Aggregators

The aggregators this instance offers: what each is for, the questions it answers and when to reach for something else. Operators are sorted by name; each name links to its detail block below.

Operators

AGG_AVERAGE

Average of a numeric field, over all rows or per group.

Level: basic

Also known as: mean, arithmetic mean

Questions it answers:

• What is the average order value?
• What is the typical rating in each region?

Use cases by domain:

• survey: Mean satisfaction score per segment.
• ops: Average order value by sales channel.
• science: Mean measurement per treatment arm.

Assumptions:

• Missing values are skipped: the average is over rows that have a value. A group where no row has a value reads 0, not empty, so check n in the components before reporting it.

Use something else:

• AGG_MEDIAN when the field is skewed or has extreme values and you want the typical row.
• AGG_WEIGHTED_MEAN when rows carry weights.
• AGG_WELFORD when you also need the spread around the average.

Glossary: mean, median, outlier, skew

Skill: op-agg-average

AGG_CI_LOWER

Lower end of a confidence interval for the mean of a numeric field, showing how precisely the average is pinned down.

Level: intermediate

Questions it answers:

• How precisely do we know the average satisfaction in each segment?
• What is the lowest average order value the data are consistent with at 95% confidence?

Use cases by domain:

• survey: Error bars on a mean score per segment.
• ops: Range for average handling time per team.
• science: Interval for a mean measurement per arm.

Assumptions:

• Rows are independent draws, unweighted.
• Normal critical value (1.96 at 95%), not Student’s t, so small groups, and strongly skewed fields even well past 30 rows, get an interval that is too narrow.
• Empty (NaN) when fewer than two rows have a value.

Use something else:

• AGG_CI_UPPER when you want the upper end of the same interval.
• TEST_WELCH when you want to test whether two group means differ.
• AGG_PERCENTILE when you want the range individual rows fall in, not the mean.

Glossary: confidence-interval, mean, standard-error, sample-size, independence

Skill: op-agg-ci-lower

AGG_CI_UPPER

Upper end of a confidence interval for the mean of a numeric field, showing how precisely the average is pinned down.

Level: intermediate

Questions it answers:

• What is the highest average wait time the data are consistent with at 95% confidence?
• How wide is the uncertainty around each region’s mean score?

Use cases by domain:

• survey: Error bars on a mean score per segment.
• ops: Upper end of the 95% interval for average delivery time per carrier: the true average can still lie above it.
• science: Interval for a mean measurement per arm.

Assumptions:

• Rows are independent draws, unweighted.
• Normal critical value (1.96 at 95%), not Student’s t, so small groups, and strongly skewed fields even well past 30 rows, get an interval that is too narrow.
• Empty (NaN) when fewer than two rows have a value.

Use something else:

• AGG_CI_LOWER when you want the lower end of the same interval.
• TEST_WELCH when you want to test whether two group means differ.

Glossary: confidence-interval, mean, standard-error, sample-size, independence

Skill: op-agg-ci-upper

AGG_COUNT

Number of rows that have a value in a field, over all rows or per group.

Level: basic

Questions it answers:

• How many responses did each region return?
• How many orders were placed through each channel?

Use cases by domain:

• survey: Number of answered questionnaires per segment.
• ops: Order count per store per day.
• science: Number of recorded measurements per treatment arm.

Assumptions:

• Rows with a missing value in the field are not counted.

Use something else:

• AGG_NULL_COUNT when you want the rows where the field is empty.
• AGG_DISTINCT_COUNT when you want how many different values appear, not how many rows.
• GROUP_CATEGORY when you want a count for every value of the field: group by it, then count.
• AGG_FREQUENCY when you want how many rows hold one particular value, such as the Yes answers.

Glossary: missing-value, sample-size

Skill: op-agg-count

AGG_DISTINCT_COUNT

Number of different values a field takes, over all rows or per group.

Level: basic

Questions it answers:

• How many different customers ordered this month?
• Does the respondent ID appear once per row, or are there duplicates?

Use cases by domain:

• survey: Number of distinct respondents per wave.
• ops: Unique customers per store.
• science: Number of distinct subjects per site.

Assumptions:

• Missing values are not counted as a value.

Use something else:

• AGG_COUNT when you want how many rows there are, not how many different values.
• GROUP_CATEGORY when you want how many rows hold each value: group by the field, then count.
• AGG_SET_DISTINCT_VALUES when the field is a multi-select and each combination is the unit.

Glossary: missing-value

Skill: op-agg-distinct-count

AGG_DISTINCT_SUM

Total of a numeric field counting each key once, such as a respondent’s weight summed once per respondent.

Level: intermediate

Questions it answers:

• What is the weighted base when each respondent appears on several rows?
• What is the total contract value when each contract repeats on every line item?

Use cases by domain:

• survey: Weighted base of a segment in a cohort with one row per answer.
• ops: Total customer credit limit when each customer has many orders.

Assumptions:

• The value is the same on every row of a key: when it differs, the first value seen is kept.
• A row missing either the value or the key adds nothing.

Use something else:

• AGG_SUM when every row is its own unit and should be added.
• AGG_DISTINCT_COUNT when you want how many keys there are, not a total.

Glossary: missing-value, weighting

Skill: op-agg-distinct-sum

AGG_FREQUENCY

How many rows hold one chosen value of a field, such as the Yes answers, over all rows or per group.

Level: basic

Questions it answers:

• How many respondents answered Yes in each region?
• How many orders were returned each day?

Use cases by domain:

• survey: Number of Yes answers to a question per segment.
• ops: Orders with the Returned status per store per day.
• science: Positive results per treatment arm.

Assumptions:

• Missing values are skipped: they are neither counted nor part of the base the share in the components divides by.
• The value is matched as a row filter matches it: a category label, otherwise a number; a value no row holds counts 0, so check its spelling.
• The result equals keeping only that value with a row filter, then counting rows.

Use something else:

• AGG_MODE_COUNT when you want how many rows share the field’s most common value, whatever it is.
• GROUP_CATEGORY when you want a count for every value of the field: group by it, then count rows.
• FILTER_INCLUDE when you want rows holding any of several values: keep them, then count rows.
• AGG_SET_FREQUENCY when the field is a multi-select.

Glossary: missing-value

Skill: op-agg-frequency

AGG_KURTOSIS

How heavy a numeric field’s tails are next to a normal distribution: positive when extreme values are more common.

Level: advanced

Questions it answers:

• Do transaction amounts have more extreme values than a bell curve would give?
• Are response times prone to rare, very long waits?

Use cases by domain:

• ops: Spotting fields where rare extreme values dominate risk.
• science: Checking a measurement’s tails before trusting a mean-based test.

Assumptions:

• Missing values are skipped.
• Population excess kurtosis g2 (dividing by n, minus 3): a normal shape scores 0, and at small n it differs from the adjusted G2 most packages print.
• It is 0 when n is 0 or 1 or every value is the same.

Use something else:

• AGG_SKEWNESS when you want to know whether the field is lopsided.
• TEST_SHAPIRO_WILK when you want a test of whether the field follows a normal shape.

Glossary: kurtosis, normal-distribution, outlier

Skill: op-agg-kurtosis

AGG_MAE

Average size of a prediction’s miss in the actual’s own units, comparing an actual column with a prediction column row by row.

Level: intermediate

Also known as: mean absolute error

Questions it answers:

• On average, how many units off is the sales model on the held-out weeks?
• Is the forecast miss larger for some stores than for others?

Use cases by domain:

• survey: Average miss of a predicted score against the observed score, in scale points.
• ops: Average holdout miss of a demand forecast, in units.
• science: Average absolute error of a calibration model on the validation samples.

Assumptions:

• A row is used only when both the actual and the prediction have a value.
• Train and holdout rows are separated with a filter or a range grouper; the operator itself never splits rows.

Use something else:

• AGG_RMSE when large misses should count for more than small ones.
• AGG_MAPE when you want the miss as a share of the actual value.

Glossary: residual, overfitting, missing-value

Skill: op-agg-mae

AGG_MAPE

Average size of a prediction’s miss as a share of the actual value, comparing an actual column with a prediction column row by row.

Level: intermediate

Also known as: mean absolute percentage error

Questions it answers:

• On the held-out weeks, how far off is the sales model, as a share of actual sales?
• Is the forecast’s percentage miss larger in some regions than in others?

Use cases by domain:

• survey: Percentage miss of a model-predicted response rate against the observed rate per wave.
• ops: Holdout forecast accuracy of weekly sales, as a share of actual sales.
• science: Relative error of a calibration model on the validation samples.

Assumptions:

• A row is used only when both the actual and the prediction have a value; a row whose actual is exactly 0 is left out and counted in components n_zero_actual.
• The result is a fraction (0.12 means 12%), not a percentage.
• Train and holdout rows are separated with a filter or a range grouper; the operator itself never splits rows.

Use something else:

• AGG_MAE when actual values can be 0 or close to 0, where a share is undefined or explodes.
• AGG_RMSE when large misses should count for more than small ones.

Glossary: residual, overfitting, missing-value

Skill: op-agg-mape

AGG_MAX

Largest value of a numeric or date field, over all rows or per group.

Level: basic

Questions it answers:

• What is the latest order date in each region?
• Is any order value implausibly large?

Use cases by domain:

• survey: Highest household size reported, to catch typing errors.
• ops: Largest single order per sales channel.
• science: Peak reading per run.

Assumptions:

• Missing values are skipped; a group where no row has a value reads 0, not empty, so check n in the components before reporting it.
• It rests on one row, so a single bad record sets it.

Use something else:

• AGG_MIN when you want the smallest value.
• AGG_PERCENTILE when one extreme row would mislead and you want a high value that ignores it.
• AGG_RANGE when you want the distance between smallest and largest.

Glossary: outlier, percentile

Skill: op-agg-max

AGG_MEDIAN

Middle value of a numeric field once sorted: the typical row, not pulled by a few extreme values.

Level: basic

Questions it answers:

• What is the typical household income in each region?
• What is the typical delivery time when a few deliveries take weeks?

Use cases by domain:

• survey: Typical income per segment when a few incomes are very large.
• ops: Typical order value when a few bulk orders inflate the average.
• science: Typical response time per condition.

Assumptions:

• Missing values are skipped; a group where no row has a value reads 0, not empty, so check n in the components before reporting it.
• With an even number of values it is the halfway point between the two middle values.

Use something else:

• AGG_PERCENTILE when you want a value other than the middle, such as the 90th percentile.
• AGG_AVERAGE when the field is roughly symmetric and you want a figure that combines across groups.
• AGG_MODE when the field holds categories rather than numbers.

Glossary: median, mean, outlier, percentile, skew

Skill: op-agg-median

AGG_MIN

Smallest value of a numeric or date field, over all rows or per group.

Level: basic

Questions it answers:

• What is the earliest order date in each region?
• Are there negative or impossible values in the age field?

Use cases by domain:

• survey: Lowest age reported, to catch out-of-range answers.
• ops: First order date per customer segment.
• science: Lowest reading per sensor, to spot faulty sensors.

Assumptions:

• Missing values are skipped; a group where no row has a value reads 0, not empty, so check n in the components before reporting it.
• It rests on one row, so a single bad record sets it.

Use something else:

• AGG_MAX when you want the largest value.
• AGG_PERCENTILE when one extreme row would mislead and you want a low value that ignores it.
• AGG_RANGE when you want the distance between smallest and largest.

Glossary: outlier, percentile

Skill: op-agg-min

AGG_MODE

Most common value of a field, over all rows or per group.

Level: basic

Questions it answers:

• What is the most common answer to the preferred-brand question?
• Which product is ordered most often in each store?

Use cases by domain:

• survey: Most chosen option per question and segment.
• ops: Most frequent payment method per channel.
• science: Most common category of outcome per group.

Assumptions:

• Missing values are skipped; a group where no row has a value reads 0, not empty, so check n in the components before reporting it.
• When several values share the top count, the smallest wins (for categories, the first in the dictionary); tie_count in the components shows it happened.

Use something else:

• GROUP_CATEGORY when you want the count of every value, not only the top one: group by the field, then count.
• AGG_MEDIAN when the field is numeric and you want its middle value.
• AGG_FREQUENCY when you want how many rows hold one particular value, such as the Yes answers.
• AGG_SET_FREQUENCY when the field is a multi-select.

Glossary: median

Skill: op-agg-mode

AGG_MODE_COUNT

How many rows share a field’s most common value, over all rows or per group.

Level: basic

Questions it answers:

• How many respondents gave the most common answer?
• How many orders used the most popular payment method?
• Grouped by the same field, how many rows fall in each category?

Use cases by domain:

• survey: Size of the largest answer group per question and segment.
• ops: Orders carrying the most common status per day.
• science: Size of the most common outcome category per treatment arm.

Assumptions:

• Missing values are skipped, so only rows that have a value are counted.
• Grouped by the same field, each group holds one value, so the result is that group’s row count.
• When several values tie for most common the count is the same; the components name the smallest tied value as mode_value.

Use something else:

• GROUP_CATEGORY when you want a count for every value of the field: group by it, then count rows.
• capability:facet when you want per-value counts for several fields in one call.
• AGG_MODE when you want which value is most common, not how many rows hold it.
• AGG_FREQUENCY when you want how many rows hold one particular value, such as the Yes answers.
• AGG_DISTINCT_COUNT when you want how many different values there are.
• AGG_SET_FREQUENCY when the field is a multi-select.

Glossary: missing-value

Skill: op-agg-mode-count

AGG_NULL_COUNT

Number of rows where a field has no value, over all rows or per group.

Level: basic

Questions it answers:

• How many respondents skipped the income question?
• Which regions have the most orders missing a delivery date?

Use cases by domain:

• survey: Item non-response per question and per segment.
• ops: Records missing a required field, per data source.
• science: Missing measurements per site before an analysis.

Assumptions:

• Only a stored null counts: an empty selection in a multi-select field is a value, not a null.

Use something else:

• AGG_COUNT when you want the rows that do have a value.
• FILTER_NULL when you want to keep or drop the rows with no value.

Glossary: missing-value

Skill: op-agg-null-count

AGG_PERCENTILE

Value of a numeric field below which a chosen share of rows fall, such as the 90th percentile of delivery time.

Level: basic

Questions it answers:

• How long do the slowest 10% of deliveries take in each region?
• What income marks the top quarter of respondents?

Use cases by domain:

• survey: Quartiles of household income per segment.
• ops: 95th-percentile response time per service.
• science: Reference range (2.5th to 97.5th percentile) of a measurement.

Assumptions:

• Missing values are skipped.
• Linear interpolation between the two nearest sorted values, so the result may be a value no row holds.

Use something else:

• AGG_MEDIAN when you want the 50th percentile.
• ATTR_PERCENTILE when you want each row’s own percentile position.
• GROUP_QUANTILE when you want rows split into equal-sized bands.

Glossary: percentile, median, outlier

Skill: op-agg-percentile

AGG_RANGE

Distance between the smallest and largest value of a numeric field, over all rows or per group.

Level: basic

Questions it answers:

• How far apart are the cheapest and most expensive orders?
• How widely do delivery times span in each depot?

Use cases by domain:

• survey: Span of ages within each segment.
• ops: Spread of daily sales between the slowest and busiest store.
• science: Span of readings across repeat measurements.

Assumptions:

• Missing values are skipped; a group where no row has a value reads 0, not empty, so check n in the components before reporting it.
• It rests on the two most extreme rows, so one outlier can stretch it a lot.

Use something else:

• AGG_STDDEV when you want a spread measure that uses every row, not only the two extremes.
• AGG_PERCENTILE when you want the spread of the middle of the data, ignoring extremes.

Glossary: outlier, standard-deviation

Skill: op-agg-range

AGG_RATIO

Total of one field divided by the total of another, over all rows or per group, such as revenue per order.

Level: basic

Questions it answers:

• What is revenue per visit in each channel?
• What share of budgeted hours were actually worked per team?

Use cases by domain:

• survey: Completes per invitation sent, per wave.
• ops: Revenue per order, or cost per unit, by region.
• science: Events per person-year of follow-up per arm.

Assumptions:

• A row missing either field is left out of both totals.
• Empty (NaN) when the denominator total is 0.

Use something else:

• ATTR_FORMULA when you want the average of each row’s own ratio: compute it per row (guarding a 0 denominator), then average it.
• AGG_WEIGHTED_MEAN when you want an average where some rows count more than others.

Glossary: mean

Skill: op-agg-ratio

AGG_RMSE

Typical size of a prediction’s miss in the actual’s own units, with large misses counting more: the root of the average squared miss.

Level: intermediate

Also known as: root mean squared error

Questions it answers:

• How many units off is the sales model on the held-out weeks, with big misses weighing more?
• Which of two models misses less on the same rows?

Use cases by domain:

• survey: Error of a predicted satisfaction score against the observed score, in scale points.
• ops: Holdout error of a handling-time model, in minutes.
• science: Prediction error of a dose-response model on the validation subjects.

Assumptions:

• A row is used only when both the actual and the prediction have a value.
• Each miss is squared before averaging, so a few large misses can dominate it.
• Train and holdout rows are separated with a filter or a range grouper; the operator itself never splits rows.

Use something else:

• AGG_MAE when every unit of miss should count the same, so a few large misses do not dominate.
• AGG_MAPE when you want the miss as a share of the actual value.

Glossary: residual, overfitting, missing-value

Skill: op-agg-rmse

AGG_SET_CARDINALITY_AVG

For a multi-select field, the average number of options chosen per row.

Level: basic

Questions it answers:

• How many brands does a respondent recognise on average?
• How many features does a typical customer have turned on?

Use cases by domain:

• survey: Average number of answers to a select-all-that-apply question, per segment.
• ops: Average number of products per customer bundle.

Assumptions:

• Rows with an empty selection count as zero and pull the average down; null rows are skipped.

Use something else:

• AGG_SET_CARDINALITY_SUM when you want the total number of selections.
• AGG_SET_FREQUENCY when you want which options were chosen.

Glossary: mean, missing-value

Skill: op-agg-set-cardinality-avg

AGG_SET_CARDINALITY_SUM

For a multi-select field, the total number of options chosen across all rows.

Level: basic

Questions it answers:

• How many brand mentions did the survey collect in total?
• How many add-ons were sold across all orders?

Use cases by domain:

• survey: Total mentions for a select-all-that-apply question, as a base for share of mentions.
• ops: Total add-ons attached to orders per channel.

Assumptions:

• Null rows are skipped; an empty selection adds zero.

Use something else:

• AGG_SET_CARDINALITY_AVG when you want the number per row.
• AGG_SET_FREQUENCY when you want the count for each option.

Skill: op-agg-set-cardinality-sum

AGG_SET_DISTINCT_VALUES

For a multi-select field, how many different combinations of options appear.

Level: basic

Questions it answers:

• How many different combinations of channels do customers use?
• How many different combinations of brands were picked (an empty answer counts as one)?

Use cases by domain:

• survey: Variety of answer patterns to a select-all-that-apply question.
• ops: Number of distinct product bundles sold.

Assumptions:

• Each combination is one value: choosing A and B differs from choosing A alone.
• An empty selection counts as one combination; null rows are skipped.

Use something else:

• AGG_SET_FREQUENCY when you want the count of each single option.
• AGG_SET_UNION when you want every option chosen by anyone.
• GROUP_SET_VALUE when you want how common each combination is: a count alone cannot show whether one dominates.

Skill: op-agg-set-distinct-values

AGG_SET_FREQUENCY

For a multi-select field, how many rows chose each option, over all rows or per group.

Level: basic

Questions it answers:

• How many respondents selected each brand they have heard of?
• How many customers use each payment method?

Use cases by domain:

• survey: Counts per option of a select-all-that-apply question.
• ops: Customers per enabled feature.

Assumptions:

• A row with several options is counted once under each, so the counts can add up to more than the rows.

Use something else:

• GROUP_CATEGORY when the field holds one value per row: group by it, then count.
• AGG_SET_DISTINCT_VALUES when you want how many different combinations were chosen.

Skill: op-agg-set-frequency

AGG_SET_INTERSECTION

For a multi-select field, the options chosen by every row, over all rows or per group.

Level: basic

Questions it answers:

• Which brands did every respondent in this segment recognise?
• Which features does every customer on a plan use?

Use cases by domain:

• survey: Options recognised by the whole segment.
• ops: Features common to every account on a plan.

Assumptions:

• Null rows are skipped, but one row with an empty selection empties the result.

Use something else:

• AGG_SET_UNION when you want every option chosen by anyone.
• AGG_SET_FREQUENCY when you want how many rows chose each option.

Skill: op-agg-set-intersection

AGG_SET_UNION

For a multi-select field, every option chosen by at least one row, over all rows or per group.

Level: basic

Questions it answers:

• Which brands were mentioned by anyone in this segment?
• Which features does at least one customer in each plan use?

Use cases by domain:

• survey: Options that received any mention per segment.
• ops: Features used by anyone on each plan.

Assumptions:

• Null rows are skipped.

Use something else:

• AGG_SET_INTERSECTION when you want the options every row chose.
• AGG_SET_FREQUENCY when you want how many rows chose each option.

Skill: op-agg-set-union

AGG_SKEWNESS

How lopsided a numeric field is: positive when a long tail runs to high values, negative when it runs to low ones.

Level: intermediate

Questions it answers:

• Are incomes in each region bunched low with a few very high ones?
• Is the delivery-time distribution symmetric or dragged out by slow orders?

Use cases by domain:

• survey: Whether a rating scale piles up at one end per segment.
• ops: Whether order values have a long tail of large orders.
• science: Checking a measurement’s shape before choosing a test.

Assumptions:

• Missing values are skipped.
• Population moment coefficient g1 (dividing by n): at small n it runs smaller in size than the adjusted G1 most packages print.
• It is 0 when n is 0 or 1 or every value is the same.

Use something else:

• AGG_KURTOSIS when you want to know whether the tails are heavy rather than lopsided.
• TEST_SHAPIRO_WILK when you want a test of whether the field follows a normal shape.
• AGG_MEDIAN when you want the typical value of a skewed field.

Glossary: skew, outlier, median, normal-distribution

Skill: op-agg-skewness

AGG_STDDEV

Typical distance of a numeric field’s values from their average, in the field’s own units, over all rows or per group.

Level: basic

Also known as: standard deviation, sd

Questions it answers:

• How much do delivery times vary around the average in each region?
• Which product line’s order values vary the most in absolute terms (divide by each line’s average to compare relative consistency)?

Use cases by domain:

• survey: How widely satisfaction scores spread within each segment.
• ops: Day-to-day variability of order volume per store.
• science: Spread of a measurement within each treatment arm.

Assumptions:

• Missing values are skipped.
• Population form: the squared distances are averaged over n, not n - 1.

Use something else:

• AGG_CI_LOWER when you want how precisely the average itself is known.
• AGG_WELFORD when you want the sample form (dividing by n - 1) together with the mean and n.
• AGG_PERCENTILE when the field has extreme values and you want a spread they cannot drag.

Glossary: standard-deviation, variance, mean, outlier

Skill: op-agg-stddev

AGG_SUM

Total of a numeric field, over all rows or per group.

Level: basic

Questions it answers:

• What is total revenue by region?
• How many units did each product line sell in total?

Use cases by domain:

• survey: Total of a per-respondent weight to get the weighted base of each segment.
• ops: Revenue per sales channel per month.
• science: Total dose delivered per treatment arm.

Assumptions:

• Missing values are skipped: they add nothing to the total.

Use something else:

• AGG_AVERAGE when you want the typical value per row rather than the total.
• AGG_WEIGHTED_MEAN when rows carry weights and you want a weighted average.
• AGG_DISTINCT_SUM when a value repeats on several rows of the same key and must be added once per key.
• WIN_RUNNING_SUM when you want the total so far, row by row.

Glossary: missing-value

Skill: op-agg-sum

AGG_VARIANCE

Spread of a numeric field as the average squared distance from its mean, in squared units, over all rows or per group.

Level: intermediate

Questions it answers:

• How much does the order value vary within each channel, in squared units?
• Which sites show the most variable readings?

Use cases by domain:

• ops: Variability of daily demand per product, for a safety-stock formula.
• science: Variance of a measurement across every unit of a fully measured batch.

Assumptions:

• Missing values are skipped.
• Population form: the squared distances are averaged over n, not n - 1.

Use something else:

• AGG_STDDEV when you want the spread in the field’s own units, which is easier to read.
• AGG_WELFORD when you want the sample variance (dividing by n - 1) with the mean and n, as pooling and study-size planning expect.

Glossary: variance, standard-deviation, mean

Skill: op-agg-variance

AGG_VEC_MEAN

Average of each item of a battery or mix, as one list per group: the group’s average profile across the items.

Level: intermediate

Also known as: average profile, mean vector, centroid, item means

Questions it answers:

• What is each segment’s average rating on each of the eight brand attributes?
• What does the typical store’s channel mix look like in each region?

Use cases by domain:

• survey: Per-segment average of every item in an agree-disagree battery, ready to chart as profile lines.
• ops: Average share of each sales channel per region, read from one list.
• science: Mean of each measured variable per treatment group, kept together as one profile.

Assumptions:

• Each item is averaged over the rows that answered it; a skipped item leaves only that item out of the row.
• An item no row answered has no average rather than 0.

Use something else:

• ATTR_SCALE_SCORE when you want one score per row from the items.
• AGG_AVERAGE when you want the average of a single field.
• MAT_COVARIANCE when you want how the items vary together.

Glossary: mean, missing-value

Skill: op-agg-vec-mean

AGG_VEC_SUM

Total of each item of a set of related fields, as one list per group.

Level: intermediate

Also known as: item totals, column totals, sum vector

Questions it answers:

• How many units of each of the six products did each store sell?
• What is the total spend per channel for every region, in one list?

Use cases by domain:

• survey: Count of yes answers to each item of a checklist battery per segment.
• ops: Total volume per product line per site, kept together as one list.
• science: Total count of each observed category per sample site.

Assumptions:

• Each item is totalled over the rows that answered it; a skipped item adds nothing to that item.
• An item no row answered totals 0.

Use something else:

• AGG_VEC_MEAN when you want each item’s average rather than its total.
• AGG_SUM when you want the total of a single field.

Glossary: missing-value

Skill: op-agg-vec-sum

AGG_WEIGHTED_MEAN

Average of a numeric field where each row counts in proportion to a weight field, such as a survey weight.

Level: intermediate

Questions it answers:

• What is the weighted average satisfaction per region?
• What is the average price per unit when each order counts by its quantity?

Use cases by domain:

• survey: Population-weighted mean score per segment.
• ops: Volume-weighted average price per product.
• science: Precision-weighted mean of repeated measurements.

Assumptions:

• Rows missing the value or the weight, or with weight 0, are left out of the average; when no row is left (or the weights add up to 0) it reads 0, not empty, so check sum_weights in the components.
• Negative weights are not refused and can make the result unstable.

Use something else:

• AGG_AVERAGE when every row should count equally.
• AGG_RATIO when you want one total divided by another.

Glossary: weighting, mean, effective-sample-size

Skill: op-agg-weighted-mean

AGG_WELFORD

Mean, sample variance and row count of a numeric field in one result: what a comparison of group means needs.

Level: intermediate

Questions it answers:

• What are the mean, variance and size of each cell, so cells can be tested against each other?
• How do the average and spread of scores compare across segments?

Use cases by domain:

• survey: Per-cell mean score, variance and base feeding a t-test overlay on a crosstab.
• science: Per-arm summary statistics for a two-sample comparison.

Assumptions:

• Missing values are skipped; only plain integer and float fields are accepted.
• The variance divides by n - 1 and is 0 when fewer than two rows have a value.

Use something else:

• AGG_AVERAGE when you want only the average.
• AGG_STDDEV when you want the population spread (dividing by n) on its own.
• TEST_WELCH when you want the comparison itself on raw rows.

Glossary: mean, variance, sample-size, t-statistic

Skill: op-agg-welford

AGG_ZSCORE

Population mean and standard deviation of a numeric field, for standardizing it; the value itself is always 0.

Level: intermediate

Questions it answers:

• What centre and scale standardize this field in each group?
• How many standard deviations does the group’s last row (in row order) sit from the group’s average, that row included?

Use cases by domain:

• ops: How far the group’s last row, in a fixed row order, sits from the group’s centre.
• science: Group centre and scale for standardizing a measurement by hand.

Assumptions:

• Missing values are skipped; the spread is the population form (dividing by n).
• The last row is the last in row order, not the latest by date, and it is part of the mean and spread it is compared with, which damps its own score; for a reading against its past use OVERLAY_ZSCORE_VS_ROLLING.
• Not streamable: the whole group is read before the result is ready.

Use something else:

• ATTR_ZSCORE when you want a z-score on every row.
• OVERLAY_ZSCORE_VS_TOTAL when you want each group’s value against all groups.
• AGG_STDDEV when you want only the spread.

Glossary: z-score, mean, standard-deviation

Skill: op-agg-zscore