latitudinal Gradient of Species Diversity The latitudinal gradient in species diversity is one of the most striking patterns in the distribution of organisms on the planet. Simply put, the average number of species per unit area increases dramatically the closer the area is to the equator, almost entirely regardless of the type of organism being considered (Pianka, 1994). Researchers investigating the gradient have formulated a wide variety of hypothesis explaining the higher level of species diversity in the tropics. These include but are not limited to: a greater degree of evolution and radiation in tropical species due to the long and relatively stable geological history of the area, seasonal climatic stability and/or predictability, a higher level of productivity, an increased rate of competition and a higher predation intensity (Pianka, 1994). Another theory is that tropical soils somehow influence species diversity and thus cause the latitudinal gradient. This paper will further investigate this final theory by outlini ng the basic characteristics of tropical soils, summarizing the mechanisms invoked to explain species diversity with these soil characteristics, and evaluating how well this research agrees with what is known about tropical soils. For the sake of narrowing the topic somewhat, attention is limited to the soils of and research occurring in Latin America. Tropical Soils In the past, tropical soils have been over-simplified and misunderstood (Sanchez, 1976), and this situation plagued soil science at least until the late seventies (Van Wambeke and Dudal, 1978). Sanchez (1976) attributes this misunderstanding of tropical soils to the fact that when temperate region-trained soil scientists first went to the... ...s richness in Costa Rican forests: Journal of Biogeography, 7, 147-157. Jordan, C.F. and Herrera, R., 1981, Tropical rain forests: are nutrients really critical?: American Naturalist, 117, 167-180. Paoletti, M.G., Taylor, R.A.J., Stinner, B.R., Stinner, D.H., and Benzing, D.H., Diversity of soil fauna in the canopy and forest floor of a Venequelan cloud forest: Journal of Tropical Ecology, 7, 373-383. Pianka, E.R., 1994. Evolutionary Ecology, Fifth Edition: New York, Harper Collins College Publishers, p. 390-396. Sanchez, P., 1976, Properties and Management of Soils in the Tropics: New York, John Wiley and Sons, Chapters 2,3,4, and 5. Van Wambeke, A., and Dudal, R., 1978, Macrovariability of soils of the tropics, p. 13-28 in Stelly, M. (editor-in-chief), Diversity of Soils in the Tropics: Ithaca, Department of Agronomy, Cornell University Press.

Barrier is something Essay

Unit 18 What is barrier? A barrier is something that gets into the way or stops another thing from happening. As we all know, communication is an extreme complex progress. And if one person finds it hard to understand subject or to write or even speak effectively about it, that person cannot be sure that his/her meaning has been received exactly. This loss of meaning which may block communication is often called Barrier. There are 3 main ways in which communication can be blocked: 1. If a person cannot see, hear, or receive the message 2. If a person cannot make sense of the message 3. If a person misunderstands the messages 1. Person cannot see, hear, or receive the message. Visual disability Hearing disability Environmental problems –(noise) Speaking from too far 2. Person cannot make sense of the message. Different language are being used, including sign language People using different terms, such as slang internet or text jargon One of the speakers has physical or intellect disability, such as memory loss or learning Dysfunction. 3. Person misunderstands the message. Cultural difference: different cultures interpret non-verbal and verbal and humour, in different ways Assumptions about people: assumptions about race, gender, disabilities etc. can lead to stereotyping and misunderstanding Emotional Difference, very angry or very happy people may misinterpret what is said think about sarcasm Social contest: conversation and non-verbal messages understood by close friend may not be understand by strangers. Physical barriers A physical barrier to communication Is something in the surrounding that stops the person from communicating with other. For example if the place where the conversation is held may be noisy. Impairmentsvc Some people will haveimpairments that can stop them from communication for example if they are unable to see,hear,or talk. Emotional factors Emotional factor can affect the way we communicate with others for example, lack of support /lack of trust, afraid,feeling happy,feeling sad,low self-estee/ or over/under confident. Different language Some people may not speak the same language as you and therefore you may have difficulty understanding each other Jargon Jargon is when people use technical words. The use of jargon can be confusing for other to understand.

Descriptive Statistics: Tabular and Graphical Presentations

chapter 2 Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations Learning Objectives 1. Learn how to construct and interpret summarization procedures for qualitative data such as : frequency and relative frequency distributions, bar graphs and pie charts. 2. Learn how to construct and interpret tabular summarization procedures for quantitative data such as: frequency and relative frequency distributions, cumulative frequency and cumulative relative frequency distributions. . Learn how to construct a dot plot, a histogram, and an ogive as graphical summaries of quantitative data. 4. Learn how the shape of a data distribution is revealed by a histogram. Learn how to recognize when a data distribution is negatively skewed, symmetric, and positively skewed. 5. Be able to use and interpret the exploratory data analysis technique of a stem-and-leaf display. 6. Learn how to construct and interpret cross tabulations and scatter diagrams of bivariate data.Solutions: 1. |Class | Frequency |Relative Frequency | |A |60 |60/120 = 0. 50 | |B | 24 |24/120 = 0. 20 | |C | 36 |36/120 = 0. 30 | | |120 | 1. 00 | 2. a. 1 – (. 22 + . 18 + . 40) = . 20 b.. 20(200) = 40 c/d. Class |Frequency |Percent Frequency | |A |. 22(200) = 44 | 22 | |B |. 18(200) = 36 | 18 | |C |. 40(200) = 80 | 40 | |D |. 20(200) = 40 | 20 | |Total |200 |100 | 3. a. 360 ° x 58/120 = 174 ° b. 360 ° x 42/120 = 126 ° c. [pic] d. [pic] 4. a. The data are qualitative. b. | |Percent Frequency | |Newspaper |Frequency | | |Liberty Times |24 |48 | |China Times |15 |30 | |United Daily News |7 |14 | |Apple Daily |4 |8 | |Total: |50 |100 | c. [pic] [pic] d. Liberty Times has the largest market share.China Times is second. 5. a. |Name |Frequency |Relative Frequency |Percent Frequency | |Chan |10 |. 200 |20. 0% | |Chang |7 |. 140 |14. 0% | |Lee |8 |. 160 |16. 0% | |Liu |7 |. 140 |14. 0% | |Wang |12 |. 240 |24. % | |Young |6 |. 120 |12. 0% | | |50 |1. 000 |100. 0% | b. [pic] c. Chan. 200 x 360 = 72 . 0( Chang. 140 x 360 = 50. 4( Lee. 160 x 360 = 58. 6( Liu. 140 x 360 = 50. 4( Wang. 240 x 360 = 86. 4( Young. 120 x 360 = 43. 2( [pic] d. Most common: Wang, Chan, and Lee 6. a. |Book |Frequency |Percent Frequency | |7 Habits |10 | |16. 6 | | |Millionaire |16 | |26. 67 | | |Motley |9 | |15. 00 | | |Dad |13 | |21. 67 | | |WSJ Guide |6 | |10. 00 | | |Other |6 | |10. 00 | | |Total: |60 | |100. 00 | |The Ernst & Young Tax Guide 2000 with a frequency of 3, Investing for Dummies with a frequency of 2, and What Color is Your Parachute? 2000 with a frequency of 1 are grouped in the â€Å"Other† category. b. The rank order from first to fifth is: Millionaire, Dad, 7 Habits, Motley, and WSJ Guide. c. The percent of sales represented by The Millionaire Next Door and Rich Dad, Poor Dad is 48. 33%. 7. |Rating |Frequency |Relative Frequency | |Outstanding |19 |0. 8 | |Very Good |13 |0. 26 | |Good |10 |0. 20 | |Average | 6 |0. 12 | |Poor | 2 |0. 04 | | |50 |1. 00 | Management should be plea sed with these results. 64% of the ratings are very good to outstanding. 84% of the ratings are good or better.Comparing these ratings with previous results will show whether or not the restaurant is making improvements in its ratings of food quality. 8. a. |Position |Frequency |Relative Frequency | |Pitcher |17 |0. 309 | |Catcher | 4 |0. 073 | |1st Base | 5 |0. 091 | |2nd Base | 4 |0. 073 | |3rd Base | 2 |0. 36 | |Shortstop | 5 |0. 091 | |Left Field | 6 |0. 109 | |Center Field | 5 |0. 091 | |Right Field | 7 |0. 127 | | |55 |1. 000 | b. Pitchers (Almost 31%) c. 3rd Base (3 – 4%) d. Right Field (Almost 13%) e. Infielders (16 or 29. 1%) to Outfielders (18 or 32. 7%) 9. a/b. Starting Time | Frequency |Percent Frequency | |7:00 |3 | |15 | | |7:30 |4 | |20 | | |8:00 |4 | |20 | | |8:30 |7 | |35 | | |9:00 |2 | |10 | | | |20 | |100 | | c. Bar Graph [pic] d. [pic] . The most preferred starting time is 8:30 a. m.. Starting times of 7:30 and 8:00 a. m. are next. 10. a. The data refer to quality levels from 1 â€Å"Not at all Satisfied† to 7 â€Å"Extremely Satisfied. † b. |Rating |Frequency |Relative Frequency | |3 |2 | 0. 03 | |4 |4 | 0. 07 | |5 |12 | 0. 20 | |6 |24 | 0. 40 | |7 |18 | 0. 0 | | |60 | 1. 00 | c. Bar Graph [pic] d. The survey data indicate a high quality of service by the financial consultant. The most common ratings are 6 and 7 (70%) where 7 is extremely satisfied. Only 2 ratings are below the middle scale value of 4. There are no â€Å"Not at all Satisfied† ratings. 11. |Class |Frequency |Relative Frequency |Percent Frequency | | | | | | |12-14 |2 |0. 50 |5. 0 | |15-17 | 8 |0. 200 | 20. 0 | |18-20 |11 |0. 275 | 27. 5 | |21-23 |10 |0. 250 | 25. 5 | |24-26 | 9 |0. 225 | 22. 5 | |Total |40 |1. 000 |100. | 12. |Class |Cumulative Frequency |Cumulative Relative Frequency | |less than or equal to 19 |10 | . 20 | |less than or equal to 29 |24 | . 48 | |less than or equal to 39 |41 | . 82 | |less than or equal to 49 |48 | . 6 | |less t han or equal to 59 |50 |1. 00 | 13. [pic] [pic] 14. a. [pic] b/c. |Class |Frequency |Percent Frequency | | 6. 0 – 7. 9 |4 | 20 | | 8. 0 – 9. 9 | 2 | 10 | |10. 0 – 11. 9 | 8 | 40 | |12. 0 – 13. 9 | 3 | 15 | |14. 0 – 15. | 3 | 15 | | |20 |100 | 15. a/b. |Waiting Time |Frequency |Relative Frequency | |0 – 4 |4 |0. 20 | |5 – 9 | 8 |0. 40 | |10 – 14 | 5 |0. 25 | |15 – 19 | 2 |0. 10 | |20 – 24 | 1 |0. 5 | |Totals |20 |1. 00 | c/d. |Waiting Time |Cumulative Frequency |Cumulative Relative Frequency | |Less than or equal to 4 |4 |0. 20 | |Less than or equal to 9 |12 |0. 60 | |Less than or equal to 14 |17 |0. 5 | |Less than or equal to 19 |19 |0. 95 | |Less than or equal to 24 |20 |1. 00 | e. 12/20 = 0. 60 16. a. The histogram is shown below. [pic] The histogram clearly shows that the annual household incomes are skewed to the right. And, of course, if annual household incomes are skewed to the right, so are annual incomes. This makes sense because the vast majority of annual incomes are less than NT$1,000,000.But, there are a few individuals with very large incomes. b. The histogram for the age is given. [pic] The histogram shows that the distribution of age is skewed to the left. This is to be expected. It is our experience that there are frequently a few very low ages causing such a pattern to appear. c. The histogram for the data in Exercise 11 is given. d. [pic] e. This histogram is skewed to the left slightly, but we would probably classify it as roughly symmetric. 17. a. |Amount (NT$ ‘000) |Frequency |Relative Frequency | |Less than 56 |3 |. 2 | |56-75 |5 |. 20 | |76-95 |11 |. 44 | |96-115 |4 |. 16 | |116-135 |1 |. 04 | |136 and more | 1 |. 04 | | |25 |1. 00 | b. Histogram [pic] The distribution has a roughly symmetric shape. c.The largest group spends NT$76-NT$95 per year on books and magazines. There are more in the NT$56 to NT$75 range than in the NT$96 to NT$115 range. 18. a. Lowest s alary: NT$29,300 Highest salary: NT$37,800 b. |Salary (NT$100s) |Frequency |Relative |Percent Frequency | | | |Frequency | | |293-307 |4 |0. 09 |9 | |308-322 |5 |0. 11 |11 | |323-337 |9 |0. 0 |20 | |338-352 |17 |0. 38 |38 | |353-367 |7 |0. 16 |16 | |368-382 |3 |0. 07 |7 | |Total |45 |1. 00 |100 | c. Proportion NT$33,700 or less: 18/45. d. Percentage more than NT$35,200: 10/45 [pic] e. The distribution is skewed slightly to the left, but is roughly symmetric. 19. a/b. Number |Frequency |Relative Frequency | |140 – 149 | 2 |0. 10 | |150 – 159 | 7 |0. 35 | |160 – 169 | 3 |0. 15 | |170 – 179 | 6 |0. 30 | |180 – 189 | 1 |0. 05 | |190 – 199 | 1 |0. 05 | |Totals |20 |1. 0 | c/d. |Number |Cumulative Frequency |Cumulative Relative Frequency | |Less than or equal to 149 | 2 |0. 10 | |Less than or equal to 159 | 9 |0. 45 | |Less than or equal to 169 |12 |0. 60 | |Less than or equal to 179 |18 |0. 0 | |Less than or equal to 189 |19 |0. 95 | |Less than o r equal to 199 |20 |1. 00 | e. [pic] 20. a. The percentage of people 39 or less is 12. 2 + 14. 2 + 17. 1 + 16. 2 = 59. 7. b. The percentage of the population over 39 years old is 16. 3 + 10. 9 + 6. 7 + 4. 7 + 1. 7 = 40. 3 c. The percentage of the population that is between 20 and 59 years old inclusively is 17. 1 + 16. 2 + 16. 3 + 10. = 60. 5 d. The percentage less than 30 years old is 12. 2 + 14. 2 + 17. 1 = 43. 5. So there are (. 435) (22,689,122) = 9,869,768. 07 people less than 30 years old. e. An estimate of the number of retired people is (. 047 + . 017) (22,689,122) = 1,452,103. 81 21. a/b. |Computer Usage | |Relative Frequency | |(Hours) |Frequency | | |0. 0 |- |2. 9 |5 |0. 10 | |3. 0 |- |5. 9 |28 |0. 56 | |6. 0 |- |8. |8 |0. 16 | |9. 0 |- |11. 9 |6 |0. 12 | |12. 0 |- |14. 9 |3 |0. 06 | | |Total |50 |1. 00 | c. [pic] d. [pic] e. The majority of the computer users are in the 3 to 6 hour range. Usage is somewhat skewed toward the right with 3 users in the 12 to 15 hour range. 22. |5 |7 8 | |6 |4 5 8 | |7 |0 2 2 5 5 6 8 | 8 |0 2 3 5 | 23. Leaf Unit = 0. 1 |6 |3 | |7 |5 5 7 | |8 |1 3 4 8 | |9 |3 6 | |10 |0 4 5 | |11 |3 | 24. Leaf Unit = 10 |11 |6 | |12 |0 2 | |13 |0 6 7 | |14 |2 2 7 | |15 |5 | |16 |0 2 8 | 17 |0 2 3 | 25. | 9 |8 9 | |10 |2 4 6 6 | |11 |4 5 7 8 8 9 | |12 |2 4 5 7 | |13 |1 2 | |14 |4 | |15 |1 | 26. a. 100 shares at $50 per share |1 |0 3 7 7 | |2 |4 5 5 | |3 |0 0 5 5 9 | |4 |0 0 0 5 5 8 | |5 |0 0 0 4 5 5 |This stem-and-leaf display shows that the trading prices are closely grouped together. Rotating the stem-and-leaf display counter clockwise shows a histogram that is slightly skewed to the left but is roughly symmetric. b. 500 shares traded online at $50 per share. |0 |5 7 | |1 |0 1 1 3 4 | |1 |5 5 5 8 | |2 |0 0 0 0 0 0 | |2 |5 5 | |3 |0 0 0 | |3 |6 | 4 | | |4 | | |5 | | |5 | | |6 |3 | This stretched stem-and-leaf display shows that the distribution of online trading prices for most of the brokers for 500 shares are lower than the trading pr ices for broker assisted trades of 100 shares. There are a couple of outliers. York Securities charges $36 for an online trade and Investors National charges much more than the other brokers: $62. 50 for an online trade. 27. 4 |1 3 6 6 7 | |5 |0 0 3 8 9 | |6 |0 1 1 4 4 5 7 7 9 | |7 |0 0 0 1 3 4 4 5 5 6 6 6 7 8 8 | |8 |0 1 1 3 4 4 5 7 7 8 9 | |9 |0 2 2 7 | or |4 |1 3 | |4 |6 6 7 | |5 |0 0 3 | |5 |8 9 | 6 |0 1 1 4 4 | |6 |5 7 7 9 9 | |7 |0 0 0 1 3 4 4 | |7 |5 5 6 6 6 7 8 8 | |8 |0 1 1 3 4 4 | |8 |5 7 7 8 9 | |9 |0 2 2 | |9 |7 | 28. a. |0 |5 8 | |1 |1 1 3 3 4 4 | |1 |5 6 7 8 9 9 | |2 |2 3 3 3 5 5 | |2 |6 8 | |3 | | 3 |6 7 7 9 | |4 |0 | |4 |7 8 | |5 | | |5 | | |6 |0 | b. |2000 P/E Forecast | |Percent Frequency | | |Frequency | | |5 – 9 |2 |6. 7 | | |10 – 14 |6 |20. 0 | | |15 – 19 |6 |20. 0 | | |20 – 24 |6 |20. | | |25 – 29 |2 |6. 7 | | |30 – 34 |0 |0. 0 | | |35 – 39 |4 |13. 3 | | |40 – 44 |1 |3. 3 | | |45 – 49 |2 |6. 7 | | | 50 – 54 |0 |0. 0 | | |55 – 59 |0 |0. 0 | | |60 – 64 |1 |3. 3 | | |Total |30 |100. 0 | | 29. a. [pic] b. [pic] c. [pic] d.Category A values for x are always associated with category 1 values for y. Category B values for x are usually associated with category 1 values for y. Category C values for x are usually associated with category 2 values for y. 30. a. [pic] b. There is a negative relationship between x and y; y decreases as x increases. 31. a. Row Percentages: | |Household Income (NT$ ‘000) | | |Age |Under 999 |1,000-1,499 |1,500-1,999 |2,000-2,499 |2,500-2,999 |3,000 or more |Total | |Under 34 |27. 6 |30. 54 |21. 01 |12. 99 |4. 93 |2. 76 |100. 00 | |35-44 |20. 90 |31. 32 |21. 49 |10. 48 |8. 79 |7. 03 |100. 00 | |45-54 |16. 00 |29. 17 |19. 24 |19. 87 |6. 83 |8. 88 |100. 00 | |55-64 |23. 73 |19. 26 |20. 01 |14. 46 |8. 81 |13. 73 |100. 00 | |65 or more |70. 57 |18. 37 |4. 42 |2. 4 |0. 74 |2. 96 |100. 00 | |Total |27. 70 |27. 32 |18. 27 |13. 05 |6. 51 |7. 15 |100. 00 | There are seven percent frequency distributions in this table with row percentages. The first six give the percent frequency distribution of income for each age level. The total row provides an overall percent frequency distribution for household income. Both of the two rows (age 35-44 and age 55- 64) have more percentage in the cells and descended in order of larger income.The second row is the percent frequency distribution for households headed by age 35-44. The fourth row is the percent frequency distribution for households headed by age 55-64. b. The percentage of heads of households by age 35-44 earning NT$2,500,000 or more is 8. 79% + 7. 03% = 15. 82%. The percentage of heads of households by age 55-64 earning $75,000 or more is 8. 81% + 13. 73% = 22. 54%. c. The percent frequency histograms are shown below. [pic] No. The histogram can not tell us any relationship between household income and age. 32. a. Column Percentages: |Household Income ($1000s) | | |Educa tion Level |Under 24. 9 |25. 0-49. 9 |50. 0-74. 9 |75. 0-99. 9 |100 or More |Total | |Not H. S. Graduate |32. 70 |14. 82 |8. 27 |5. 02 |2. 53 |15. 86 | |H. S. Graduate |35. 74 |35. 56 |31. 48 |25. 39 |14. 47 |30. 78 | |Some College |21. 17 |29. 77 |30. 25 |29. 2 |22. 26 |26. 37 | |Bachelor's Degree |7. 53 |14. 43 |20. 56 |25. 03 |33. 88 |17. 52 | |Beyond Bach. Deg. |2. 86 |5. 42 |9. 44 |14. 74 |26. 86 |9. 48 | |Total |100. 00 |100. 00 |100. 00 |100. 00 |100. 00 |100. 00 | There are six percent frequency distributions in this table of column percentages. The first five columns give the percent frequency distributions for each income level.The percent frequency distribution in the â€Å"Total† column gives the overall percent frequency distributions for educational level. From that percent frequency distribution we see that 15. 86% of the heads of households did not graduate from high school. b. The column percentages show that 26. 86% of households earning over $100,000 were h eaded by persons having schooling beyond a bachelor's degree. The row percentages show that 39. 72% of the households headed by persons with schooling beyond a bachelor's degree earned $100,000 or more. These percentages are different because they came from different percent frequency distributions. c.Compare the â€Å"under 24. 9† percent frequency distributions to the â€Å"Total† percent frequency distributions. We see that for this low income level the percentage with lower levels of education is lower than for the overall population and the percentage with higher levels of education is higher than for the overall population. Compare the â€Å"100 or more† percent frequency distribution to â€Å"Total† percent frequency distribution. We see that for this high income level the percentage with lower levels of education is lower than for the overall population and the percentage with higher levels of education is higher than for the overall population.Fr om the comparisons here it is clear that there is a positive relationship between household incomes and the education level of the head of the household. 33. a. The crosstabulation of condition of the greens by gender is below. | |Green Condition | | |Gender |Too Fast |Fine |Total | |Male |35 | 65 |100 | |Female |40 | 60 |100 | |Total |75 |125 |200 |The female golfers have the highest percentage saying the greens are too fast: 40%. b. 10% of the women think the greens are too fast. 20% of the men think the greens are too fast. So, for the low handicappers, the men have a higher percentage who think the greens are too fast. c. 43% of the woman think the greens are too fast. 50% of the men think the greens are too fast. So, for the high handicappers, the men have a higher percentage who think the greens are too fast. . This is an example of Simpson's Paradox. At each handicap level a smaller percentage of the women think the greens are too fast. But, when the crosstabulations are aggr egated, the result is reversed and we find a higher percentage of women who think the greens are too fast. The hidden variable explaining the reversal is handicap level. Fewer people with low handicaps think the greens are too fast, and there are more men with low handicaps than women. 34. a. | |EPS Rating | | | |Sales/Margins/ROE |0-19 |20-39 |40-59 |60-79 |80-100 |Total | |A | | | |1 |8 |9 | |B | |1 |4 |5 |2 |12 | |C |1 | |1 |2 |3 |7 | |D |3 |1 | |1 | |5 | |E | |2 |1 | | |3 | |Total |4 |4 |6 |9 |13 |36 | b. | | |EPS Rating | | | |Sales/Margins/ROE |0-19 |20-39 |40-59 |60-79 |80-100 |Total | |A | | | |11. 11 |88. 89 |100 | |B | |8. 33 |33. 33 |41. 67 |16. 67 100 | |C |14. 29 | |14. 29 |28. 57 |42. 86 |100 | |D |60. 00 |20. 00 | |20. 00 | |100 | |E | |66. 67 |33. 33 | | |100 | Higher EPS ratings seem to be associated with higher ratings on Sales/Margins/ROE. Of those companies with an â€Å"A† rating on Sales/Margins/ROE, 88. 89% of them had an EPS Rating of 80 or higher. Of the 8 companies with a â€Å"D† or â€Å"E† rating on Sales/Margins/ROE, only 1 had an EPS rating above 60. 35. a. | |Industry Group Relative Strength | | | |Sales/Margins/ROE |A |B |C |D |E |Total | |A |1 |2 |2 |4 | |9 | |B |1 |5 |2 |3 |1 |12 | |C |1 |3 | |2 |1 |7 | |D |1 | |1 |1 |2 |5 | |E | |1 |2 | | |3 | |Total |4 |11 |7 |10 |4 |36 | | | | | | | | | b/c. The frequency distributions for the Sales/Margins/ROE data is in the rightmost column of the crosstabulation.The frequency distribution for the Industry Group Relative Strength data is in the bottom row of the crosstabulation. d. Once the crosstabulation is complete, the individual frequency distributions are available in the margins. 36. a. [pic] b. One might expect stocks with higher EPS ratings to show greater relative price strength. However, the scatter diagram using this data does not support such a relationship. The scatter diagram appears similar to the one showing â€Å"No Apparent Relationship† in the text. 37. a. The crosstabulation is shown below: | |Speed |   | |Position |4-4. 49 |4. 5-4. 99 |5-5. 49 |5. 5-5. 9 |Grand Total | |Guard | | |12 |1 |13 | |Offensive tackle | |2 |7 |3 |12 | |Wide receiver |6 |9 | | |15 | |Grand Total |6 |11 |19 |4 |40 | b. There appears to be a relationship between Position and Speed; wide receivers had faster speeds than offensive tackles and guards. c. The scatter diagram is shown below: [pic] d. There appears to be a relationship between Speed and Rating; slower speeds appear to be associated with lower ratings. In other words,, prospects with faster speeds tend to be rated higher than prospects with slower speeds. 38. a. |Vehicle |Frequency |Percent Frequency | F-Series |17 |34 | |Silverado |12 |24 | |Taurus |8 |16 | |Camry |7 |14 | |Accord |6 |12 | |Total |50 |100 | b. The two top selling vehicles are the Ford F-Series Pickup and the Chevrolet Silverado. c. 39. a/b. |Industry |Frequency |Percent Frequency | |Beverage |2 |10 | |Chemicals | 3 | 15 | |Electronics | 6 | 30 | |Food | 7 | 35 | |Aerospace | 2 | 10 | |Totals: |20 |100 | . 40. a. Response |Frequency |Percent Frequency | |Accuracy |16 |16 | |Approach Shots |3 |3 | |Mental Approach |17 |17 | |Power |8 |8 | |Practice |15 |15 | |Putting |10 |10 | |Short Game |24 |24 | |Strategic Decisions | 7 | 7 | |Total |100 |100 | b. Poor short game, poor mental approach, lack of accuracy, and limited practice. 41. a/b/c/d. Book Value | |Relative Frequency |Cumulative Frequency |Cumulative | |per Share |Frequency | | |Relative Frequency | |0. 00-5. 99 | 3 |0. 10 | 3 |0. 10 | |6. 00-11. 99 | 15 |0. 50 |18 |0. 60 | |12. 00-17. 99 | 9 |0. 30 |27 |0. 90 | |18. 00-23. 99 | 2 |0. 07 |29 |0. 97 | |24. 00-29. 99 | 0 |0. 00 |29 |0. 7 | |30. 00-35. 99 | 1 |0. 03 |30 |1. 00 | |Total |30 |1. 00 | | | e. The histogram shown below shows that the distribution of most of the book values is roughly symmetric. However, there is one outlier (General Motors). 42. a. |Closing Price |Frequency |Rel ative Frequency | |0 – 9 7/8 |9 |0. 225 | |10 – 19 7/8 |10 |0. 250 | |20 – 29 7/8 | 5 |0. 25 | |30 – 39 7/8 |11 |0. 275 | |40 – 49 7/8 | 2 |0. 050 | |50 – 59 7/8 | 2 |0. 050 | |60 – 69 7/8 | 0 |0. 000 | |70 – 79 7/8 | 1 |0. 025 | |Totals |40 |1. 000 | b. |Closing Price |Cumulative Frequency |Cumulative Relative Frequency | |Less than or equal to 9 7/8 |9 |0. 25 | |Less than or equal to 19 7/8 |19 |0. 475 | |Less than or equal to 29 7/8 |24 |0. 600 | |Less than or equal to 39 7/8 |35 |0. 875 | |Less than or equal to 49 7/8 |37 |0. 925 | |Less than or equal to 59 7/8 |39 |0. 975 | |Less than or equal to 69 7/8 |39 |0. 75 | |Less than or equal to 79 7/8 |40 |1. 000 | c. [pic] d. Over 87% of common stocks trade for less than $40 a share and 60% trade for less than $30 per share. 43. a. | | |Relative Frequency | |Exchange |Frequency | | |American |3 |0. 15 | |New York |2 |0. 10 | |Over the Counter |15 |0. 75 | | |20 |1. 00 | b. Earn ings Per Share | |Relative Frequency | | |Frequency | | |0. 00 – 0. 19 |7 |0. 35 | |0. 20 – 0. 39 |7 |0. 35 | |0. 40 – 0. 59 |1 |0. 05 | |0. 60 – 0. 79 |3 |0. 15 | |0. 80 – 0. 99 |2 |0. 10 | | |20 |1. 00 | Seventy percent of the shadow stocks have earnings per share less than $0. 40. It looks like low EPS should be expected for shadow stocks. | | | |Price-Earning Ratio | |Relative Frequency | | |Frequency | | |0. 00 – 9. 9 |3 |0. 15 | |10. 0 – 19. 9 |7 |0. 35 | |20. 0 – 29. 9 |4 |0. 20 | |30. 0 – 39. 9 |3 |0. 15 | |40. 0 – 49. 9 |2 |0. 10 | |50. 0 – 59. 9 |1 |0. 05 | | |20 |1. 00 |P-E Ratios vary considerably, but there is a significant cluster in the 10 – 19. 9 range. 44. | | |Relative Frequency | |Income ($) |Frequency | | |18,000-21,999 |13 |0. 255 | |22,000-25,999 |20 |0. 392 | |26,000-29,999 |12 |0. 235 | |30,000-33,999 |4 |0. 078 | |34,000-37,999 |2 |0. 039 | |Total |51 |1. 000 | 45. a. 0 |8 9 | |1 |0 2 2 2 3 4 4 4 | |1 |5 5 6 6 6 6 7 7 8 8 8 8 9 9 9 | |2 |0 1 2 2 2 3 4 4 4 | |2 |5 6 8 | |3 |0 1 3 | b/c/d. |Number Answered Correctly | |Relative Frequency |Cumulative Frequency | | |Frequency | | | |5 – 9 |2 |0. 50 |2 | |10 – 14 | 8 |0. 200 |10 | |15 – 19 |15 |0. 375 |25 | |20 – 24 | 9 |0. 225 |34 | |25 – 29 | 3 |0. 075 |37 | |30 – 34 | 3 |0. 075 |40 | |Totals |40 |1. 000 | | e. Relatively few of the students (25%) were able to answer 1/2 or more of the questions correctly.The data seem to support the Joint Council on Economic Education’s claim. However, the degree of difficulty of the questions needs to be taken into account before reaching a final conclusion. 46. a/b. [pic] c. It is clear that the range of low temperatures is below the range of high temperatures. Looking at the stem-and-leaf displays side by side, it appears that the range of low temperatures is about 20 degrees below the range of high temperatures. d. There are two stems showing high temperatures of 80 degrees or higher. They show 8 cities with high temperatures of 80 degrees or higher. e. Frequency |Temperature |High Temp. |Low. Temp. |30-39 |0 |1 | |40-49 |0 |3 | |50-59 |1 |10 | |60-69 |7 |2 | |70-79 |4 |4 | |80-89 |5 |0 | |90-99 |3 |0 | |Total |20 |20 | 47. a. b. There is clearly a positive relationship between high and low temperature for cities. As one goes up so does the other. 48. a. | |Satisfaction Score | | | |Occupation |30-39 |40-49 |50-59 |60-69 |70-79 |80-89 |Total | |Cabinetmaker | | |2 |4 |3 |1 |10 | |Lawyer |1 |5 |2 |1 |1 | |10 | |Physical Therapist | | |5 |2 |1 |2 |10 | |Systems Analyst | |2 |1 |4 |3 | |10 | |Total |1 |7 |10 |11 |8 |3 |40 | b. | | |Satisfaction Score | | | |Occupation |30-39 |40-49 |50-59 |60-69 |70-79 |80-89 |Total | |Cabinetmaker | | |20 |40 |30 |10 |100 | |Lawyer |10 |50 |20 |10 |10 | |100 | |Physical Therapist | | |50 |20 |10 |20 |100 | Systems Analyst | |20 |10 |40 |30 | |100 | c. Each row o f the percent crosstabulation shows a percent frequency distribution for an occupation. Cabinet makers seem to have the higher job satisfaction scores while lawyers seem to have the lowest. Fifty percent of the physical therapists have mediocre scores but the rest are rather high. 49. a. [pic]b. There appears to be a positive relationship between number of employees and revenue. As the number of employees increases, annual revenue increases. 50. a. | | |Fuel Type | | | |Year Constructed |Elec |Nat.Gas |Oil |Propane |Other |Total | |1973 or before | 40 |183 |12 |5 | 7 |247 | |1974-1979 | 24 | 26 | 2 |2 | 0 | 54 | |1980-1986 | 37 | 38 | 1 |0 | 6 | 82 | |1987-1991 | 48 | 70 | 2 |0 | 1 |121 | |Total |149 |317 |17 |7 |14 |504 | b. |Year Constructed |Frequency |Fuel Type |Frequency | |1973 or before |247 | Electricity |149 | |1974-1979 | 54 | Nat.Gas |317 | |1980-1986 | 82 | Oil | 17 | |1987-1991 |121 | Propane | 7 | |Total |504 | Other | 14 | | | |Total |504 | c. Crosstabulation of Colum n Percentages | | |Fuel Type | | |Year Constructed |Elec |Nat. Gas |Oil |Propane |Other | |1973 or before | 26. 9 | 57. 7 | 70. 5 | 71. 4 | 50. 0 | |1974-1979 | 16. 1 | 8. 2 | 11. 8 | 28. 6 | 0. 0 | |1980-1986 | 24. 8 | 12. 0 | 5. 9 | 0. 0 | 42. 9 | |1987-1991 | 32. 2 | 22. 1 | 11. 8 | 0. 0 | 7. 1 | |Total |100. 0 |100. 0 |100. 0 |100. 0 |100. 0 | d. Crosstabulation of row percentages. | |Fuel Type | | | |Year Constructed |Elec |Nat. Gas |Oil |Propane |Other |Total | |1973 or before |16. 2 |74. 1 |4. 9 |2. 0 |2. 8 |100. 0 | |1974-1979 |44. 5 |48. 1 |3. 7 |3. 7 |0. 0 |100. 0 | |1980-1986 |45. 1 |46. 4 |1. 2 |0. 0 |7. 3 |100. 0 | |1987-1991 |39. 7 |57. 8 |1. 7 |0. 0 |0. 8 |100. 0 | e. Observations from the column percentages crosstabulation For those buildings using electricity, the percentage has not changed greatly over the years.For the buildings using natural gas, the majority were constructed in 1973 or before; the second largest percentage was constructed in 1987-1991. Most of t he buildings using oil were constructed in 1973 or before. All of the buildings using propane are older. Observations from the row percentages crosstabulation Most of the buildings in the CG&E service area use electricity or natural gas. In the period 1973 or before most used natural gas. From 1974-1986, it is fairly evenly divided between electricity and natural gas. Since 1987 almost all new buildings are using electricity or natural gas with natural gas being the clear leader. 51. a. Crosstabulation for stockholder's equity and profit. | |Profits ($000) | | | |Stockholders' Equity ($000) |0-200 |200-400 |400-600 |600-800 |800-1000 |1000-1200 |Total | |0-1200 |10 |1 | | | |1 |12 | |1200-2400 |4 |10 | | |2 | |16 | |2400-3600 |4 |3 |3 |1 |1 |1 |13 | |3600-4800 | | | | |1 |2 |3 | |4800-6000 | |2 |3 |1 | | |6 | |Total |18 |16 |6 |2 |4 |4 |50 | b. Crosstabulation of Row Percentages. | | |Profits ($000) | | | |Stockholders' Equity ($1000s) |0-200 |200-400 |400-600 |600-800 |800-1000 |10 00-1200 |Total | |0-1200 |83. 33 |8. 33 |0. 00 |0. 00 |0. 00 |8. 33 |100 | |1200-2400 |25. 00 |62. 50 |0. 00 |0. 00 |12. 50 |0. 0 |100 | |2400-3600 |30. 77 |23. 08 |23. 08 |7. 69 |7. 69 |7. 69 |100 | |3600-4800 | |0. 00 |0. 00 |0. 00 |33. 33 |66. 67 |100 | |4800-6000 |0. 00 |33. 33 |50. 00 |16. 67 |0. 00 |0. 00 |100 | c. Stockholder's equity and profit seem to be related. As profit goes up, stockholder's equity goes up. The relationship, however, is not very strong. 52. a. Crosstabulation of market value and profit. | |Profit ($1000s) | | | |Market Value ($1000s) |0-300 |300-600 |600-900 |900-1200 |Total | |0-8000 |23 |4 | | |27 | |8000-16000 |4 |4 |2 |2 |12 | |16000-24000 | |2 |1 |1 |4 | |24000-32000 | |1 |2 |1 |4 | |32000-40000 | |2 |1 | |3 | |Total |27 |13 |6 |4 |50 | b. Crosstabulation of Row Percentages. | | |Profit ($1000s) | | | |Market Value ($1000s) |0-300 |300-600 |600-900 |900-1200 |Total | |0-8000 |85. 19 |14. 81 |0. 00 |0. 00 |100 | |8000-16000 |33. 33 |33. 33 |16. 67 | 16. 67 |100 | |16000-24000 |0. 00 |50. 00 |25. 00 |25. 0 |100 | |24000-32000 |0. 00 |25. 00 |50. 00 |25. 00 |100 | |32000-40000 |0. 00 |66. 67 |33. 33 |0. 00 |100 | c. There appears to be a positive relationship between Profit and Market Value. As profit goes up, Market Value goes up. 53. a. Scatter diagram of Profit vs. Stockholder's Equity. [pic] b. Profit and Stockholder's Equity appear to be positively related. 54. a. Scatter diagram of Market Value and Stockholder's Equity. [pic] b. There is a positive relationship between Market Value and Stockholder's Equity. ———————– [pic] [pic] [pic]

The Career Criminal Essays - Criminology, Criminal Law, Crime

The Career Criminal Essays - Criminology, Criminal Law, Crime The career criminal The career criminal, or, more pointedly, those individuals who participate in criminal acts on a regular basis for both a central and constant source of income has, generally, a specific set of identifying factors which, while conclusive in laymen's terms, fail to meet the criteria necessary for scientific inquiry. While definitions exist as to what a career criminal is, the research methods employed in determining these definitions are a large point of contention for criminal justice theorists, especially due to their potential and virtually imminent inclusion to modern hypothesis on the subject. These research methods include longitudinal data collection and compilation, cross-sectional data collection and compilation, and, as at least one group of theorists argue, the most efficient method, informative interviewing. The longitudinal research method employs a data collection technique which focuses on the duration of a particular actin this case, the so-called criminal careerbased not upon specific incidents, but the length of time measured between such acts (Blumstein, Cohen, and Farrington, 1988). That is, an individual's propensity for criminal conduct in a so-called career mode would be measured first by the original act as an origin, then with the succeeding acts, until a final point became evident. Therefore, such a research method would logically conclude that an individual who performed or participated in criminal conduct on two occasions several years apart would be considered a career criminal. It is for this reason, that criminal justice theorists differ as to the applicability and relevance of the longitudinal research method (Blumstein, Cohen, and Farrington, 1988). Since the longitudinal research method could construe two independentor even two interdependantcriminal acts as the foundational make-up of a career criminal, theorists may hypothesize incorrectly as to the actuality of an individual having a career based in criminal behavior. Because it is widely believed by opponents of the longitudinal research method that the mere occurrence of two criminal acts spaced out over an individual's lifetime or testing window is not indicative of the so-called career criminal modus operandi, the research method has increasingly lost its popularity and application in such studies, unless, of course, it is supported or otherwise confirmed by other utilized research procedures (Blumstein, Cohen, and Farrington, 1988). One of these alternative testing and research methods is the cross-sectional data collection and compilation model. The cross-sectional data collection and compilation model, when applied to the criminal career hypothezation, measures the probability of occurrence of a particular act of criminal conduct or other so-called criminal behavior. The cross-sectional model allows for a glimpse into each individual criminal act which may be thought to, when compiled, comprise a framework which indicates that individual is a career criminal. For this reason, the cross-sectional model is infinitely more applicable and accurate in determining, or at least providing indicators which would lead to a determination, of conduct constituting that of a career criminal. While such assistance is immeasurable for a determination of whether or not an individual is a career criminal, it still falls short of a definite model for such identification. For this reason, many criminal justice theorists feel that the individual application of the cross-sectional model is inappropriate for its unsupported inclusion into relevant scientific hypothesis. Once again, however, when such data is adequately supported or otherwise confirmed by other information, inclusion is proper. Criminal justice theorists have relied on either one, or both models since the inception of investigation into all areas of criminal behavior. Such data, however, comes under fire if, and when, other theories surface which either provide additional information, or information which is more in-depth and in deference to that data already obtained and reported upon (Gottfredson and Hirschi, 1988). The dilemma, of course, is that regardless of how detailed and in-depth even the most comprehensive of testing techniques are, there is always one method which is the most detailed, as it originates from the primary source. This data is called informative interviewing (Gottfredson and Hirschi, 1988). Informative interviewing is a method through which criminal justice theorists acquire information from the primary source (Gottfredson and Hirschi, 1988). In the case of the present issue, deliberating over the question of what behavior is indicative of a career criminal, information would most probably be

Custom Component Development in Delphi

Custom Component Development in Delphi Components are essential elements of the Delphi environment. One of the most important features of Delphi is that we can use Delphi to create our own components. We can derive a new component from any existing component, but the following are the most common ways to create components: modifying existing controls, creating windowed controls, creating graphic controls, subclassing Windows controls and creating nonvisual components. Visual or not, with or without property editor, from scratch...you name it. Developing Delphi components isnt a simple task, it involves quite a bit of knowledge of the VCL. However, developing custom components is not an impossible task; writing components is just pure programming. Articles, Papers, Tutorials What follows is a list of articles that deal with custom component development in Delphi. Accessing protected members of a componentMany Delphi components have useful properties and methods that are marked invisible (protected) to a Delphi developer. In this article, you will find the workaround to this problem - thus enabling you to access a DBGrids RowHeights property, for example.Creating Custom Delphi Components - Inside and OutThis tutorial will explain component writing to you, which should result in more code reuse. It will go over properties, events, and methods, and will also explain how to install components. The final part of this tutorial is about Object-Oriented design.Creating Custom Delphi Components, Part IThis first part demonstrates some of the best approaches to building components, and at the same time provides tips on deciding on the best base class to inherit from, using virtual declarations, the complexities of overriding, and so on.Creating Custom Delphi Components, Part IIQuite often it is necessary to write components that perform more advanced f unctions. These components often need to either reference other components, have custom property data formats, or have a property that owns a list of values rather than a single value. We will explore various examples covering these very subjects, starting with the most simple. Creating Custom Delphi Components, Part IIIThis article is the final part of a three part article on components. Part one covered the basic creating of components, part two covered how to write advanced properties, how to write custom streaming for those properties and sub-properties. This final part will cover property/component editors, how to write dedicated editors for your component/property, and how to write hidden components. More Resources First, if you want more, consider buying a book on Developing custom components.Second, why not try locating an existing (with source perhaps) component you are looking for.Third, when you are 100% sure there is no such question on custom component development you cant answer...there will be something that you dont know. Everything you have to do is to ask a question on the Delphi Programming Forum and wait for answers. Articles, papers, tutorialsHere is a list of articles that deal with custom component development in Delphi. VCL Component Messages [RTF]Component Messages (CM_) are generated only by the VCL and are not reflected Windows Messages (WM_), as one may assume. In spite of that Component Notifications (CN_) are reflected Windows Messages. The idea behind it is, that Windows often sends messages to a parent window of a control instead of the control itself. The VCL simply converts (reflects) these messages to Component Notifications and then sends it to the control, for which the message originally was meant. Delphi Component Building.In this article, read about every aspect of Delphi Component building. Design a TTicTacToe component and learn about: how to build our own components for Delphi, how to add properties, methods and custom events to them, how to wrap them around DLLs, how to install them, how to design a palette bitmap and write on-line help to support the component user. Building SuperComponents in Delphi [download]SuperComponents, also known as aggregate or compound components, are collections of existing sub-components and their relationships combined into a single component. The collections are typically arranged inside a container parent component that manages the visual layout of the sub-components.

Marketing Discussion Assignment Example | Topics and Well Written Essays - 750 words

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Letting the Big Ones Get Away Case Study Example | Topics and Well Written Essays - 750 words

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