Below you will find complete descriptions and links to 10 different analytics calculators for computing different analytics-related confidence intervals.

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Compute the 90%, 95%, and 99% confidence intervals for a binomial probability using the Clopper-Pearson (exact) method, given the total number of trials and the number of successes. Knowing the confidence interval for a binomial probability can be very useful for analytics studies that rely on binomial experiments.

Compute the 90%, 95%, and 99% confidence intervals for Cohen's f-square effect size for a multiple regression study, given the f-square value, the number of predictor variables, and the total sample size. Knowing the confidence interval for an f-square effect size can be very useful for comparing different models in analytics studies that rely on multiple regression.

Compute the 90%, 95%, and 99% confidence intervals for a mediation indirect effect, given values of the regression coefficient and standard error for the relationship between the mediator and the dependent variable, and values of the regression coefficient and standard error for the relationship between the independent variable and the mediator. Knowing the confidence interval for an indirect effect can be very useful for assessing the true nature of the indirect effect in analytics studies that rely on mediation models.

Compute the 90%, 95%, and 99% confidence intervals for an R-square value, given the R-square value, the number of predictor variables, and the total sample size. Knowing the confidence interval for an R-square value can be very useful in analytics when considering the true degree of usefulness that a regression model might have in the overall population.

Compute the exact 90%, 95%, and 99% confidence intervals for a Poisson mean, given the total number of number of event occurrences. Knowing the confidence interval for a Poisson mean can be very useful for analytics studies that use the Poisson distribution to examine interval data.

Compute the 90%, 95%, and 99% confidence intervals for the mean of a normal population when the population standard deviation is known, given the population standard deviation, the sample mean, and the sample size. Knowing the confidence interval for the mean of a normal population can be very useful for assessing the true nature of the population in analytics studies that rely on normally distributed sample data.

Compute the 90%, 95%, and 99% confidence intervals for the mean of a normal population, given the sample standard deviation, the sample mean, and the sample size. Knowing the confidence interval for the mean of a normal population can be very useful for assessing the true nature of a population variable in analytics studies that use normally distributed sample data.

Compute the 99%, 95%, and 90% confidence intervals for a predicted value of a regression equation, given the standard error of the estimate, the number of predictor variables, the total sample size, and a predicted value of the dependent variable. Knowing the confidence interval for a predicted regression value can be very useful for assessing the true range of outcomes that might occur in light of a given set of input values in analytics studies that rely on multiple regression.

Compute 90%, 95%, and 99% confidence intervals for a regression coefficient, given the regression coefficient value, the standard error of the regression coefficient, the number of predictor variables, and the total sample size. Knowing the confidence interval for a regression coefficient can be very useful in analytics when considering the true range of values that a predictor variable might have in the overall population.

Compute the 90%, 95%, and 99% confidence intervals for a regression intercept (or regression constant), given the regression intercept value, the standard error of the regression intercept, the number of predictor variables, and the total sample size. Knowing the confidence interval for a regression intercept can be very useful in analytics when considering the true range of values that the intercept might have in the overall population.