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Outliers: By introducing extreme values that deviate significantly from the rest of the data, outliers can exert a disproportionate influence on the regression line.

False data: Introducing false or fabricated data points deliberately can disrupt the integrity of linear regression analysis by introducing artificial patterns or trends, leading to biased estimates.

Data manipulation: Manipulating the values of existing data points or intentionally excluding certain data points can also influence linear regression outcomes.

Data


Equation ( y = mx + b ):
Slope:
0
Y-intercept:
0
User Data Points:
0
Bot Data Points:
0