After understanding how to perform data cleaning in JMP, start basic statistical analysis. In this article and the following articles, we will introduce the analysis process of linear regression one by one.
Before performing linear regression multivariate analysis, it is often necessary to conduct univariate analysis first. However, the single factor analysis of most of the commonly used software operations is cumbersome at present, and the single factor analysis of multiple independent variables needs to be replaced continuously to repeat the operation.
However, in JMP The single factor analysis of all independent variables can be realized in one time in the software. Today's article is to share how to use JMP Complete a one-time multivariate univariate analysis.
Why do linear regression?
Compared with the univariate analysis with only one group variable described above, multivariate analysis is more common in practice, because most outcomes are affected by more than one factor. For example, comparing the efficacy of two different drugs in treating diabetes, even if there is a difference in the efficacy between the two groups, it may not be attributed to drug factors, because many factors such as BMI, blood pressure, diet, etc. will also affect the effect of drug treatment at the same time, if these If the factors are unbalanced between the two groups, it will affect the evaluation effect of the drug. Therefore, we often use multivariate analysis in practice.
We usually say that "correcting" the influence of factors such as age and gender, or that smoking is an "independent prognostic factor" for lung cancer, these refer to multivariate analysis. Linear regression is often used in multivariate analysis of quantitative outcomes, and is mainly used to describe the linear relationship between one or more independent variables and quantitative outcomes. When we want to explore risk factors for a quantitative outcome, we usually consider linear regression first, and then consider other alternative methods if linear regression is not suitable.
Univariate Analysis of Linear Regression
The so-called single factor analysis, that is, every time you do a regression, you first put in 1 independent variables, and observe the relationship between each independent variable and the dependent variable.
At present, when many statistical softwares do single-factor analysis, they can only put in one independent variable, and then make a result; if you want to see the effect of another independent variable, you need to go back and do it again.
Imagine if there are 20 independent variables, you need to do the same work 20 times back and forth. This process is very tedious and time-consuming. JMP The software provides a tool to realize the single factor analysis of all independent variables at one time .
Take the data in Figure 1 as an example to explore the possible influencing factors of the physical health score.
Figure 1 Sample data (partial display)
In JMP, click the "Analyze" menu → "Fit Y by X" (Figure 2), and in the pop-up dialog box, import the physical health score into "Y, response", and import all independent variables into "X, factor" ( image 3).
Figure 2 Schematic diagram of the operation of "fitting Y with X" Step 1
Figure 3 The second step of the operation diagram of "fitting Y with X"
The default output results are shown in Figure 4 (only 3 independent variables are shown, and the rest are omitted), and only the scatter plot of each independent variable and dependent variable is presented on the page.
Figure 4 Output of "Fit Y by X"
If you want to View the test results of the univariate analysis for each independent variable , you need to perform some subsequent operations in JMP. It should be noted that the operation steps are different for continuous variables and categorical variables.
continuous variable
For continuous variables, take age as an example, click the red down arrow on the left side of "Binary Fitting" in the result interface → select "Fitting Line" (Figure 5).
Figure 5 Schematic diagram of single factor analysis of continuous variables
The output result is shown in Figure 6. It can be seen from the figure that there is a negative linear relationship between age and physical health score. From the statistical test results, the linear relationship between age and physical health score is statistically significant at the test level of 0.05 (P=0.0275).
Figure 6 Single factor analysis results of continuous variables
Categorical variables
For categorical variables, take alcohol drinking as an example, click the red down arrow on the left side of "...Single Factor Analysis" → select "Analysis of Means/Analysis of Variations/Merged t" (Figure 7).
Figure 7 Schematic diagram of univariate analysis of categorical variables
The output results are shown in Figure 8. The results of the t-test and the single-factor ANOVA are also given in the results, and the results of the two are consistent. The results showed that the relationship between alcohol consumption and physical health score was not statistically significant (P=0.8871).
Figure 8 Single factor results of categorical variables
Seeing this, you may ask: In the case of many independent variables, each independent variable must operate "fitting line" or "mean/variation analysis/merged t", will the process be cumbersome?
JMP There is a convenient way to solve this problem in , which can display the statistical test results of all independent variables very quickly .
- for For continuous variables, first press ctrl key , and then click the red down arrow on the left side of "Binary Fitting" of any independent variable → select " Fitting line ”, which can realize the output of univariate analysis results of all continuous variables.
- for Categorical variables ,same with press first ctrl key , then click the red down arrow to the left of any categorical variable, choose " average / Analysis of variance / combined t " , which can realize the output of univariate analysis results of all categorical variables.
The above is the single factor analysis based on JMP to achieve all independent variables at one time shared today. Then we will introduce how to carry out multivariate analysis of linear regression in JMP. Stay tuned!
>> Learn more about JMP Multivariate Analysis
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Original link: https://mp.weixin.qq.com/s/uHau9nfIKPAmk9YaPoAb5Q
