Principal Component Analysis Xlstat License Of One
Each participant will leave with the software used and will have a license of one year of use.To help implement these methods in the workplace, a follow-up and support will be provided by the teacher for 4 weeks in order to respond to the learners' requests and to accompany them in a company: it is "learning in the flow of work ".At the end of the training participants will receive a certificate of participation issued by the House of TrainingUpgrade videos to consult before the face-to-face courseA synthetic course on Powerpoint serving as a framework for the course and made available to the learners after the trainingSoftware taught during the internship, made available and for a period of one year thereafter. This software will put into practice and build skills immediately and tangiblyRather short video sequences (of the order of 10 mns), each illustrating and serving to interpret a method. Pros: I am a heavy user of XLStat since 2013, and I really like how easy it is to manage and run the analysis once all the essential functions are part of excel, and the statistical tests are easily shown in a very well-organized menu.I usually conduct preference mappings, PCA, cluster analysis, ANOVAs (with all post hoc options), Penalty Analysis, Qui-square, and Panel Check analysis.This Data Mining course allows you to acquire the essentials of the keys to structure the data for analytical purposes and to appropriate the main methods of data analysis.Principal Component Analysis (PCA) Statistical Software. And Xlstat.com All Courses.
However, if you want to perform other analyses on the data, you may want to have at least 90% of the variance explained by the principal components. For descriptive purposes, you may only need 80% of the variance explained. The acceptable level depends on your application. Retain the principal components that explain an acceptable level of variance.
Scree plot The scree plot orders the eigenvalues from largest to smallest. For example, using the Kaiser criterion, you use only the principal components with eigenvalues that are greater than 1. Retain the principal components with the largest eigenvalues.
The third component has large negative associations with income, education, and credit cards, so this component primarily measures the applicant's academic and income qualifications. The second component has large negative associations with Debt and Credit cards, so this component primarily measures an applicant's credit history. Use the components in the steep curve before the first point that starts the line trend.Income 0.314 0.145 -0.676 -0.347 -0.241 0.494 0.018 -0.030Education 0.237 0.444 -0.401 0.240 0.622 -0.357 0.103 0.057Age 0.484 -0.135 -0.004 -0.212 -0.175 -0.487 -0.657 -0.052Residence 0.466 -0.277 0.091 0.116 -0.035 -0.085 0.487 -0.662Employ 0.459 -0.304 0.122 -0.017 -0.014 -0.023 0.368 0.739Savings 0.404 0.219 0.366 0.436 0.143 0.568 -0.348 -0.017Debt -0.067 -0.585 -0.078 -0.281 0.681 0.245 -0.196 -0.075Credit cards -0.123 -0.452 -0.468 0.703 -0.195 -0.022 -0.158 0.058In these results, first principal component has large positive associations with Age, Residence, Employ, and Savings, so this component primarily measures long-term financial stability.
0 kommentar(er)
