public Statistics(IEnumerable<double> values) { list = values.ToList(); N = list.Count; if (N == 0) throw new InvalidOperationException("StatSummary: Sequence contains no elements"); list.Sort(); if (N == 1) Q1 = Median = Q3 = list[0]; else { Func<IList<double>, double> getMedian = x => x.Count % 2 == 0 ? (x[x.Count / 2 - 1] + x[x.Count / 2]) / 2 : x[x.Count / 2]; Median = getMedian(list); Q1 = getMedian(list.Take(N / 2).ToList()); Q3 = getMedian(list.Skip((N + 1) / 2).ToList()); } Min = list.First(); Mean = list.Average(); Max = list.Last(); InterquartileRange = Q3 - Q1; LowerFence = Q1 - 1.5 * InterquartileRange; UpperFence = Q3 + 1.5 * InterquartileRange; Outliers = list.Where(IsOutlier).ToArray(); StandardDeviation = N == 1 ? 0 : Math.Sqrt(list.Sum(d => Math.Pow(d - Mean, 2)) / (N - 1)); StandardError = StandardDeviation / Math.Sqrt(N); ConfidenceInterval = new ConfidenceInterval(Mean, StandardError); Percentiles = new PercentileValues(list); }
public Statistics(IEnumerable <double> values) { list = values.Where(d => !double.IsNaN(d)).ToList(); N = list.Count; if (N == 0) { throw new InvalidOperationException("Sequence of values contains no elements, Statistics can't be calculated"); } list.Sort(); if (N == 1) { Q1 = Median = Q3 = list[0]; } else { Func <IList <double>, double> getMedian = x => x.Count % 2 == 0 ? (x[x.Count / 2 - 1] + x[x.Count / 2]) / 2 : x[x.Count / 2]; Median = getMedian(list); Q1 = getMedian(list.Take(N / 2).ToList()); Q3 = getMedian(list.Skip((N + 1) / 2).ToList()); } Min = list.First(); Mean = list.Average(); Max = list.Last(); InterquartileRange = Q3 - Q1; LowerFence = Q1 - 1.5 * InterquartileRange; UpperFence = Q3 + 1.5 * InterquartileRange; Outliers = list.Where(IsOutlier).ToArray(); Variance = N == 1 ? 0 : list.Sum(d => Math.Pow(d - Mean, 2)) / (N - 1); StandardDeviation = Math.Sqrt(Variance); StandardError = StandardDeviation / Math.Sqrt(N); Skewness = CalcCentralMoment(3) / StandardDeviation.Pow(3); Kurtosis = CalcCentralMoment(4) / StandardDeviation.Pow(4); ConfidenceInterval = new ConfidenceInterval(Mean, StandardError, N); Percentiles = new PercentileValues(list); }
public Statistics(IEnumerable <double> values) { OriginalValues = values.Where(d => !double.IsNaN(d)).ToArray(); SortedValues = OriginalValues.OrderBy(value => value).ToArray(); N = SortedValues.Count; if (N == 0) { throw new InvalidOperationException("Sequence of values contains no elements, Statistics can't be calculated"); } var quartiles = Quartiles.FromSorted(SortedValues); Min = quartiles.Min; Q1 = quartiles.Q1; Median = quartiles.Median; Q3 = quartiles.Q3; Max = quartiles.Max; InterquartileRange = quartiles.InterquartileRange; var moments = Moments.Create(SortedValues); Mean = moments.Mean; StandardDeviation = moments.StandardDeviation; Variance = moments.Variance; Skewness = moments.Skewness; Kurtosis = moments.Kurtosis; var tukey = TukeyOutlierDetector.FromQuartiles(quartiles); LowerFence = tukey.LowerFence; UpperFence = tukey.UpperFence; AllOutliers = SortedValues.Where(tukey.IsOutlier).ToArray(); LowerOutliers = SortedValues.Where(tukey.IsLowerOutlier).ToArray(); UpperOutliers = SortedValues.Where(tukey.IsUpperOutlier).ToArray(); outlierDetector = tukey; StandardError = StandardDeviation / Math.Sqrt(N); ConfidenceInterval = new ConfidenceInterval(Mean, StandardError, N); Percentiles = new PercentileValues(SortedValues); }
public Statistics(IEnumerable <double> values) { list = values.ToList(); N = list.Count; if (N == 0) { throw new InvalidOperationException("StatSummary: Sequence contains no elements"); } list.Sort(); if (N == 1) { Q1 = Median = Q3 = list[0]; } else { Func <IList <double>, double> getMedian = x => x.Count % 2 == 0 ? (x[x.Count / 2 - 1] + x[x.Count / 2]) / 2 : x[x.Count / 2]; Median = getMedian(list); Q1 = getMedian(list.Take(N / 2).ToList()); Q3 = getMedian(list.Skip((N + 1) / 2).ToList()); } Min = list.First(); Mean = list.Average(); Max = list.Last(); InterquartileRange = Q3 - Q1; LowerFence = Q1 - 1.5 * InterquartileRange; UpperFence = Q3 + 1.5 * InterquartileRange; Outliers = list.Where(IsOutlier).ToArray(); StandardDeviation = N == 1 ? 0 : Math.Sqrt(list.Sum(d => Math.Pow(d - Mean, 2)) / (N - 1)); StandardError = StandardDeviation / Math.Sqrt(N); ConfidenceInterval = new ConfidenceInterval(Mean, StandardError); Percentiles = new PercentileValues(list); }