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from sklearn.base import TransformerMixin class DataFrameImputer(TransformerMixin): def fit(self, X, y=None): self.fill = pd.Series([X[c].value_counts().index[0] if X[c].dtype == np.dtype('O') else X[c].median() for c in X], index=X.columns) return self def transform(self, X, y=None): return X.fillna(self.fill)

A 95% confidence interval does not mean that for a given realised interval calculated from sample data there is a 95% probability the population parameter lies within the interval, nor that there is a 95% probability that the interval covers the population parameter.^{[11]} Once an experiment is done and an interval calculated, this interval either covers the parameter value or it does not, it is no longer a matter of probability. The 95% probability relates to the reliability of the estimation procedure, not to a specific calculated interval.^{[12]} Neyman himself (the original proponent of confidence intervals) made this point in his original paper:^{[3]}

If repeated samples were taken and the 95% confidence interval computed for each sample, 95% of the intervals would contain the population mean. Naturally, 5% of the intervals would not contain the population mean.

For instance waterway tonnage, as tracked by the U.S. Army Corps of Engineers Navigation Data Center, indicates the monthly tonnage volume of goods traveling across the ocean and other major bodies of water, including the Great Lakes region. Sources such as the Association of American Railroads (AAR) Weekly Rail Traffic Summary provide statistics on the percent change in monthly rail carloads and intermodal units.
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