The Ljung-Box test is a test for significant autocorrelation in a stationary time series. For stationary time series the joint distribution of Xi and Xj for the series (in your case the residual series) only depends on the time difference i-j. This difference is called the lag. The correlation between Xi and Xj over all lags i-j is called the autocorrelation function. Tests like Ljung-Box are testing to see if one or more of the lagged correlations is significantly different from 0. The term fitdf stands for degrees of freedom for the fit. The statistic involves several lagged correlation estimates and has a chi square ditribution with l degrees of freedom under the null hypothesis that none of the correlations differ from 0 where l is the number of lagged correlation estimates used in the test statistic. It is most commonly used to check the adequacy of an ARIMA model by testing the model residuals for autocorrelation. Rejecting the null hypothesis would be an indication of model inadequacy.

The Ljung-Box test is a test for significant autocorrelation in a stationary time series. For stationary time series the joint distribution of $X_i$ and $X_j$ for the series (in your case the residual series) only depends on the time difference $i-j$. This difference is called the lag. The correlation between $X_i$ and $X_j$ over all lags $i-j$ is called the autocorrelation function. Tests like Ljung-Box are testing to see if one or more of the lagged correlations is significantly different from 0. The term fitdf stands for degrees of freedom for the fit. The statistic involves several lagged correlation estimates and has a chi square distribution with l degrees of freedom under the null hypothesis that none of the correlations differ from 0 where l is the number of lagged correlation estimates used in the test statistic. It is most commonly used to check the adequacy of an ARIMA model by testing the model residuals for autocorrelation. Rejecting the null hypothesis would be an indication of model inadequacy.