A correlation coefficient is the statistical measure that will tell us whether there is a relationship between our two variables of interest, and if there is one, how strong that relationship is. The value of th
It helps to examine how changes in the independent variables impact the dependent variable. By fitting a mathematical model to the data, regression allows us to make predictions or estimate values for the dependent variable. This is based on the values of the independent variables. It is widely ...
What does the coefficient of variation tell us, and how is it related to the Sharpe Ratio? What is a marketability risk premium? Why is it important to adjust projected cash flows for this risk? How might the size of a firm affect it...
Question: How can we explain the Lorenz Curve and how it is used to calculate the Gini Coefficient? What does the Gini Coefficient tell us? The Lorenz Curve and the GINI Coefficient: The Lorenz curve and the GINI coefficient are two co...
The closer the resemblance to a straight line of the scatter plot, the higher the strength of association. Numerically, the Pearson coefficient is represented the same way as a correlation coefficient that is used in linear regression, ranging from -1 to +1. A value of +1 is the result ...
What it doesn’t tell you is how one variable affects the other. Regression does. It quantifies how changes in one variable predict changes in another, giving you a mathematical formula for making predictions. This makes it useful when you're trying to understand the drivers behind key outcom...
A regression line is a straight line used in linear regression to indicate a linear relationship between one independent variable (on the x-axis) and one dependent variable (on the y-axis). Regression lines may be used to predict the value of Y for a given value of X....
Step 10: Extract the regression coefficient print(regressor.coef_) Step 11: Generate predictions print(y_pred) Step 12: Compare with actual values y_test Step 13: Assess model performance import numpy as np from sklearn import metrics
Not only does this call on teams to curate large amounts of quality data, it brings in many practical considerations. Storage, cleaning/transformation, processing, and general quality control all grow increasingly difficult as a data set gets larger. Computing power and infrastructure requirements: ...
The Regression Coefficient is the constant ‘b’ in the regression equation that tells about the change in the value of dependent variable corresponding to the unit change in the independent variable