{"id":2592,"date":"2024-10-09T09:46:08","date_gmt":"2024-10-09T07:46:08","guid":{"rendered":"https:\/\/clooma.ai\/?post_type=guide-dutilisateur&p=2592"},"modified":"2024-10-23T16:28:41","modified_gmt":"2024-10-23T14:28:41","slug":"04-xy","status":"publish","type":"guide-dutilisateur","link":"https:\/\/clooma.ai\/en\/users-guide\/04-xy\/","title":{"rendered":"XY"},"content":{"rendered":"

Ellistat Data Analysis<\/a> allows you to carry out an XY correlation study, to measure and analyze the relationship between two quantitative variables. This study determines whether and how the variables vary together. Here's a guide to carrying out a correlation study between two quantitative variables.<\/p>\n\n\n\n

The links below contain the data used for the XY correlation study proposed by Ellistat Data Analysis. Let's take the following data as an example:<\/p>\n\n\n\n

Independent Data \ud83c\uddfa\ud83c\uddf8\/ Donn\u00e9es ind\u00e9pendantes\ud83c\uddeb\ud83c\uddf7 <\/a><\/p>\n\n\n\n

Example 1: Find the correlation between a response Y and a factor X :<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
    \n
  • Place quantitative data in the grid with two columns X and Y. In the example, we want to find the correlation between Y=\"strength\" and X=\"delta\".<\/li>\n\n\n\n
  • Click on the \"Inferential statistics\" menu.<\/li>\n\n\n\n
  • In the zone 1<\/strong>Select the \"Force\" column in Y and the \"Delta\" column in X.<\/li>\n\n\n\n
  • In zone 2,<\/strong> select your data type. By default, if the 2 columns selected contain quantitative values, Ellistat will plot the correlation curve between Y and X. In addition to the XY sub-menu, you can also choose the \"Proportion\", \"Population\" or correlation sub-menus \ud83d\udcdd: choose \"\" XY<\/li>\n\n\n\n
  • In zone 3<\/strong>to obtain a graph: the scatterplot shows the values of Y for values X .<\/li>\n\n\n\n
  • In the zone 4<\/strong>the following elements are available:\n
      \n
    • the linear regression model of Y as a function of X, of the type Y= aX+b<\/li>\n\n\n\n
    • The coefficient of determination R\u00b2, is a statistical measure used to assess the predictive quality of a linear regression model. It indicates the proportion of the variance of the dependent variable that is explained by the variance of the independent variable.<\/li>\n\n\n\n
    • the P-value, which characterizes the degree of significance of the impact of factor X on response Y. In other words, it helps determine whether the results observed are statistically significant and whether the regression coefficients are different from zero.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
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