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數學與統計 大學

看不懂表格的數據 有人可以詳細解釋嗎?

其次作雙變項分析,由表四觀察,可知各自變 項與良好飲食行為的相關均為中等至稍弱,最強的 相關為情境自我效能(r=.36 ),其次為自覺障礙 性(r = -25 ),自覺利益性(r.19)與自覺罹患性 =.16)則稍弱而數值較接近,自覺嚴重性及個人自 我效能則沒有顯著相關。 研究者將各自變項放入複迴歸模式中,結果如 表五。達到顯著意義的預測變項為性別(B=.50)、 情境自我效能(B=.42)、自覺障礙性(B=.19)、自 覺利益性(B=.13),自覺罹患性(B-09),整體模 50 黃連具 纸元青 式可解釋的變異量達18%。較值得注意的是:自覺 罹患性對飲食行為的影響係負號,即愈覺得不可能 罹患疾病者,飲食行為愈佳,這是和健康信念模式 所預測的方向相反的。健康信念模式的變項和飲食 行為的相關,在控制社會人口學變項後,也仍然存 在。 表五 各變項對飲食行為之複迴歸係數及解釋變異 量n=451 迴歸係數(6) 標準誤 T值 顯著水準, 自從罹患性 -.09 204 .04 自覺嚴重性 .01 .06 .03 n..s. 自覺障礙性 -.19 .07 .01 自覺利益性 .13 .41 .00 個人自我效能 .04 .04 1.03 n.s. 情境自我效能 .42 .06 6.77 健康狀況 .12 .23 .50 n.s. 年級 .03 .23 .03 n.s. 性別(d) .50 .46 .13 家庭社經地位 .19 .16 1.19 n.s. R-square 車車事 表四 飲食行為與社會心理學變項相關情形 n=451 自觉耀自覺嚴自障自覺利個人自情境自 患性重性礙 性益性我效能我效能 飲食行為 - 16中車 - 25** .19** .09 .36*** **p<.05 ***p<.001 .04 .18 d-dummy variable ES: l-0-3 *p<.05. **p<.01 ***p<.001

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數學與統計 大學

急!求救 #6 8 9 如果需要酬勞可私下談 (ex.$70題)

pie participation in informal 6.The National Science Foundation (NSF) sponsored a study on girls' science, technology, engineering, or mathematics (STEM) programs. The results of the study were published in Cascading Influences: Long-Term Impacts of Informal STEM Experiences for Girls (March 2013). The researchers sampled 174 young women who recently participated in a STEM program. They used a pie chart to describe the geographic location (urban, suburban, or rural) of the STEM programs attended. Of the 174 participants, 107 were in urban areas, 57 in suburban areas, and 10 in rural areas. a.Determine the proportion of STEM participants from urban areas. b.Determine the proportion of STEM participants from suburban areas. c.Determine the proportion of STEM participants from rural areas. d. Multiply each proportion in parts a-c by 360 to determine the pie slice size (in degrees) for each location. e.Use the results, part d, to construct a pie chart for geographic location of STEM participants. f.Interpret the pie slice for urban areas. g.Convert the pie chart into a bar graph. Which, in your opinion, is more informative? 7. All high way bridges in the US are inspected periodically for structural deficiency by the FHWA. Data from the FHWA inspections are compiled into the National Bridge Inventory (NBI). Classify each variable below as quantitative or qualitative. a. Length of maximum span (feet). b. Number of vehicle lanes. c. Toll bridge (yes or no). d. Average daily traffic. e. Condition of deck (good, fair, or poor). f. Bypass or detour length (miles). g. Route type (interstate, U.S., state, county, or city) 8. The NBI data were analyzed and the results made available at the FHWA Web site. Using the FHWA inspection ratings, each of the 608,272 highway bridges in the US was categorized as structural deficient, functionally obsolete, or safe. About 13.5% of the bridges were found to be structural deficient, while 3.5% were functionally obsolete. a. What is the variable of interest to the researchers? b. Is the variable of part a quantitative or qualitative? c. Is the data set analyzed a population or a sample? Explain. d. How did the NBI obtain the data for the study?

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數學與統計 大學

第二題的d的積分範圍要怎麼設

6 Kx, lo, 14) 1. (10 points) How many even numbers can be formed from the digits 9, 1,4,5,6, and 9 if each digit can be used only once? 2. (50 points) Let X and Y denote the lengths of life, in years, of two components A and B, respectively, ş! x2 in an electronic system. If the joint density function of these variables is 64 0<x<1-ycl EX,Y) Rx hy 0 < x <1.0<x<1-x: f(x, y) = elsewe jey.301-4)*84f CX74 3 Rxdy * 了 1' Jay You Determine the value k; FED ECX) = 86 x 6xci->)dy cy) 3(1-2) ² (b) Find the marginal distributions, expected values, variances, and covariance of X and Y; dy= 1 (C) Determine whether X and Y are dependent or independent; X(d) Find the probability that the length of life of component A is less than that of component B; X(e) Find the probability that the length of life of component A is greater than one year, given the ar length of life of component B is equal to two year. xcy 1313. (10 points) The probability that a flight departs on time is 0.3; the probability that it arrives on time is 0.3; and the probability that it departs and arrives on time is 0.1. Find the probability that it arrives on time, given that it did not depart on time. ex oin 4. (20 points) The waiting time, in hours, between successive speeders spotted by a radar unit is a continuous random variable with cumulative distribution -8x76 / le = 1- e 11-e dx x ZO; dv=e 0, f(x) = f'(X) = x < 0. 8 e V= 1 84 (a) Find the probability of waiting less than 10 minutes between successive speeders; hind the wyerane waiting time between successiye speeders spotted by a radar unit. 013-0il u=X -8X -81 -8% ge

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