Explain the correlation and sample regression line or Goodness of fit test
correlation means a linear fit between data and a model, it determines if the data grows linearly as the model grows in time or just a variable, sample regresion line is usually to describe that the data behaves like Y=aX+b using tools to provide a and b from data x,y. goodness of fit is a number that calculates the error between data and the model..usually moves from 0 to 1. being 1 complete fit or just null error.
Correlation measures the strength and direction of a linear relationship between two variables, while a sample regression line represents the best-fitting line through data points, predicting the dependent variable based on changes in the independent variable within a given sample.
Correlation indicates how strongly two variables are related, while a sample regression line is a statistical tool that models and predicts the dependent variable’s value based on the independent variable’s value within sample data.
Correlation shows the strength and direction of a linear relationship between two variables, while a sample regression line estimates the expected value of a dependent variable based on an independent variable within a sample, offering predictive and analytical insights.
Correlation quantifies the linear association between two variables, while a sample regression line is a mathematical representation that models and predicts the relationship, allowing analysts to estimate how one variable influences another within a data sample.
Correlation measures the strength and direction of a linear relationship between two variables, while a sample regression line predicts values of one variable based on another using a best-fit line derived from sample data.
Correlation indicates how two variables move together—positively, negatively, or not at all—while a sample regression line provides a statistical model to predict one variable’s value based on another, visualized as a line of best fit through data points.
Correlation is all about finding the most accurate numerical value to describe the connection between different values, while regression calculates quantitative measures of a random variable with fixed variables. Overall, these two methods help provide useful insights into data analysis
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