DSTDEVP

Microsoft publishes this example's result as 2.6532998. Derived independently from the documented definition ("the true standard deviation ... if the data in the database is the entire population"). Criteria A3:A5 is the Tree column header plus the two tree names, so it selects the five apple and pear records (yields 14, 10, 10, 8 and 6) and excludes the single cherry. Their yields are 14, 10, 10, 8, 6 with mean 9.6 and squared deviations summing to 35.2; the population variance is 35.2/5 = 7.04 and the population standard deviation is sqrt(7.04) = 2.6532998322843198. Rounded to the seven decimals the page prints, that is exactly 2.6532998, so the published figure is reproduced. SIBLING PAGES DISAGREE ABOUT THE DOCUMENTED RANGE, AND ONE OF THE TWO READINGS CANNOT WORK. All six database-function pages in this batch (DCOUNTA, DPRODUCT, DSTDEV, DSTDEVP, DVAR, DVARP) print a byte-identical example table, but they cite two different database ranges for it: DCOUNTA, DVAR and DVARP write A4:E10 while DPRODUCT, DSTDEV and DSTDEVP write A5:E11 -- and DSTDEV repeats its version in prose ("if the data in A5:E11 is only a sample"). For that table pasted at A1 the database header row (Tree/Height/Age/Yield/Profit) is row 4 and the last record is row 10, so A4:E10 is the reading that works; A5:E11 starts one row BELOW the header, which leaves the field label "Yield" unresolvable and could not produce any of the six published results. Every one of those results is reproduced by the A4:E10 reading, which is the one used here (shifted two rows, as A6:E12). Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Derived independently from the documented definition ("the true standard deviation ... if the data in the database is the entire population"). Criteria A3:A5 is the Tree column header plus the two tree names, so it selects the five apple and pear records (yields 14, 10, 10, 8 and 6) and excludes the single cherry. Their yields are 14, 10, 10, 8, 6 with mean 9.6 and squared deviations summing to 35.2; the population variance is 35.2/5 = 7.04 and the population standard deviation is sqrt(7.04) = 2.6532998322843198. sqrt(7.04) = 2.6532998322843198 rounds to 2.653299832 at 9 dp. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Column 4 of A6:E12 is Yield, so the positional field form must equal the label form. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Derived from the documented definition, and deliberately paired with DSTDEV_single_record_div0 to separate the two estimators. DSTDEVP divides by n rather than n-1, so a one-record population has a well-defined standard deviation of 0 -- where the sample estimator DSTDEV has none. An engine that returns the same answer for both cases has confused the two. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear". EXECUTED RESULT: all four LibreOffice builds return the documented 0, so the population estimator is correctly distinguished from the sample estimator, which errors on the same data.

Microsoft publishes this example's result as 2.6532998. Derived independently from the documented definition ("the true standard deviation ... if the data in the database is the entire population"). Criteria A3:A5 is the Tree column header plus the two tree names, so it selects the five apple and pear records (yields 14, 10, 10, 8 and 6) and excludes the single cherry. Their yields are 14, 10, 10, 8, 6 with mean 9.6 and squared deviations summing to 35.2; the population variance is 35.2/5 = 7.04 and the population standard deviation is sqrt(7.04) = 2.6532998322843198. Rounded to the seven decimals the page prints, that is exactly 2.6532998, so the published figure is reproduced. SIBLING PAGES DISAGREE ABOUT THE DOCUMENTED RANGE, AND ONE OF THE TWO READINGS CANNOT WORK. All six database-function pages in this batch (DCOUNTA, DPRODUCT, DSTDEV, DSTDEVP, DVAR, DVARP) print a byte-identical example table, but they cite two different database ranges for it: DCOUNTA, DVAR and DVARP write A4:E10 while DPRODUCT, DSTDEV and DSTDEVP write A5:E11 -- and DSTDEV repeats its version in prose ("if the data in A5:E11 is only a sample"). For that table pasted at A1 the database header row (Tree/Height/Age/Yield/Profit) is row 4 and the last record is row 10, so A4:E10 is the reading that works; A5:E11 starts one row BELOW the header, which leaves the field label "Yield" unresolvable and could not produce any of the six published results. Every one of those results is reproduced by the A4:E10 reading, which is the one used here (shifted two rows, as A6:E12). Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Derived independently from the documented definition ("the true standard deviation ... if the data in the database is the entire population"). Criteria A3:A5 is the Tree column header plus the two tree names, so it selects the five apple and pear records (yields 14, 10, 10, 8 and 6) and excludes the single cherry. Their yields are 14, 10, 10, 8, 6 with mean 9.6 and squared deviations summing to 35.2; the population variance is 35.2/5 = 7.04 and the population standard deviation is sqrt(7.04) = 2.6532998322843198. sqrt(7.04) = 2.6532998322843198 rounds to 2.653299832 at 9 dp. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Column 4 of A6:E12 is Yield, so the positional field form must equal the label form. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Derived from the documented definition, and deliberately paired with DSTDEV_single_record_div0 to separate the two estimators. DSTDEVP divides by n rather than n-1, so a one-record population has a well-defined standard deviation of 0 -- where the sample estimator DSTDEV has none. An engine that returns the same answer for both cases has confused the two. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear". EXECUTED RESULT: all four LibreOffice builds return the documented 0, so the population estimator is correctly distinguished from the sample estimator, which errors on the same data.

Microsoft publishes this example's result as 2.6532998. Derived independently from the documented definition ("the true standard deviation ... if the data in the database is the entire population"). Criteria A3:A5 is the Tree column header plus the two tree names, so it selects the five apple and pear records (yields 14, 10, 10, 8 and 6) and excludes the single cherry. Their yields are 14, 10, 10, 8, 6 with mean 9.6 and squared deviations summing to 35.2; the population variance is 35.2/5 = 7.04 and the population standard deviation is sqrt(7.04) = 2.6532998322843198. Rounded to the seven decimals the page prints, that is exactly 2.6532998, so the published figure is reproduced. SIBLING PAGES DISAGREE ABOUT THE DOCUMENTED RANGE, AND ONE OF THE TWO READINGS CANNOT WORK. All six database-function pages in this batch (DCOUNTA, DPRODUCT, DSTDEV, DSTDEVP, DVAR, DVARP) print a byte-identical example table, but they cite two different database ranges for it: DCOUNTA, DVAR and DVARP write A4:E10 while DPRODUCT, DSTDEV and DSTDEVP write A5:E11 -- and DSTDEV repeats its version in prose ("if the data in A5:E11 is only a sample"). For that table pasted at A1 the database header row (Tree/Height/Age/Yield/Profit) is row 4 and the last record is row 10, so A4:E10 is the reading that works; A5:E11 starts one row BELOW the header, which leaves the field label "Yield" unresolvable and could not produce any of the six published results. Every one of those results is reproduced by the A4:E10 reading, which is the one used here (shifted two rows, as A6:E12). Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Derived independently from the documented definition ("the true standard deviation ... if the data in the database is the entire population"). Criteria A3:A5 is the Tree column header plus the two tree names, so it selects the five apple and pear records (yields 14, 10, 10, 8 and 6) and excludes the single cherry. Their yields are 14, 10, 10, 8, 6 with mean 9.6 and squared deviations summing to 35.2; the population variance is 35.2/5 = 7.04 and the population standard deviation is sqrt(7.04) = 2.6532998322843198. sqrt(7.04) = 2.6532998322843198 rounds to 2.653299832 at 9 dp. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Column 4 of A6:E12 is Yield, so the positional field form must equal the label form. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear".

Derived from the documented definition, and deliberately paired with DSTDEV_single_record_div0 to separate the two estimators. DSTDEVP divides by n rather than n-1, so a one-record population has a well-defined standard deviation of 0 -- where the sample estimator DSTDEV has none. An engine that returns the same answer for both cases has confused the two. Set up from Microsoft's own worked example for the database functions: the orchard table, which is one criteria header row, two criteria rows, a database header row and six records. HARNESS PLACEMENT: the page pastes the table at A1, but this harness writes the formula under test into cell F1, which the table's second "Height" criteria header would occupy, so the whole block is pasted two rows lower instead. Every relative offset the page describes is preserved -- the database header is still the row immediately below the last criteria row -- which puts the criteria at A3:F5 and the database at A6:E12 here. One further deviation: the page writes its tree criteria as the cell entry ="=Apple" (the documented trick for storing the literal text =Apple, which forces an exact match). openpyxl cannot author that cell -- any string beginning with = is serialized as a formula -- so the criteria cells here hold the plain text Apple and Pear. On this data the two forms select the same records, because no other tree name in the table begins with "Apple" or "Pear". EXECUTED RESULT: all four LibreOffice builds return the documented 0, so the population estimator is correctly distinguished from the sample estimator, which errors on the same data.