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上周，我们就如何衡量变量之间的关系进行了讲座。
首先，皮尔森相关性是衡量2个变量之间的线性相关的。在课堂上，学生的英语能力和适应在英国的生活，是在SPSS分析。我们演示了如何使用该模型的步骤和读取结果。总之，这一假设的皮尔森相关（代表的字母）是0.411，这意味着高度与这2个变量。
其次，介绍了卡方检验，确定了2个变量之间是否存在显著差异。中国和英国的主要倾向于男性和女性的假设被使用。根据以往的调查问卷的数据输入SPSS模型。中文专业，结果表明，卡方检验值（X2）是6.071，当该值大于5，我们可以得出结论说，没有统计学意义，而假设是一个空；我们在英国可以找到性别重大案件相同的结果。
第三，t检验是一个统计假设的正常分布的方差是未知的，样本量小（一般是不到30）。在个案研究中，独立变量是受教育程度的高低，而相关变量则是对英语能力的信任程度。再次，在SPSS分析，试验表明，T是-2.388，和SIG（双尾）是0.021（当它小于0.5），我们可以得出这样的结论：存在一个明显的具有学士学位，学生在英语能力更有信心。
最后，方差分析是短的方差分析，它是用来比较2个样本的装置。在接下来的案例研究中，独立变量是年龄（分为2组），另一个依赖方是信心的水平。通过计算机分析，我们最终进入了：描述表，该列“平均”应该是显着不同的，否则有数据本身是没有意义的。方差分析（占）的值是3.028，大于1，我们可以得出结论，有一个统计意义。在最后的事后检验21岁的信心，正态分布（95%置信区间）水平低于其他年龄段。Last week, we had lecture of how to measure relationships between variables. The lecturer go through some definition of concepts, such as probability and variables. The main task is to analyze a hypothesis under SPSS by choosing a certain kind of statistic test.Firstly, Pearson Correlation is measure of the linear correlation between 2 variables. In the class, students’ English ability and their adjust to life in the UK is examed under SPSS. We are demonstrated how to use this model step by step and read results. In conclusion, the Pearson Correlation (represented by letter r) in this hypothesis is 0.411, which means highly related with this 2 variables. Secondly, Chi-square Test was introduced; it is a test to determine whether there is a significant difference between 2 variables. The hypothesis of Chinese and UK major preferred by males and females was used. Based on previous questionnaire data was typed into SPSS model. For Chinese major, the result shows that the value of chi-square test (X2 ) is 6.071, when the value is larger than 5, we might conclude that, there is no statistical significant, and the hypothesis is a null; we can find the same result in UK gender-Major case. Thirdly, T-test is a statistical hypothesis when the variances of the normal distributions are unknown and the sample size is small (generally n is less than 30). In the case study next, the independent variable is level of education; while dependent variable is the level of confidence in English ability. Again, under SPSS analysis, the test shows that the t is -2.388, and Sig (2-tailed) is 0.021 (when it is less than 0.5), we can conclude: there exists a significant that with bachelor degrees, students are more confidence in English ability. Finally, ANOVA is short for analysis of variance, it is used to compare the means of more than 2 samples. In the next case study, the independent variable is age (categorized into 2 groups); the other dependent party is the level of confidence. Trough computer analysis, we finally came into: the descriptive table, the column “MEAN” should be significantly different, otherwise there data itself is meaningless. The value of ANOVA (represented by F) is 3.028, which is larger than 1, we can conclude, there is a statistic significant. In the final post hoc test of 21-year-old confidence, the normal distribution (95% confidence interval) level is lower than other ages. |
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