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Right approach if the data satisfy the prerequisite test parameters, the choice of two-factor factorial design analysis of variance of quantitative data processing; if can not satisfy the prerequisite test parameters should be accordingly the variable transformation, the transformed to meet the prerequisites of data to conduct two-factor factorial design analysis of variance of quantitative data.
Data for the same purpose or the same analysis, the statistical methods used in different, sometimes not exactly the same conclusions reached, and even sometimes come to opposite conclusions. Therefore, the correct and reasonable selection of statistical analysis is essential [1]. How, then, quantitative data can be properly achieved statistical analysis? The key is two things: First, check whether the data meet parametric test of quantitative prerequisite; Second, quantitative data corresponding to the right discrimination test (experiment) design type [2]. Through \quantitative data. Of course, the statistics raise the level of theoretical knowledge can not only rely on the study, also need to integrate theory with practice, through a large number of \In this paper, some common test (experiment) design of quantitative information on the misuse of statistics displayed by an example problem, and for error discrimination, given the right approach, to better help the researchers improve the level of practical application of statistics.
Group GMCSA (1 × 105 bone marrow cells to produce granulocyte monocyte progenitor cell colony number) Time: 1 day 3 days 7 days 5 days in control group 9.80 ± 3.2716. 00 ± 6.1617.00 ± 1.0024.80 ± 6.69 catechin group 33.80 ± 9.0461.40 ± 9.5062.60 ± 6.5069.40 ± 6.11

Table 2 Influence of catechins on GMCSA
Keywords: statistics; medicine; statistical analysis; interaction; quantitative data
Analysis of this table
(X ± s)
Analysis of the data
2.2 recognize the type experimental design in biomedical experiments and apply more of the experimental design is a single factor κ (κ ≥ 3) the level of design, factorial designs and repeated measurement design and so on. In addition, the pilot (experimental) conditions, limitations or lack of statistical knowledge researchers, non-balanced portfolio of multi-factor test (experimental) applications is greater. Readers can be combined,[link widoczny dla zalogowanych], \
involving two factors: \The former has two levels, which has four levels. Since the researchers at each time point,[link widoczny dla zalogowanych], 3 animals were sacrificed, and the \All experimental conditions, the level of the two comprehensive combination of various factors, and each experimental condition were conducted three times independently repeated experiments, and no professional basis for two experimental factors can be considered the result of the existence of primary and secondary observation divided Therefore information should be for the two factors factorial design quantitative data. Several times with the t test analysis of this data will not only increase the probability of committing false-positive errors, and there was an interaction between the two factors that may draw the wrong conclusions.
Analysis shows, according to researchers in the testing process,[link widoczny dla zalogowanych], before and after treatment in the treatment of 24,48,72 h time points of 4 repeated measurements on the same subjects fasting gastric juice pH, Therefore, \In fact, the expression of Table 3 is the performance of this data type, the reader may mistakenly think 4 time points independently of each other, and treat it as irrelevant to four \t-test designed to quantitative data analysis, do so because there is no clear reason is the nature of the information. In fact, four time points is a factor of four levels, this factor is the \Therefore, we can improve on Table 3, gives the standard expression of the data, which clearly shows the design of data types and their nature. Table 4. It can be seen,[link widoczny dla zalogowanych], the data should have a two-factor repeated measures design of quantitative information.
factors measured value measured value of the control group, tP number of patients body mass index 9921.29 ± 3.3829721.71 ± 3.701.043> 0.05 a day heavy manual labor time (h) 1002.17 ± 3.222961. 86 ± 2.810.860> 0.05 cigarette consumption (sticks / d) 984.35 ± 8.44293 4.61 ± 10.840.248> 0.05 white wine (times / week) 801.65 ± 3.892381.93 ± 4.330.538> 0.05 rice wine (times / week) 831.57 ± 3.472361.84 ± 4.170.583> 0.05 beer (times / week) 800.98 ± 2.612421.83 ± 3.992.2100.028 body mass index = body weight (kg) / height 2 (m2)
(X ± s, n = 3)
2 instances of discrimination
Number of patients in case group
Correct information distribution practices on the normal test, if the case group and control group in one or the values of some indicators do not meet the normal distribution, should be appropriate variable transformation or Wilcoxon rank sum test used for processing; if the case group and control group or some of the indicators in a consistent value on the normal distribution, you should also carry out test of homogeneity of variance between the two groups to meet the homogeneity of variance conditions, these indicators can be used on this or design into a set of quantitative data to analyze the t-test, otherwise the design was to be used as quantitative data t 'test to analyze.
1 Introduction
cross-heading \value the existence of significant differences. If these indicators between the link is not professional, you can separate analysis for each index, then for each indicator, this data is indeed designed for groups of quantitative data, but quantitative data using a group t-test design is appropriate to analyze it? Carefully observe the data in Table 1 was found except for body mass index, the case group and control group in the other indicators appear on the standard deviation of the values are greater than the mean phenomenon, so the data may not meet the normal distribution, so be used for all groups of indicators designed t-test to quantitative data analysis is inappropriate.
Example 2 For a researcher of catechin on the activity of hematopoietic growth factors stimulate biological effects, the choice of 24 rats, randomly divided into two groups: control group and the catechin group, 12 in each group. Control rats injected intraperitoneally 0.5 ml saline was injected intraperitoneally catechin group 2 mg / ml standard solution of catechin 0.5 ml. 1,3,5,7 days after the administration of 3 rats were killed to measure granulocyte-macrophage colony-stimulating factor activity (granulocytemacrophage colonystimulating activity, GMCSA). The results in Table 2. 1,3,5,7 by t test analysis of the two groups of rats were measured at day GMCSA whether statistically significant differences. I ask you: this analysis right?
Inspection if they want to by a number of indicators are used to predict the probability of femoral head necrosis, can be used unconditional multiple logistic regression analysis; if cases and controls in strict accordance with an important non-experimental factors (age or body mass) for 1:1 or m: n matching conditions can be used multiple logistic regression analysis. On this particular issue, and many times compared to single factor analysis, multiple logistic regression analysis, the conclusion is more credible, and more practical value.
Example 1 a researcher for the study of risk factors for femoral head necrosis, which provide the basis for the prevention of the disease. A case-control study, 100 patients with clinically diagnosed cases of femoral head necrosis in patients as a group, selected by 1:3 matching ratio with the case of the same sex, same age and same area of residence of non-femoral head necrosis (excluding severe heart , liver, lung, kidney disease) as the control group on the history of labor time and factors such as smoking, drinking research. The results in Table 1. The researchers designed by a group t test analysis of quantitative data this information, whether properly?

Common experimental design statistical analysis of quantitative data error Analysis


<div style=\Key words statistical analysis of the interaction of Medical Statistics quantitative data
Table 1, body mass index, alcohol consumption and other factors and the relationship between osteonecrosis
Example 3 a researcher to observe the omeprazole treatment of peptic ulcer and upper gastrointestinal bleeding in the acidic environment to improve the results, select the 80 cases of upper gastrointestinal bleeding due to peptic ulcer patients were randomly divided into two groups: treatment group and control group, all 40 cases. Omeprazole treatment group and control group with famotidine. Measured before treatment and after 24,[link widoczny dla zalogowanych],48,72 h fasting gastric juice pH. The results in Table 3. Statistical analysis using t test. Will: The t test analysis of the data on it?
2.1 Check condition of parametric test parametric test generally refers to a prerequisite for independence, normality and homogeneity of variance, of course, matching the design, repeated measurement design does not require quantitative data subjects (experimental) objects to meet the independence requirements. Normality requires quantitative data of each group were taken from the general normal distribution, homogeneity of variance requirements of the test (experimental) group factors of each level of the overall variance of the same; and for repeated measures design with quantitative data, but also check meets HuynhFeldt (HF) conditions, the ball must do test or treatment with hybrid model [3]. More articles related to topics：


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