Question

Q: When time series exhibit a trend the first differences should be used to fit a ARIMA model.

True or False and Why?

Q: Randomized experimental designs are considered the gold standard because the influence of confounders on the treatment effect estimation is limited through the process of random selection into treatment group and control group, so it leads to an unbiased estimation of the treatment effect.

True or False and Why?

Answer 1

1) True

because:-

The first (and most important) step in fitting an ARIMA model is the determination of the order of differencing needed to stationarize the series. Normally, the correct amount of differencing is the lowest order of differencing that yields a time series which fluctuates around a well-defined mean value and whose autocorrelation function (ACF) plot decays fairly rapidly to zero, either from above or below. If the series still exhibits a long-term trend, or otherwise lacks a tendency to return to its mean value, or if its autocorrelations are are positive out to a high number of lags (e.g., 10 or more), then it needs a higher order of differencing.

2)

False

because :-

Randomized double blind placebo control (RDBPC) studies are considered the “gold standard” of epidemiologic studies. RDBPC studies remain the most convincing research design in which randomly assigning the intervention can eliminate the influence of unknown or immeasurable confounding variables that may otherwise lead to biased and incorrect estimate of treatment effect.