colspan="5" | |- style="text-align:right;" ! style="padding-left:0.5em; text-align:left;" | Parameter ! style="padding-left:0.5em;" | Value ! style="padding-left:0.5em;" | Std error ! style="padding-left:0.5em;" | t-statistic ! style="padding-left:0.5em;" | p-value |- | colspan="5" | |- style="text-align:right;" | style="text-align:left;" | | 128.8128 || 16.3083 || 7.8986 || 0.0000 |- style="text-align:right;" | style="text-align:left;" | | –143.1620 || 19.8332 || –7.2183 || 0.0000 |- style="text-align:right;" | style="text-align:left;" | | 61.9603 || 6.0084 || 10.3122 || 0.0000 |- | colspan="5" | |- | R2 || style="text-align:right;" | 0.9989 | colspan="2" | S.E. of regression || style="text-align:right;" | 0.2516 |- | Adjusted R2 || style="text-align:right;" | 0.9987 | colspan="2" | Model sum-of-sq. || style="text-align:right;" | 692.61 |- | Log-likelihood || style="text-align:right;" | 1.0890 | colspan="2" | Residual sum-of-sq. || style="text-align:right;" | 0.7595 |- | Durbin–Watson stat. || style="text-align:right;" | 2.1013 | colspan="2" | Total sum-of-sq. || style="text-align:right;" | 693.37 |- | Akaike criterion || style="text-align:right;" | 0.2548 | colspan="2" | F-statistic || style="text-align:right;" | 5471.2 |- | Schwarz criterion || style="text-align:right;" | 0.3964 | colspan="2" | p-value (F-stat) || style="text-align:right;" | 0.0000 |} In this table: The Value column gives the least squares estimates of parameters βj The Std error column shows standard errors of each coefficient estimate: The t-statistic and p-value columns are testing whether any of the coefficients might be equal to zero. The t-statistic is calculated simply as . If the errors ε follow a normal distribution, t follows a Student-t distribution. Under weaker conditions, t is asymptotically normal. Large values of t indicate that the null hypothesis can be rejected and that the corresponding coefficient is not zero. The second column, p-value, expresses the results of the hypothesis test as a significance level. Conventionally, p-values smaller than 0.05 are taken as evidence that the population coefficient is nonzero. R-squared is the coefficient of determination indicating goodness-of-fit of the regression. This statistic will be equal to one if fit is perfect, and to zero when regressors X have no explanatory power whatsoever. This is a biased estimate of the population R-squared, and will never decrease if additional regressors are added, even if they are irrelevant. Adjusted R-squared is a slightly modified version of , designed to penalize for the excess number of regressors which do not add to the explanatory power of the regression. This statistic is always smaller than , can decrease as new regressors are added, and even be negative for poorly fitting models: