what is the formula to find outliers?
440888
A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5\cdot \text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile.
1039841
['State the null hypothesis and alternate hypothesis.', 'Choose an alpha level.', 'Find the critical value of z in a z table.', 'Calculate the z test statistic (see below).', 'Compare the test statistic to the critical z value and decide if you should support or reject the null hypothesis.']
57605
Percent Deviation From a Known Standard To find this type of percent deviation, subtract the known value from the mean, divide the result by the known value and multiply by 100.
841608
['Standard Error = [(Geometric Stdev-1)/Sqrt(N)],', 'Margin of Error = [Standard Error * 1.96], and.', 'CI = [Geometric Mean +/- Margin of Error]']
1097286
An outlier can affect the mean of a data set by skewing the results so that the mean is no longer representative of the data set.
504423
Outliers beyond the whiskers may be indi- vidually plotted. Box plot construction requires a sample of at least n= 5 (preferably larger), although some software does not check for this. For n < 5 we recommend showing the individual data points.
213495
Box plots are a graphical depiction of numerical data through their quantiles. It is a very simple but effective way to visualize outliers. Think about the lower and upper whiskers as the boundaries of the data distribution. Any data points that show above or below the whiskers, can be considered outliers or anomalous.
715081
['Step 1 - Find the mean of your terms.', 'Step 2 - Subtract the sample mean from each term ( ¯x−xi ).', 'Step 3 - Square each of the results. ... ', 'Step 4 - Find the sum of the squared terms.', "Step 5 - Finally, we'll find the variance."]
191169
['For each value, find the square of the difference between the value and the mean.', 'Find the sum of the squared differences. ... ', 'Divide them by one less than the number of values. ... ', 'Take the square root of this result. ... ', 'Add the standard deviation to the mean. ... ', 'Subtract the standard deviation from the mean.']
541784
['Step 1: Calculate the mean of the data—this is μ in the formula.', 'Step 2: Subtract the mean from each data point. ... ', 'Step 3: Square each deviation to make it positive.', 'Step 4: Add the squared deviations together.', 'Step 5: Divide the sum by the number of data points in the population.']
972638
['Standard Error Calculation 9/22/04.', 'STANDARD ERROR CALCULATION. Procedure: ... ', 'Example:', '5 Divide by number of measurements-1. ∑ (m-i)2 / (n-1) = 272.70 / 4 = 68.175. ... ', 'Name. Height to nearest 0.5 cm.', '2 Deviations (m-i) 3 Squared deviations (m-i)2.', 'Waldo. 150.5. ... ', '1 Mean m = 162.4 cm. 4 Sum of squared deviations.']
1250446
['Find a mean of the set of data.', 'Subtract each number from a mean.', 'Square the result.', 'Add the results together.', 'Divide a result by the total number of numbers in the data set.']
30274
['Add up the sample items.', 'Divide sum by the number of samples.', 'The result is the mean.', 'Use the mean to find the variance.', 'Use the variance to find the standard deviation.']
1416543
["Subtract the values. For example, let's say the mean of your data set is 10, and you have 5 values: 1, 5, 10, 15 and 19. The absolute deviations are: 10 – 1 = 9. 10 – 5 = 5. ... ", 'Take the absolute value of the numbers found. The absolute value of -5 is 5, and -9 is 9. The final list of values would be 9 ,5, 0, 5, and 9.']
961137
['Step 1: Enter the actual values and forecasted values in two separate columns.', 'Step 2: Calculate the absolute percent error for each row. Recall that the absolute percent error is calculated as: |actual-forecast| / |actual| * 100. ... ', 'Step 3: Calculate the mean absolute percent error.']
464709
['Calculate the deviation of each measurement. ... ', 'Calculate the squares of each of the deviations (i.e. di2). ... ', 'Add together all the squares of the deviations i.e.', 'Divide by the total number of measurements (i.e. n) to obtain the mean of all the square deviations.']
1082314
This study finds evidence that the estimates in ANOVA are sensitive to outliers, i.e. that the procedure is not robust. Samples with a larger portion of extreme outliers have a higher type-I error probability than the expected level.