服务承诺
资金托管
原创保证
实力保障
24小时客服
使命必达
51Due提供Essay，Paper，Report，Assignment等学科作业的代写与辅导，同时涵盖Personal Statement，转学申请等留学文书代写。
51Due将让你达成学业目标51Due将让你达成学业目标51Due将让你达成学业目标51Due将让你达成学业目标私人订制你的未来职场 世界名企，高端行业岗位等 在新的起点上实现更高水平的发展
积累工作经验
多元化文化交流
专业实操技能
建立人际资源圈Logistic Analysis by SPSS--论文代写范文
2016-04-18 来源: 51Due教员组 类别: Report范文
51Due论文代写网精选report代写范文:“Logistic Analysis by SPSS”。由于统计数据的分布不符合严格的正态分布，我们不使用卡方检验和T检验分析方法。采用Logistic回归统计数据可以帮助我们通过日志功能的非正态变量变换成正态分布，并通过数据的分解分析影响因素。
经过分析，我们发现，英国的当地居民不断增长的接纳多元文化。由于中国学生的交流，英国开始接受中国文化。龙新年活动的运行给中国和英国人民友好交流的机会。事件旅游不仅促进了知名度和游客数量在英国，也使得中国人民更加重视英国。然而，由于文化差异的存在，它需要时间，英国的居民完全接受外来文化。然而，由于问卷的设计主要是为跨文化的影响，它不涉及相关利益或干扰。这是方向，进一步的研究可以扩大。
Because the distribution of statistical data does not meet the strict normal distribution, we do not use the chi-square test and T-test analysis methods. The use of logistic regression statistics can help us to transform the non-normal variables into the normal distribution by the log function, and analyze the affecting factors by the decomposition of the data.
We use the log function to demonstrate the differences in cultural acceptance before and after the event, in order to analyze the effectiveness of the activities. Dummy variables involved Nationality, Whether heared, participateWe choose ‘Analyze→ Regression → Multinomal Logistic’ from the menu to execute the logistic regression function. It is shown as the following Figure 5.3.1.
Figure 5.3.1
And the result is shown as Figure 5.3.2.
Case Processing Summary
N Marginal Percentage
participate Yes 52 44.1%
No 66 55.9%
nationality UK 69 58.5%
Other EU 6 5.1%
Chinese 41 34.7%
Other 2 1.7%
dragon year Yes 80 67.8%
No 38 32.2%
feel a. I like it, these events let me know more about foreign culture 74 62.7%
b. I like it, these events made me feel at home 19 16.1%
c. This sort of thing has nothing to do with m 22 18.6%
d. I don’t like it, these events do not suit UK culture 3 2.5%
Valid 118 100.0%
Missing 1
Total 119
Subpopulation 23a
a. The dependent variable has only one value observed in 17 (73.9%) subpopulations.
Model Fitting Information
Model Model Fitting Criteria Likelihood Ratio Tests
-2 Log Likelihood Chi-Square df Sig.
Intercept Only 91.734
Final 13.900 77.834 19 .000
Pseudo R-Square
Cox and Snell .483
Nagelkerke .647
McFadden .481
Likelihood Ratio Tests
Effect Model Fitting Criteria Likelihood Ratio Tests
-2 Log Likelihood of Reduced Model Chi-Square df Sig.
Intercept 13.900a .000 0 .
VAR15 17.153 3.254 1 .071
VAR1 13.900a .000 0 .
VAR4 13.900a .000 0 .
VAR14 13.900a .000 0 .
VAR1 * VAR4 13.900a .000 0 .
VAR1 * VAR14 13.900a .000 0 .
VAR4 * VAR14 13.900a .000 0 .
VAR1 * VAR4 * VAR14 13.900 .000 2 1.000
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.
a. This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.
Parameter Estimates
participatea B Std. Error Wald df Sig. Exp(B) 95% Confidence Interval for Exp(B)
Lower Bound Upper Bound
No Intercept -13.720 6910.895 .000 1 .998
VAR15 16.072 1659.321 .000 1 .992 9551622.595 .000 .b
[VAR1=1] -1.201 4455.141 .000 1 1.000 .301 .000 .b
[VAR1=2] .000 5599.024 .000 1 1.000 1.000 .000 .b
[VAR1=3] -.317 4029.466 .000 1 1.000 .728 .000 .b
[VAR1=4] 0c . . 0 . . . .
[VAR4=1] 15.923 6986.328 .000 1 .998 8226493.622 .000 .b
[VAR4=2] 0c . . 0 . . . .
[VAR14=1] 15.189 5392.921 .000 1 .998 3949016.032 .000 .b
[VAR14=2] -.649 5166.491 .000 1 1.000 .523 .000 .b
[VAR14=3] 14.872 4748.179 .000 1 .998 2875453.869 .000 .b
[VAR14=4] 0c . . 0 . . . .
[VAR1=1] * [VAR4=1] -15.923 1863.163 .000 1 .993 1.216E-7 .000 .b
[VAR1=1] * [VAR4=2] 0c . . 0 . . . .
[VAR1=2] * [VAR4=1] -34.351 7230.917 .000 1 .996 1.206E-15 .000 .b
[VAR1=2] * [VAR4=2] 0c . . 0 . . . .
[VAR1=3] * [VAR4=1] -15.654 .000 . 1 . 1.590E-7 1.590E-7 1.590E-7
[VAR1=3] * [VAR4=2] 0c . . 0 . . . .
[VAR1=4] * [VAR4=1] 0c . . 0 . . . .
[VAR1=4] * [VAR4=2] 0c . . 0 . . . .
[VAR1=1] * [VAR14=1] -16.341 3086.891 .000 1 .996 8.002E-8 .000 .b
[VAR1=1] * [VAR14=2] -17.868 6313.893 .000 1 .998 1.738E-8 .000 .b
[VAR1=1] * [VAR14=3] 0c . . 0 . . . .
[VAR1=1] * [VAR14=4] 0c . . 0 . . . .
[VAR1=2] * [VAR14=1] -.317 6113.422 .000 1 1.000 .728 .000 .b
[VAR1=2] * [VAR14=3] 0c . . 0 . . . .
[VAR1=3] * [VAR14=1] 0c . . 0 . . . .
[VAR1=3] * [VAR14=2] 0c . . 0 . . . .
[VAR1=3] * [VAR14=3] 0c . . 0 . . . .
[VAR1=4] * [VAR14=1] 0c . . 0 . . . .
[VAR1=4] * [VAR14=3] 0c . . 0 . . . .
[VAR4=1] * [VAR14=1] -16.240 6733.306 .000 1 .998 8.851E-8 .000 .b
[VAR4=1] * [VAR14=2] .137 6986.329 .000 1 1.000 1.147 .000 .b
[VAR4=1] * [VAR14=3] -16.024 7031.510 .000 1 .998 1.099E-7 .000 .b
[VAR4=1] * [VAR14=4] 0c . . 0 . . . .
[VAR4=2] * [VAR14=1] 0c . . 0 . . . .
[VAR4=2] * [VAR14=2] 0c . . 0 . . . .
[VAR4=2] * [VAR14=3] 0c . . 0 . . . .
[VAR4=2] * [VAR14=4] 0c . . 0 . . . .
[VAR1=1] * [VAR4=1] * [VAR14=1] 14.829 .000 . 1 . 2755582.521 2755582.521 2755582.521
[VAR1=1] * [VAR4=1] * [VAR14=2] 0c . . 0 . . . .
[VAR1=1] * [VAR4=1] * [VAR14=3] 0c . . 0 . . . .
[VAR1=1] * [VAR4=1] * [VAR14=4] 0c . . 0 . . . .
[VAR1=1] * [VAR4=2] * [VAR14=1] 0c . . 0 . . . .
[VAR1=1] * [VAR4=2] * [VAR14=3] 0c . . 0 . . . .
[VAR1=1] * [VAR4=2] * [VAR14=4] 0c . . 0 . . . .
[VAR1=2] * [VAR4=1] * [VAR14=1] .216 9746.915 .000 1 1.000 1.242 .000 .b
[VAR1=2] * [VAR4=1] * [VAR14=3] 0c . . 0 . . . .
[VAR1=2] * [VAR4=2] * [VAR14=1] 0c . . 0 . . . .
[VAR1=2] * [VAR4=2] * [VAR14=3] 0c . . 0 . . . .
[VAR1=3] * [VAR4=1] * [VAR14=1] 0c . . 0 . . . .
[VAR1=3] * [VAR4=1] * [VAR14=2] 0c . . 0 . . . .
[VAR1=3] * [VAR4=1] * [VAR14=3] 0c . . 0 . . . .
[VAR1=3] * [VAR4=2] * [VAR14=1] 0c . . 0 . . . .
[VAR1=3] * [VAR4=2] * [VAR14=2] 0c . . 0 . . . .
[VAR1=3] * [VAR4=2] * [VAR14=3] 0c . . 0 . . . .
[VAR1=4] * [VAR4=1] * [VAR14=1] 0c . . 0 . . . .
[VAR1=4] * [VAR4=2] * [VAR14=3] 0c . . 0 . . . .
a. The reference category is: Yes.
b. Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing.
c. This parameter is set to zero because it is redundant.
We can see from the result that, the suppose is correct. And we can analyze more information by this function. However, our purpose has completed. That is the event is effective when promote the cultural exchange and the native acceptance.
Conclusion
After the analysis, we found that the local residents of the United Kingdom are growing the acceptance to diverse culture. Due to the exchange of Chinese students, the British began to accept Chinese culture. The run of Dragon New Year activities gave a friendly communication opportunity for the people of China and Britain. The event tourism not only promotes the popularity and the number of visitors in the UK, but also makes the Chinese people to pay more attention to the United Kingdom. However, due to the existence of cultural differences, it takes time for the residents of the United Kingdom to fully accept the foreign culture. However, because the design of the questionnaire was mainly for the cross-cultural impact, it does not involve the related benefits or interference. This is the direction that further research can expand.
更多论文代写范文欢迎访问我们主页 www.51due.net 当然有论文代写需求可以和我们24小时在线客服QQ:800020041 联系交流。
51Due网站原创范文除特殊说明外一切图文著作权归51Due所有；未经51Due官方授权谢绝任何用途转载或刊发于媒体。如发生侵犯著作权现象，51Due保留一切法律追诉权。-J
