SoftHome 教學範例

1. SoftHome教學範例 SigmaPlot Statistics Total View 全傑科技 Pid Gau 高金海www.softhome.com.tw

2. 【內建統計種類】 敘述統計 Descriptive Statistics 單母體檢定 One-Sample t-test 雙母體檢定 Compare Two Groups 多母體檢定 Compare Many Groups 前後檢定 Before and After 重複測量 Repeated Measures 比例及列聯表 Rates And Proportions 回歸 Regression 相關分析 Correlation 存活分析 Survival 常態檢定 Normality 檢定力 Power 樣本數 Sample Size 非線性回歸Nonlinear Regression

3. 【敘述統計 Descriptive Statistics】 資料呈現方式 1. Raw Data (原始資料) 即每一欄代表一組樣本 例如第一欄為男生之身高 第二欄為女生身高

4. 【單母體檢定 One-Sample t-test】 資料呈現方式 1. Raw Data (原始資料) 即每一欄代表一組樣本 例如第一欄為男生之身高 3.Mean,Size,SD 有一些已處理好的資料,只有平均數,標準差,樣本數 可選此資料方式輸入

5. 【單母體檢定 One-Sample t-test】資料呈現方式

6. 【單母體檢定 One-Sample t-test】說明 The One Sample t-test calculates the t statistic, degrees of freedom, and P value of the specified data. These results are displayed in the One Sample t-Test report which automatically appears after the One Sample t-Test is performed. The other results displayed in the report are enabled and disabled in the Options for t-Test dialog box. For descriptions of the derivations for t-test results, you can reference any appropriate statistics reference.

7. 【雙母體檢定 Compare Two Groups】種類

8. 【雙母體檢定 Compare Two Groups】t-test 雙母體t檢定 例: 兩組病人的血糖資料的差異,(Unpaired t-test)檢定是最適合的 資料呈現方式 1. Raw Data (原始資料) 即每一欄代表一組樣本 例如第一欄為男生之身高 第二欄為女生身高 2. Indexed Data (索引資料) 即每一欄內含數組樣本之索引 例如第一欄內含男生女生之索引 男生為1 女生為0 第二欄為為其對應之身高 第三欄為為其對應之體重 建議您使用Indexed Data (索引資料)可以有無窮的變化 3.Mean,Size,SD 有一些已處理好的資料,只有平均數,標準差,樣本數 可選此資料方式輸入

9. 【雙母體檢定 Compare Two Groups】t-test 說明 Use an Unpaired t-test when: You want to see if the means of two different samples are significantly different. Your samples are drawn from normally distributed populations with the same variances. When there are more than two groups to compare, do a One Way Analysis of Variance. Note: Depending on your t-test options settings, if you attempt to perform a t-test on non-normal populations or populations with unequal variances, SigmaPlot will inform you that the data is unsuitable for a t-test, and suggest the Mann-Whitney Rank Sum Test instead. See Setting t-Test Options for more information.

10. 【雙母體檢定 Compare Two Groups】Rank Sum Test Mann-Whitney Rank Sum Test 順序和檢定 無母數檢定 檢定中位數 雙母體檢定(不服從常態分配) 例: 兩組病人的傷口長度資料 (不服從常態分配) 資料呈現方式 1. Raw Data (原始資料) 即每一欄代表一組樣本 例如第一欄為男生之身高 第二欄為女生身高 2. Indexed Data (索引資料) 即每一欄內含數組樣本之索引 例如第一欄內含男生女生之索引 男生為1 女生為0 第二欄為為其對應之身高 第三欄為為其對應之體重

11. 【雙母體檢定 Compare Two Groups】Rank Sum Test說明 Use the Rank Sum Test when: You want to see if the medians of two different samples are significantly different. The samples are not drawn from normally distributed populations with the same variances, or you do not want to assume that they were drawn from normal populations. If you know your data was drawn from a normally distributed population, use the Unpaired t-test. When there are more than two groups to compare, run a Kruskal-Wallis ANOVA on Ranks test. Note: Depending on your Rank Sum Test options settings, if you attempt to perform a rank sum test on normal populations with equal variances, SigmaPlot informs you that the data can be analyzed with the more powerful Unpaired t-test instead. See Setting Mann-Whitney Rank Sum Test Options for more information.

12. 【雙母體檢定 Compare Two Groups】資料呈現方式

13. 【多母體檢定 Compare Many Groups】種類

14. 【多母體檢定 Compare Many Groups】One Way ANOVA 單因子變異數分析 (服從常態分配) 例: 三種藥物藥效比較 資料呈現方式如下

15. 【多母體檢定 Compare Many Groups】One Way ANOVA 資料呈現方式

16. 【多母體檢定 Compare Many Groups】One Way ANOVA 說明 One Way Analysis of Variance is a parametric test that assumes that all the samples are drawn from normally distributed populations with the same standard deviations (variances). Use a One Way or One Factor ANOVA when: You want to see if the means of two of more different experimental groups are affected by a single factor. Your samples are drawn from normally distributed populations with equal variance. If you know that your data was drawn from non-normal populations, use the Kruskal-Wallis ANOVA on Ranks test. See Kruskal-Wallis Analysis of Variance on Ranks for more information. If you want to consider the effects of two factors on your experimental groups, use Two Way ANOVA. See Two Way Analysis of Variance (ANOVA) for more information. When there are only two groups to compare, you can do a t-test (depending on the type of results you want). Performing an ANOVA for two groups yields exactly the same P value as an unpaired t-test. See Unpaired t-Test for more information. Note: Depending on your ANOVA options settings, if you attempt to perform an ANOVA on non-normal populations or populations with unequal variances, SigmaStat informs you that the data is unsuitable for a parametric test, and suggests the Kruskal-Wallis ANOVA on Ranks. See Setting One Way ANOVA Options for more information.

17. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法 Holm-Sidak Tukey Student-Newman-Keuls Dunnett Bonferroni Fisher LSD Duncan's Multiple Range

18. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Holm-Sidak Test Use the Holm-Sidak Test for both pairwise comparisons and comparisons versus a control group. It is more powerful than the Tukey and Bonferroni tests and, consequently, is able to detect differences that these other tests do not. It is recommended as the first-line procedure for pairwise comparison testing. When performing the test, the P values of all comparisons are computed and ordered from smallest to largest. Each P value is then compared to a critical level that depends upon the significance level of the test (set in the test options), the rank of the P value, and the total number of comparisons made. A P value less than the critical level indicates there is a significant difference between the corresponding two groups.

19. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Tukey Test The Tukey Test and the Student-Newman-Keuls test are conducted similarly to the Bonferroni t-test, except that they use a table of critical values that is computed based on a better mathematical model of the probability structure of the multiple comparisons. The Tukey Test is more conservative than the Student-Newman-Keuls test, because it controls the errors of all comparisons simultaneously, while the Student-Newman-Keuls test controls errors among tests of k means. Because it is more conservative, it is less likely to determine that a give differences is statistically significant and it is the recommended test for all pairwise comparisons.

20. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Student-Newman-Keuls Test The Student-Newman-Keuls Test and the Tukey Test are conducted similarly to the Bonferroni t-test, except that they use a table of critical values that is computed based on a better mathematical model of the probability structure of the multiple comparisons. The Student-Newman-Keuls Test is less conservative than the Tukey Test because it controls errors among tests of k means, while the Tukey Test controls the errors of all comparisons simultaneously. Because it is less conservative, it is more likely to determine that a give differences is statistically significant. The Student-Newman-Keuls Test is usually more sensitive than the Bonferroni t-test, and is only available for all pairwise comparisons.

21. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Dunnett Test Dunnett's test is the analog of the Student-Newman-Keuls Test for the case of multiple comparisons against a single control group. It is conducted similarly to the Bonferroni t-test, but with a more sophisticated mathematical model of the way the error accumulates in order to derive the associated table of critical values for hypothesis testing. This test is less conservative than the Bonferroni Test, and is only available for multiple comparisons vs. a control.

22. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Bonferroni Test The Bonferroni t-test performs pairwise comparisons with paired t-tests. The P values are then multiplied by the number of comparisons that were made. It can perform both all pairwise comparisons and multiple comparisons vs. a control, and is the most conservative test for both each comparison type. For less conservative all pairwise comparison tests, see the Tukey and the Student-Newman-Keuls tests, and for the less conservative multiple comparison vs. a control tests, see the Dunnett's Test.

23. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Fisher LSD Test Fisher's Least Significant Difference (LSD) Test is the least conservative of the all-pairwise comparison tests. Unlike the Tukey and Student-Newman-Keuls tests, it controls the error rate of individual comparisons and does not control the family error rate, where the "family" is the whole set of comparisons. Because of this it is not recommended.

24. 【多母體檢定 Compare Many Groups】One Way ANOVA檢定方法Duncan's Multiple Range Test The Duncan's Test is the same way as the Tukey and the Student-Newman-Keuls tests, except that it is less conservative in determining whether the difference between groups is significant by allowing a wider range for error rates. Although it has a greater power to detect differences than the Tukey and the Student-Newman-Keuls tests, it has less control over the Type 1 error rate, and is, therefore, not recommended.

25. 【多母體檢定 Compare Many Groups】Two Way ANOVA 雙因子變異數分析 (服從常態分配) 例: 三種藥物藥效比較,也比較男性與女性 即因子有兩個 1.不同藥物 2.不同性別 3.也可有交互作用項 資料呈現方式如下

26. 【多母體檢定 Compare Many Groups】Two Way ANOVA 資料呈現方式

27. 【多母體檢定 Compare Many Groups】Two Way ANOVA 說明 Use a Two Way or Two Factor ANOVA (analysis of variance) when: You want to see if two of more different experimental groups are affected by two different factors which may or may not interact. Samples are drawn from normally distributed populations with equal variances. If you want to consider the effects of only one factor on your experimental groups, use the One Way ANOVA. If you are considering the effects of three factors on your experimental graphs, use the Three Way ANOVA.SigmaPlot has no equivalent nonparametric two or three factor comparison for samples drawn from a non-normal population. If your data is non-normal, you can transform the data to make them comply better with the assumptions of analysis of variance using Transform menu commands. If the sample size is large, and you want to do a nonparametric test, use the Transforms menu Rank command to convert the observations to ranks, then run a Two or Three Way ANOVA on the ranks.

28. 【多母體檢定 Compare Many Groups】Two Way ANOVA檢定方法 Holm-Sidak Tukey Student-Newman-Keuls Dunnett Bonferroni Fisher LSD Duncan's Multiple Range

29. 【多母體檢定 Compare Many Groups】Three Way ANOVA 三因子變異數分析 (服從常態分配) 例: 三種藥物藥效比較,也比較男性與女性,也比較不同醫院 即因子有三個 1.不同藥物 2.不同性別 3.不同醫院 4.也可有交互作用項 資料呈現方式如下

30. 【多母體檢定 Compare Many Groups】Three Way ANOVA 資料呈現方式

31. 【多母體檢定 Compare Many Groups】Three Way ANOVA說明 Use a Three Way or three factor ANOVA (analysis of variance) when: You want to see if two of more different experimental groups are affected by three different factors which may or may not interact. Samples are drawn from normally distributed populations with equal variances. or a Two Way ANOVA.SigmaPlot has no equivalent nonparametric three factor comparison for samples drawn from a non-normal population. If your data is non-normal, you can transform the data to make them comply better with the assumptions of analysis of variance using Transforms menu commands. If the sample size is large, and you want to do a nonparametric test, use the Transforms menu Rank command to convert the observations to ranks, then run a Three Way ANOVA on the ranks.

32. 【多母體檢定 Compare Many Groups】Three Way ANOVA檢定方法 Holm-Sidak Tukey Student-Newman-Keuls Dunnett Bonferroni Fisher LSD Duncan's Multiple Range

33. 【多母體檢定 Compare Many Groups】ANOVA on Rank Kruskal-Wallis One Way Analysis of Variance on Ranks 單因子變異數分析 (不服從常態分配) 為無母數檢定 比較中位數 例: 三種藥物藥效比較,但不服從常態分配 資料呈現方式如下

34. 【多母體檢定 Compare Many Groups】ANOVA on Rank資料呈現方式如下

35. 【多母體檢定 Compare Many Groups】ANOVA on Rank 說明 Use a Kruskal-Wallis ANOVA (analysis of variance) on Ranks when: You want to see if three or more different experimental groups are affected by a single factor. Your samples are drawn from non-normal populations or do not have equal variances. If you know that your data were drawn from normal populations with equal variances, use One Way ANOVA.When there are only two groups to compare, do a Mann-Whitney Rank Sum Test. There is no two or three factor test for non-normal populations; however, you can transform your data using Transform menu commands so that it fits the assumptions of a parametric test. . Note: If you selected normality testing in the Options for ANOVA on Ranks dialog box to perform an ANOVA on Ranks on a normal population, SigmaPlot informs you that the data is suitable for a parametric test, and suggests a One Way ANOVA instead.

36. 【多母體檢定 Compare Many Groups】ANOVA on Rank 檢定方法

37. 【前後檢定 Before and After】種類

38. 【前後檢定 Before and After】paired t-test 雙母體t檢定 paired t-test 服從常態分配 例: 新藥效用檢定,服藥前與服藥後之檢定 資料呈現方式如下

39. 【前後檢定 Before and After】paired t-test資料呈現方式

40. 【前後檢定 Before and After】paired t-test 說明 The Paired t-test is a parametric statistical method that assumes the observed treatment effects are normally distributed. It examines the changes which occur before and after a single experimental intervention on the same individuals to determine whether or not the treatment had a significant effect. Examining the changes rather than the values observed before and after the intervention removes the differences due to individual responses, producing a more sensitive, or powerful, test. Use Paired t-test when: You want to see if the effect of a single treatment on the same individual is significant. The treatment effects (i.e., the changes in the individuals before and after the treatment) are normally distributed. If you know that the distribution of the observed effects are non-normal, use the Wilcoxon Signed Rank Test. If you are comparing the effect of multiple treatments on the same individuals, do a Repeated Measures Analysis of Variance.

41. 【前後檢定 Before and After】Signed Rank Test Wilcoxon Signed Rank Test 符號檢定 例: 病人服藥前與服藥後之傷口長度資料 不服從常態分配 資料呈現方式如下

42. 【前後檢定 Before and After】Signed Rank Test 資料呈現方式

43. 【前後檢定 Before and After】Signed Rank Test說明 The Signed Rank Test is a nonparametric procedure which does not require assuming normality or equal variance. Use a Signed Rank Test when: You want to see if the effect of a single treatment on the same individual is significant. The treatment effects are not normally distributed with the same variances. If you know that the effects are normally distributed, use the Paired t-test. When there are multiple treatments to compare, do a Friedman Repeated Measures ANOVA on Ranks. Note: Depending on your Signed Rank Test option settings, if you attempt to perform a Signed Rank Test on a normal population, SigmaPlot suggests that the data can be analyzed with the more powerful Paired t-test instead.

44. 【重複測量 Repeated Measures】種類

45. 【重複測量 Repeated Measures】One Way ANOVA Repeated Measures 單因子變異數分析 重複測量 (服從常態分配) 使用目的：比較同一群體三個(含)以上的平均數的差異。 使用時機：每個受試者都有三次(含)以上的處理 例: 減肥藥A,B,C 小明小英小華吃完 減肥藥A後量一次,經過一各月後吃減肥藥B後量一次,經過一各月後吃減肥藥C後量一次 此種稱為 單因子變異數分析 重複測量

46. 【重複測量 Repeated Measures】One Way ANOVA Repeated Measures資料呈現方式

47. 【重複測量 Repeated Measures】One Way ANOVA Repeated Measures 說明 Use a one way or one factor repeated measures ANOVA (analysis of variance) when: You want to see if a single group of individuals was affected by a series of experimental treatments or conditions. Only one factor or one type of intervention is considered in each treatment or condition. The treatment effects are normally distributed with the same variances. If you know that the treatment effects are not normally distributed, use the Friedman Repeated Measures ANOVA on Ranks. If your want to consider the effects of an additional factor on your experimental treatments, use Two Way Repeated Measures ANOVA. When there is only a single treatment, you can do a Paired t-test (depending on the type of results you want). Note:Depending on your One Way Repeated Measures ANOVA options settings if you attempt to perform an ANOVA on a non-normal population, SigmaPlot informs you that the data is unsuitable for a parametric test, and suggest the Friedman ANOVA on Ranks instead.

48. 【重複測量 Repeated Measures】One Way ANOVA Repeated Measures檢定方法

49. 【重複測量 Repeated Measures】Two Way ANOVA Repeated Measures 雙因子變異數分析 重複測量 (服從常態分配) 使用目的：比較同一群體三個(含)以上的平均數的差異。 使用時機：每個受試者都有三次(含)以上的處理 例: 減肥藥A,B,C 也比較男性與女性 小明小英小華吃完 減肥藥A後量一次,經過一個月後吃減肥藥B後量一次,經過一個月後吃減肥藥C後量一次

50. 【重複測量 Repeated Measures】Two Way ANOVA Repeated Measures資料呈現方式