enow.com Web Search

  1. Ad

    related to: how to check for outliers in spss excel test statistic
  2. wyzant.com has been visited by 10K+ users in the past month

    • Tutors Near You

      Expert Tutors, Private Sessions.

      Tutors From $25/hr. Try Today.

    • Find a Tutor

      Find Affordable Tutors at Wyzant.

      1-on-1 Sessions From $25/hr.

Search results

  1. Results from the WOW.Com Content Network
  2. Grubbs's test - Wikipedia

    en.wikipedia.org/wiki/Grubbs's_test

    In statistics, Grubbs's test or the Grubbs test (named after Frank E. Grubbs, who published the test in 1950 [1]), also known as the maximum normalized residual test or extreme studentized deviate test, is a test used to detect outliers in a univariate data set assumed to come from a normally distributed population.

  3. Cochran's C test - Wikipedia

    en.wikipedia.org/wiki/Cochran's_C_test

    Cochran's test, [1] named after William G. Cochran, is a one-sided upper limit variance outlier statistical test .The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable.

  4. Dixon's Q test - Wikipedia

    en.wikipedia.org/wiki/Dixon's_Q_test

    To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined: Q = gap range {\displaystyle Q={\frac {\text{gap}}{\text{range}}}} Where gap is the absolute difference between the outlier in question and the closest number to it.

  5. Studentized residual - Wikipedia

    en.wikipedia.org/wiki/Studentized_residual

    This is an important technique in the detection of outliers. It is among several named in honor of William Sealey Gosset , who wrote under the pseudonym "Student" (e.g., Student's distribution ). Dividing a statistic by a sample standard deviation is called studentizing , in analogy with standardizing and normalizing .

  6. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation.

  7. Outlier - Wikipedia

    en.wikipedia.org/wiki/Outlier

    The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.

  8. Chauvenet's criterion - Wikipedia

    en.wikipedia.org/wiki/Chauvenet's_criterion

    The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...

  9. Leverage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Leverage_(statistics)

    In statistics and in particular in regression analysis, leverage is a measure of how far away the independent variable values of an observation are from those of the other observations. High-leverage points , if any, are outliers with respect to the independent variables .

  1. Ad

    related to: how to check for outliers in spss excel test statistic