VIF quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity (See test 1 for numerical example)
√3970 × √730 - √2400 = ?
9.95% of 1299.99 + 19.95 × 17.05 - 299.99 = ?
A certain sum of money invested at R% p.a. fetches a compound interest (compounded annually) of 1620 and simple interest of Rs.1500 at the end of 2 year...
10.232 + 19.98% of 619.99 = ? × 6.99
(660.05) ÷ 120.04% of (55.022/2.24) = (? ÷ 10.02)
486, 162, 51, 18, 6, 2
657.94 + 335.21 - 211.09 - 82.30 = ?
11.06 2 – 7.12 × 4.88 + 9.96 = 12.22 × ?
{1722.95 + 5.05 × 648.08 – (2728.06 ÷ 22.05)} = ?