/// <summary>
/// Return a set of simulated bootstrap statistics that form the bootstrap distribution for means via simulation given the original sample
/// </summary>
/// <param name="originalSample">The original bootstrap sample</param>
/// <param name="bootstrapSampleCount">The number of bootstrap samples collected to form the bootstrap distribution</param>
/// <returns></returns>
public static double[] SimulateSampleMeans(double[] originalSample, int bootstrapSampleCount)
{
double originalSampleMean = Mean.GetMean(originalSample);
double originalSampleStdDev = StdDev.GetStdDev(originalSample, originalSampleMean);
return(SimulateSampleMeans(originalSampleMean, originalSampleStdDev, originalSample.Length, bootstrapSampleCount));
}
/// <summary>
/// Return the correlation for observations (x_1, y_1), (x_2, y_2), ... (x_n, y_n), where n is the sample size
/// The correlation is computed as correlation(x, y) = sum_i((x_i - mu_x) * (y_i - mu_y)) / (sum_i((x_i - mu_x)^2) * sum_i((y_i - mu_y)^2))
/// which can also be written as n * sum_i((x_i - mu_x) * (y_i - mu_y) / (sigma_x * sigma_y))
/// where mu_x = sum_i(x_i) / n and sigma_x = sqrt(sum_i((x_i - mu_x)^2) / n)
/// </summary>
/// <param name="observations">The observations (x_1, y_1), (x_2, y_2), ... (x_n, y_n), where n is the sample size</param>
/// <returns>The correlation value for variable x and y</returns>
public double GetCorrelation(Tuple <double, double>[] observations)
{
int n = observations.Length;
double[] x = new double[n];
double[] y = new double[n];
for (int i = 0; i < n; ++i)
{
x[i] = observations[i].Item1;
y[i] = observations[i].Item2;
}
double mu_x = Mean.GetMean(x);
double mu_y = Mean.GetMean(y);
double sigma_x = StdDev.GetStdDev(x, mu_x);
double sigma_y = StdDev.GetStdDev(y, mu_y);
double sum = 0;
for (int i = 0; i < n; ++i)
{
sum += ((x[i] - mu_x) / sigma_x) * ((y[i] - mu_y) / sigma_y);
}
return(sum / n);
}
/// <summary>
/// Return the standard error of the sampling distribution given a random sample
/// </summary>
/// <param name="sample">The random sample given</param>
/// <returns>Standard error of a random sample, which is the standard deviation of the sample statistic normal distribution by CLT</returns>
public static double GetStandardError(double[] sample)
{
double sampleMean = Mean.GetMean(sample);
double sampleStdDev = StdDev.GetStdDev(sample, sampleMean);
return(GetStandardError(sampleStdDev, sample.Length));
}
/// <summary>
/// Get the confidence interval of a continuous variable for a random sample
/// </summary>
/// <param name="sample"></param>
/// <param name="confidence_level"></param>
/// <returns></returns>
public static double[] GetConfidenceInterval(double[] sample, double confidence_level, bool useStudentT = false)
{
double sampleMean = Mean.GetMean(sample);
double sampleStdDev = StdDev.GetStdDev(sample, sampleMean);
return(GetConfidenceInterval(sampleMean, sampleStdDev, sample.Length, confidence_level, useStudentT));
}
/// <summary>
/// Two-sided or one-sided test for whether statitics of two variables are equal in the true population, var1 and var2 are paired and dependent
///
/// Hypotheses are:
/// H_0: mu_var1 = mu_var2
/// H_1: mu_var1 != mu_var2
///
/// The hypotheses can be written as
/// H_0: mu_var1 - mu_var2 = 0
/// H_1: mu_var1 - mu_var2 != 0
///
/// By Central Limt Theorem:
/// sample_mean_var1 - sample_mean_var2 ~ N(0, SE), where null_value = 0 and SE is the standard error of the sampling distribution
///
/// p-value = (sample_mean is at least ||null_value-point_estimate|| away from the null_value) | mu = null_value)
/// </summary>
/// <param name="sample_for_paired_data">a random sample consisting data paired together, var1 and var2, var1 and var2 are not independent</param>
/// <param name="one_sided">True if the test is one-sided</param>
/// <param name="significance_level"></param>
/// <returns></returns>
public bool RejectH0_PairedData(Tuple <double, double>[] sample_for_paired_data, out double pValue, double significance_level = 0.05, bool one_sided = false, bool useStudentT = false)
{
int sample_size = sample_for_paired_data.Length;
double[] diff = new double[sample_size];
for (int i = 0; i < sample_size; ++i)
{
diff[i] = sample_for_paired_data[i].Item1 - sample_for_paired_data[i].Item2;
}
double point_estimate = Mean.GetMean(diff);
double null_value = 0;
double SE = StandardError.GetStandardError(diff);
double test_statistic = System.Math.Abs(point_estimate - null_value) / SE;
double percentile = 0;
if (sample_for_paired_data.Length < 30 || useStudentT) //if sample size is smaller than 30, then CLT for population statistics such as sample mean no longer holds and Student's t distribution should be used in place of the normal distribution
{
percentile = StudentT.GetPercentile(test_statistic, sample_for_paired_data.Length - 1);
}
else
{
percentile = Gaussian.GetPercentile(test_statistic);
}
pValue = (1 - percentile) * (one_sided ? 1 : 2);
return(pValue < significance_level);
}
/// <summary>
/// Return the confidence interval of the difference between two classes in terms of the proportion of SUCCESS in the population at a given confidence level
///
/// Note that each class should be a categorical variable with two levels : {SUCCESS, FAILURE}
/// Note that class 1 and class 2 are not paired or dependent
/// </summary>
/// <param name="p_hat1">point estimate of the proportion of SUCCESS in class 1</param>
/// <param name="p_hat2">point estimate of the proportion of SUCCESS in class 2</param>
/// <param name="n1">sample size in class 1</param>
/// <param name="n2">sample size in class 2</param>
/// <param name="confidence_level">The given confidence level</param>
/// <param name="useSimulation">Flag for whether simulation should be used instead of the normal distribution for proportion of SUCCESS</param>
/// <returns>The confidence interval of the difference between two classes in terms of the proportion of SUCCESS</returns>
public static double[] GetConfidenceInterval(double p_hat1, double p_hat2, int n1, int n2, double confidence_level, bool useSimulation = false, int simulationCount = 500)
{
bool shouldUseSimulation = useSimulation;
double p1 = (1 - confidence_level) / 2;
double p2 = 1 - p1;
if (!shouldUseSimulation && (p_hat1 * n1 < 10 || (1 - p_hat1) * n1 < 10 || p_hat2 * n2 < 10 || (1 - p_hat2) * n2 < 10))
{
shouldUseSimulation = true;
}
if (shouldUseSimulation)
{
double[] sim_sample1 = new double[simulationCount]; // this will follow a normal distribution based on CTL for proportion
double[] sim_sample2 = new double[simulationCount]; // this will follow a normal distribution based on CLT for proportion
int simulationSampleSize = System.Math.Max((int)System.Math.Max(10 / p_hat1, 10 / (1 - p_hat1)) * 2, (int)System.Math.Max(10 / p_hat2, 10 / (1 - p_hat2)) * 2);
for (int i = 0; i < simulationCount; ++i)
{
int successCount1 = 0;
int successCount2 = 0;
for (int j = 0; j < simulationSampleSize; ++j)
{
if (DistributionModel.GetUniform() <= p_hat1)
{
successCount1++;
}
if (DistributionModel.GetUniform() <= p_hat2)
{
successCount2++;
}
}
sim_sample1[i] = (double)(successCount1) / simulationSampleSize;
sim_sample2[i] = (double)(successCount2) / simulationSampleSize;
}
double sim_mu1 = Mean.GetMean(sim_sample1);
double sim_sigma1 = StdDev.GetStdDev(sim_sample1, sim_mu1);
double sim_mu2 = Mean.GetMean(sim_sample2);
double sim_sigma2 = StdDev.GetStdDev(sim_sample2, sim_mu2);
double sim_mud = sim_mu1 - sim_mu2;
double sim_SE = System.Math.Sqrt(sim_sigma1 * sim_sigma1 + sim_sigma2 * sim_sigma2);
return(new double[] { sim_mud + Gaussian.GetPercentile(p1) * sim_SE, sim_mud + Gaussian.GetQuantile(p2) * sim_SE });
}
else
{
double SE = System.Math.Sqrt((p_hat1 * (1 - p_hat1) / n1 + (p_hat2 * (1 - p_hat2)) / n2));
double pd_hat = p_hat1 - p_hat2;
return(new double[] { pd_hat + Gaussian.GetQuantile(p1) * SE, pd_hat + Gaussian.GetQuantile(p2) * SE });
}
}
/// <summary>
/// Two-sided or one-sided test for a single median
///
/// Given that:
/// H_0 : median = null_value
/// H_A : median != null_value
///
/// By Central Limit Theorem:
/// sample_median ~ N(mu, SE)
///
/// p-value = (sample_median is at least ||null_value - point_estimate|| away from the null_value) | median = null_value)
/// if(p-value < significance_level) reject H_0
/// </summary>
/// <param name="originalSample">The original sample</param>
/// <param name="bootstrapSampleCount"></param>
/// <param name="null_value"></param>
/// <param name="significance_level"></param>
/// <param name="one_sided"></param>
/// <returns></returns>
public static bool RejectH0_ForMedian(double[] originalSample, int bootstrapSampleCount, double null_value, out double pValue, double significance_level = 0.05, bool one_sided = false)
{
double[] bootstrapMedians = SimulateSampleMedians(originalSample, bootstrapSampleCount);
double bootstrap_mean = Mean.GetMean(bootstrapMedians);
double bootstrap_SE = StdDev.GetStdDev(bootstrapMedians, bootstrap_mean);
return(HypothesisTesting.RejectH0(bootstrap_mean, null_value, bootstrap_SE, originalSample.Length, out pValue, significance_level, one_sided));
}
/// <summary>
/// Return the standard error of the sampling distribution of the difference between two population statistics var1 and var2, assuming var1 and var2 are independent
/// </summary>
/// <param name="sample_for_var1">random sample for var1</param>
/// <param name="sample_for_var2">random sample for var2</param>
/// <returns>Standard error of a random sample, which is the standard deviation of the sample statistic normal distribution by CLT</returns>
public static double GetStandardError(double[] sample_for_var1, double[] sample_for_var2)
{
double mu_for_var1 = Mean.GetMean(sample_for_var1);
double mu_for_var2 = Mean.GetMean(sample_for_var2);
double sigma_for_var1 = StdDev.GetStdDev(sample_for_var1, mu_for_var1);
double sigma_for_var2 = StdDev.GetStdDev(sample_for_var2, mu_for_var2);
return(System.Math.Sqrt(sigma_for_var1 * sigma_for_var1 / sample_for_var1.Length + sigma_for_var2 * sigma_for_var2 / sample_for_var2.Length));
}
public static Dictionary <int, double> GetMeanWithinGroup(Dictionary <int, List <double> > groupSample)
{
Dictionary <int, double> means = new Dictionary <int, double>();
foreach (int grpId in groupSample.Keys)
{
means[grpId] = Mean.GetMean(groupSample[grpId]);
}
return(means);
}
/// <summary>
/// Pairwise comparison of group1 and group2
/// </summary>
/// <param name="group1">random sample from class 1</param>
/// <param name="group2">random sample from class 2</param>
/// <param name="anova">parameters obtained after ANOVA</param>
/// <returns>p-value = P(observed or more extreme values | H_0 is true)</returns>
public static double PairwiseCompare(List <double> group1, List <double> group2, ANOVA anova)
{
double x_bar1 = Mean.GetMean(group1);
double x_bar2 = Mean.GetMean(group2);
int n1 = group1.Count;
int n2 = group2.Count;
int null_value = 0;
double t = GetTStatistic(x_bar1, x_bar2, n1, n2, null_value, anova.MSE);
double pValue = GetPValue(t, anova.dfE);
return(pValue);
}
/// <summary>
/// Return the sum of squares group (SSG)
///
/// SSG measures the variability between groups
/// This is also known as explained variablity: deviation of group mean from overral mean, weighted by sample size
/// </summary>
/// <param name="groupedSample">The sample groupped based on the classes</param>
/// <returns></returns>
public static double GetSSG(Dictionary <int, List <double> > groupedSample, double grand_mean)
{
double SSG = 0;
foreach (int grpId in groupedSample.Keys)
{
List <double> group = groupedSample[grpId];
double group_mean = Mean.GetMean(group);
double group_size = group.Count;
SSG += group_size * (group_mean - grand_mean) * (group_mean - grand_mean);
}
return(SSG);
}
public static double[] GetConfidenceIntervalForMean(double[] originalSample, int bootstrapSampleCount, double confidence_level)
{
double[] bootstrapMeans = SimulateSampleMeans(originalSample, bootstrapSampleCount);
double bootstrap_mean = Mean.GetMean(bootstrapMeans);
double bootstrap_SE = StdDev.GetStdDev(bootstrapMeans, bootstrap_mean);
double p1 = (1 - confidence_level) / 2;
double p2 = 1 - p1;
double z1 = Gaussian.GetQuantile(p1);
double z2 = Gaussian.GetQuantile(p2);
return(new double[] { bootstrap_mean + z1 * bootstrap_SE, bootstrap_mean + z2 * bootstrap_SE });
}
/// <summary>
/// Return the sum of squares total
///
/// SST measures the total variability in the response variable
/// </summary>
/// <param name="totalSample">all the data points in the sample containing all classes</param>
/// <param name="grand_mean">The mean of all the data points in the sample containing all classes</param>
/// <returns>The sum of squares total, which measures p=o--i9i9</returns>
public static double GetSST(double[] totalSample, out double grand_mean)
{
grand_mean = Mean.GetMean(totalSample);
double SST = 0;
int n = totalSample.Length;
for (int i = 0; i < n; ++i)
{
double yd = totalSample[i] - grand_mean;
SST += yd * yd;
}
return(SST);
}
/// <summary>
/// Return the NormalTable distribution of population statistic (a*x + b*y) for correlated random variables x and y
/// </summary>
/// <param name="x">random sample for random variable x</param>
/// <param name="y">random sample for random variable y</param>
/// <param name="x_coefficient">a which is the coefficient of x</param>
/// <param name="y_coefficient">b which is the coefficient of y</param>
/// <param name="correlation">correlation between x and y</param>
/// <param name="result_mean">output mean for the a*x + b*y</param>
/// <param name="result_SE">output standard error for the a*x + b*y</param>
public static void Sum(double[] x, double[] y, int x_coefficient, double y_coefficient, double correlation, out double result_mean, out double result_SE)
{
result_mean = 0;
result_SE = 0;
double mean_x = Mean.GetMean(x);
double mean_y = Mean.GetMean(y);
double stddev_x = StdDev.GetStdDev(x, mean_x);
double stddev_y = StdDev.GetStdDev(y, mean_y);
result_mean = x_coefficient * mean_x + y_coefficient * mean_y;
result_SE = System.Math.Sqrt(System.Math.Pow(x_coefficient * stddev_x, 2) / x.Length + System.Math.Pow(y_coefficient * stddev_y, 2) / y.Length + 2 * correlation * x_coefficient * stddev_x * y_coefficient * stddev_y / System.Math.Sqrt(x.Length * y.Length));
}
/// <summary>
/// Return a set of simulated bootstrap statistics that form the bootstrap distribution for means via simulation given the original sample
/// </summary>
/// <param name="originalSampleMean">point estimate of sample mean from the original sample</param>
/// <param name="originalSampleStdDev">standard deviation of the original sample</param>
/// <param name="originalSampleSize">size of the original sample</param>
/// <param name="bootstrapSampleCount">The number of bootstrap samples collected to form the bootstrap distribution</param>
/// <returns></returns>
public static double[] SimulateSampleMeans(double originalSampleMean, double originalSampleStdDev, int originalSampleSize, int bootstrapSampleCount)
{
Gaussian distribution = new Gaussian(originalSampleMean, originalSampleStdDev);
double[] bootstrapMeans = new double[bootstrapSampleCount];
double[] bootstrapSample = new double[originalSampleSize];
for (int i = 0; i < bootstrapSampleCount; ++i)
{
for (int j = 0; j < originalSampleSize; ++j)
{
bootstrapSample[j] = distribution.Next();
}
bootstrapMeans[i] = Mean.GetMean(bootstrapSample);
}
return(bootstrapMeans);
}
/// <summary>
/// Return the confidence interval for proportion of SUCCESS in the population at a given confidence level given the sample proportion point estimate
/// </summary>
/// <param name="proportion">sample proportion point estimate</param>
/// <param name="sampleSize">sample size</param>
/// <param name="confidence_level"></param>
/// <returns>confidence interval for proportion of SUCCESS in the population at a given confidence level</returns>
public static double[] GetConfidenceInterval(double proportion, int sampleSize, double confidence_level, bool useSimulation = false, int simulationCount = 500)
{
double standard_error = StandardError.GetStandardErrorForProportion(proportion, sampleSize);
double p1 = (1 - confidence_level) / 2;
double p2 = 1 - p1;
int expected_success_count = (int)(proportion * sampleSize);
int expected_failure_count = (int)((1 - proportion) * sampleSize);
if (expected_failure_count < 10 || expected_success_count < 10 || useSimulation) //if np < 10 or n(1-p) < 10, then CLT for proportion no longer holds and simulation should be used in place of the normal distribution
{
double[] sampleProportions = new double[simulationCount];
int simulationSampleSize = (int)System.Math.Max(10 / proportion, 10 / (1 - proportion)) * 2;
for (int i = 0; i < simulationCount; ++i)
{
int successCount = 0;
for (int j = 0; j < simulationSampleSize; ++j)
{
if (DistributionModel.GetUniform() <= proportion)
{
successCount++;
}
}
sampleProportions[i] = (double)successCount / simulationSampleSize;
}
double proportion_mu = Mean.GetMean(sampleProportions);
double proportion_sigma = StdDev.GetStdDev(sampleProportions, proportion_mu);
return(new double[] { proportion_mu + Gaussian.GetPercentile(p1) * proportion_sigma, proportion_mu + Gaussian.GetQuantile(p2) * proportion_sigma });
}
else
{
double critical_value1 = Gaussian.GetQuantile(p1);
double critical_value2 = Gaussian.GetQuantile(p2);
double[] confidence_interval = new double[2];
confidence_interval[0] = proportion + critical_value1 * standard_error;
confidence_interval[1] = proportion + critical_value2 * standard_error;
return(confidence_interval);
}
}
/// <summary>
/// Two-sided or one-sided test for a single mean
///
/// Given that:
/// H_0 : mu = null_value
/// H_A : mu != null_value
///
/// By Central Limit Theorem:
/// sample_mean ~ N(mu, SE)
///
/// p-value = (sample_mean is at least ||null_value-point_estimate|| away from the null_value) | mu = null_value)
/// if(p-value < significance_level) reject H_0
/// </summary>
/// <param name="sample"></param>
/// <param name="null_value"></param>
/// <param name="significance_level"></param>
/// <param name="one_sided">True if the test is one_sided</param>
/// <returns></returns>
public static bool RejectH0(double[] sample, double null_value, out double pValue, double significance_level = 0.05, bool one_sided = false, bool useStudentT = false)
{
double pointEstimate = Mean.GetMean(sample);
double standardError = StandardError.GetStandardError(sample); //SE is the estimated standard deviation of the true population mean, mu
double test_statistic = System.Math.Abs(pointEstimate - null_value) / standardError; //This assumes that H_0 is true, that is, the true population mean, mu = null_value
double percentile = 0;
if (sample.Length < 30 || useStudentT) //if sample size is smaller than 30, then CLT for population statistics such as sample mean no longer holds and Student's t distribution should be used in place of the normal distribution
{
percentile = StudentT.GetPercentile(test_statistic, sample.Length - 1);
}
else
{
percentile = Gaussian.GetPercentile(test_statistic);
}
pValue = pValue = (1 - percentile) * (one_sided ? 1 : 2);
return(pValue < significance_level);
}
public static Dictionary <int, double[]> GetMeanWithinGroup(Dictionary <int, List <double> >[] groupSampleWithDim)
{
Dictionary <int, double[]> means = new Dictionary <int, double[]>();
int D = groupSampleWithDim.Length;
foreach (int groupId in groupSampleWithDim[0].Keys)
{
means[groupId] = new double[D];
}
for (int d = 0; d < D; ++d)
{
Dictionary <int, List <double> > groupSample = groupSampleWithDim[d];
foreach (int grpId in groupSample.Keys)
{
means[grpId][d] = Mean.GetMean(groupSample[grpId]);
}
}
return(means);
}
/// <summary>
/// Two-sided or one-sided test for whether statitics of two variables are equal in the true population, var1 and var2 are independent
///
/// Hypotheses are:
/// H_0: mu_var1 = mu_var2
/// H_1: mu_var1 != mu_var2
///
/// The hypotheses can be written as
/// H_0: mu_var1 - mu_var2 = 0
/// H_1: mu_var1 - mu_var2 != 0
///
/// By Central Limt Theorem:
/// sample_mean_var1 - sample_mean_var2 ~ N(0, SE), where null_value = 0 and SE is the standard error of the sampling distribution
///
/// p-value = (sample_mean is at least ||null_value-point_estimate|| away from the null_value) | mu = null_value)
/// </summary>
/// <param name="sample_for_var1">value sample for variable 1</param>
/// <param name="sample_for_var2">value sample for variable 2</param>
/// <param name="one_sided">True if the test is one-sided</param>
/// <param name="significance_level"></param>
/// <returns></returns>
public bool RejectH0(double[] sample_for_var1, double[] sample_for_var2, out double pValue, double significance_level = 0.05, bool one_sided = false, bool useStudentT = false)
{
double pointEstimate = Mean.GetMean(sample_for_var1) - Mean.GetMean(sample_for_var2);
double null_value = 0;
double SE = StandardError.GetStandardError(sample_for_var1, sample_for_var2);
double test_statistic = System.Math.Abs(pointEstimate - null_value) / SE;
double percentile = 0;
if (sample_for_var1.Length < 30 || sample_for_var2.Length < 30 || useStudentT) //if sample size is smaller than 30, then CLT for population statistics such as sample mean no longer holds and Student's t distribution should be used in place of the normal distribution
{
int df = System.Math.Min(sample_for_var1.Length - 1, sample_for_var2.Length - 1);
percentile = StudentT.GetPercentile(test_statistic, df);
}
else
{
percentile = Gaussian.GetPercentile(test_statistic);
}
pValue = (1 - percentile) * (one_sided ? 1 : 2);
return(pValue < significance_level);
}
/// <summary>
/// Calculate the confidence interval for the proportion of SUCCESS in the population at a given confidence interval, given the point estimate proprotions are known from multiple groups
///
/// Note that this is only for categorical variable with two levels : SUCCESS, FAILURE
/// </summary>
/// <param name="proportions">The point estimate proportion of SUCESS obtained from multiple groups</param>
/// <param name="sampleSizes">The sample size of each group</param>
/// <param name="confidence_level">The given confidence interval</param>
/// <returns>The confidence interval for the proportion of SUCCESS in the population at the given confidence level</returns>
public static double[] GetConfidenceInterval(double[] proportions, int[] sampleSizes, double confidence_level, bool useSimulation = false, int simulationCount = 500)
{
double p1 = (1 - confidence_level) / 2;
double p2 = 1 - p1;
bool shouldUseSimulation = useSimulation;
if (!shouldUseSimulation)
{
for (int i = 0; i < sampleSizes.Length; ++i)
{
int n_i = sampleSizes[i];
int expected_success_count = (int)(proportions[i] * n_i);
int expected_failure_count = (int)((1 - proportions[i]) * n_i);
if (expected_failure_count < 10 || expected_success_count < 10)
{
shouldUseSimulation = true;
break;
}
}
}
if (shouldUseSimulation)
{
double sucess_count = 0;
double total_count = 0;
for (int i = 0; i < sampleSizes.Length; ++i)
{
int n_i = sampleSizes[i];
sucess_count += proportions[i] * n_i;
total_count += n_i;
}
double p_hat = sucess_count / total_count;
double[] sampleProportions = new double[simulationCount];
int simulationSampleSize = (int)System.Math.Max(10 / p_hat, 10 / (1 - p_hat)) * 2;
for (int i = 0; i < simulationCount; ++i)
{
int successCount = 0;
for (int j = 0; j < simulationSampleSize; ++j)
{
if (DistributionModel.GetUniform() <= p_hat)
{
successCount++;
}
}
sampleProportions[i] = (double)successCount / simulationSampleSize;
}
double proportion_mu = Mean.GetMean(sampleProportions);
double proportion_sigma = StdDev.GetStdDev(sampleProportions, proportion_mu);
return(new double[] { proportion_mu + Gaussian.GetPercentile(p1) * proportion_sigma, proportion_mu + Gaussian.GetQuantile(p2) * proportion_sigma });
}
else
{
double[] standardErrors = new double[proportions.Length];
for (int i = 0; i < proportions.Length; ++i)
{
standardErrors[i] = StandardError.GetStandardErrorForProportion(proportions[i], sampleSizes[i]);
}
double standardError = StandardError.GetStandardErrorForWeightAverages(sampleSizes, standardErrors);
double sampleMean = Mean.GetMeanForWeightedAverage(proportions, sampleSizes);
double critical_value1 = 0;
double critical_value2 = 0;
critical_value1 = Gaussian.GetQuantile(p1);
critical_value2 = Gaussian.GetQuantile(p2);
double[] confidence_interval = new double[2];
confidence_interval[0] = sampleMean + critical_value1 * standardError;
confidence_interval[1] = sampleMean + critical_value2 * standardError;
return(confidence_interval);
}
}