# 參考資料：進階函數

## 和 concept_E14513FE464F4491AD0D4130D4EE621C

NOTE
0 (零) 表示 False，其他值表示 True。
``````AND(logical_test1,[logical_test2],...)
``````

logical_test1

logical_test2

## 近似相異計數 (維度) concept_000776E4FA66461EBA79910B7558D5D7

``````Approximate Count Distinct (dimension)
``````

### 比較計數函數 section_440FB8FB44374459B2C6AE2DA504FC0B

Approximate Count Distinct() 是改良 Count() 和 RowCount() 函數後的成果。其可將建立的量度用於任何維度報表，藉此演算不同維度項目的近似計數。例如，用於「行動裝置類型」報表中的客戶 ID 計數。

## 反餘弦 (列) concept_1DA3404F3DDE4C6BAF3DBDD655D79C7B

``````ACOS(metric)
``````

## 反正弦 (列) concept_90F00DEC46BA47F8A21493647D9668CD

``````ASIN(metric)
``````

## 反正切 (列) concept_3408520673774A10998E9BD8B909E90C

``````ATAN(metric)
``````

## 指數迴歸：預計 Y (列) concept_25615693312B4A7AB09A2921083502AD

``````ESTIMATE.EXP(metric_X, metric_Y)
``````

metric_X

metric_Y

## Cdf-T concept_4E2F2673532A48B5AF786521DE428A66

``````cdf_t( -∞, n ) = 0
cdf_t(  ∞, n ) = 1
cdf_t( 3, 5 ) ? 0.99865
cdf_t( -2, 7 ) ? 0.0227501
cdf_t( x, ∞ ) ? cdf_z( x )
``````

## Cdf-Z concept_99C97ACC40A94FADBCF7393A17BC2D12

``````cdf_z( -∞ ) = 0
cdf_z( ∞ ) = 1
cdf_z( 0 ) = 0.5
cdf_z( 2 ) ? 0.97725
cdf_z( -3 ) ? 0.0013499
``````

## 上限 (列) concept_A14CDB1E419B4AA18D335E5BA2548346

``````CEILING(metric)
``````

## 餘弦 (列) concept_DD07AA1FB08145DC89B69D704545FD0A

``````COS(metric)
``````

## 立方根 concept_BD93EFA45DF7447A8F839E1CA5B5F795

``````CBRT(metric)
``````

## 累積 concept_3D3347797B6344CE88B394C3E39318ED

``````| Date | Rev  | cumul(0,Rev) | cumul(2,Rev) |
|------+------+--------------+--------------|
| May  | \$500 | \$500         | \$500         |
| June | \$200 | \$700         | \$700         |
| July | \$400 | \$1100        | \$600         |
``````

## 累積平均值 concept_ABB650962DC64FD58A79C305282D3E61

NOTE

``````cumul(revenue)/cumul(visitor)
``````

## 指數迴歸_ 相關係數 (表格) concept_C18BBFA43C1A499293290DF49566D8D8

``````CORREL.EXP(metric_X, metric_Y)
``````

metric_X

metric_Y

## 指數迴歸：截距 (表格) concept_0047206C827841AD936A3BE58EEE1514

``````INTERCEPT.EXP(metric_X, metric_Y)
``````

metric_X

metric_Y

## 指數迴歸：斜率 (表格) concept_230991B0371E44308C52853EFA656F04

``````SLOPE.EXP(metric_X, metric_Y)
``````

metric_X

metric_Y

## 下限 (列) concept_D368150EC3684077B284EE471463FC31

``````FLOOR(metric)
``````

## 雙曲餘弦 (列) concept_79DD5681CE9640BDBA3C3F527343CA98

``````COSH(metric)
``````

## 雙曲正弦 (列) concept_96230731600C45E3A4E823FE155ABA85

``````SINH(metric)
``````

## 雙曲正切 (列) concept_BD249013732F462B9863629D142BCA6A

``````TANH(metric)
``````

## IF (列) concept_6BF0F3EAF3EF42C288AEC9A79806C48E

``````IF(logical_test, [value_if_true], [value_if_false])
``````

logical_test

[value_if_true]

[value_if_false]

## 線性迴歸_ 相關係數 concept_132AC6B3A55248AA9C002C1FBEB55C60

Y = a X + b。傳回相關係數

## 線性迴歸_ 截距 concept_E44A8D78B802442DB855A07609FC7E99

Y = a X + b。傳回 b。

## 指數迴歸_ 預計 Y concept_9612B9BF106D4D278648D2DF92E98EFC

Y = a X + b。傳回 Y。

## 線性迴歸_ 斜率 concept_12352982082A4DDF824366B073B4C213

Y = a X + b。傳回 a。

## 以 10 為底的對數 (列) concept_4C65DF9659164261BE52AA5A95FD6BC1

``````LOG10(metric)
``````

## 對數迴歸：相關係數 (表格) concept_F3EB35016B754E74BE41766E46FDC246

``````CORREL.LOG(metric_X,metric_Y)
``````

metric_X

metric_Y

## 對數迴歸：截距 (表格) concept_75A3282EDF54417897063DC26D4FA363

``````INTERCEPT.LOG(metric_X, metric_Y)
``````

metric_X

metric_Y

## 對數迴歸：預計 Y (列) concept_5F3A9263BBB84E6098160A4DFB9E3607

``````ESTIMATE.LOG(metric_X, metric_Y)
``````

metric_X

metric_Y

## 對數迴歸：斜率 (表格) concept_B291EFBE121446A6B3B07B262BBD4EF2

``````SLOPE.LOG(metric_A, metric_B)
``````

metric_A

metric_B

## 自然對數 concept_D3BE148A9B84412F8CA61734EB35FF9E

``````LN(metric)
``````

## NOT concept_BD954C455A8148A3904A301EC4DC821E

``````NOT(logical)
``````

logical

## 或 (列) concept_AF81A33A376C4849A4C14F3A380639D2

NOTE
0 (零) 表示 False，其他值表示 True。
``````OR(logical_test1,[logical_test2],...)
``````

logical_test1

logical_test2

## Pi concept_41258789660D4A33B5FB86228F12ED9C

``````PI()
``````

PI 函數沒有引數。

## 乘冪迴歸：相關係數 (表格) concept_91EC2CFB5433494F9E0F4FDD66C63766

``````CORREL.POWER(metric_X, metric_Y)
``````

metric_X

metric_Y

## 乘冪迴歸：截距 (表格) concept_7781C85597D64D578E19B212BDD1764F

`````` INTERCEPT.POWER(metric_X, metric_Y)
``````

metric_X

metric_Y

## 乘冪迴歸：預計 Y (列) concept_CD652C0A921D4EFBA8F180CB8E486B18

`````` ESTIMATE.POWER(metric_X, metric_Y)
``````

metric_X

metric_Y

## 乘冪迴歸：斜率 (表格) concept_5B9E71B989234694BEB5EEF29148766C

``````SLOPE.POWER(metric_X, metric_Y)
``````

metric_X

metric_Y

## 二次迴歸：相關係數 (表格) concept_9C9101A456B541E69BA29FCEAC8CD917

``````CORREL.QUADRATIC(metric_X, metric_Y)
``````

metric_X

metric_Y

## 二次迴歸：截距 (表格) concept_69DC0FD6D38C40E9876F1FD08EC0E4DE

``````INTERCEPT.POWER(metric_X, metric_Y)
``````

metric_X

metric_Y

## 二次迴歸：預計 Y (列) concept_2F1ED70B1BDE4664A61CC09D30C39CBB

``````ESTIMATE.QUADRATIC(metric_A, metric_B)
``````

metric_A

metric_B

## 二次迴歸：斜率 (表格) concept_0023321DA8E84E6D9BCB06883CA41645

``````SLOPE.QUADRATIC(metric_X, metric_Y)
``````

metric_X

metric_Y

## 倒數迴歸：相關係數 (表格) concept_EBEC509A19164B8AB2DBDED62F4BA2A5

``````CORREL.RECIPROCAL(metric_X, metric_Y)
``````

metric_X

metric_Y

## 倒數迴歸：截距 (表格) concept_2DA45B5C69F140EC987649D2C88F19B3

``````INTERCEPT.RECIPROCAL(metric_A, metric_B)
``````

metric_X

metric_Y

## 倒數迴歸：預計 Y (列) concept_2CF4B8F417A84FE98050FE488E227DF8

``````ESTIMATE.RECIPROCAL(metric_X, metric_Y)
``````

metric_X

metric_Y

## 倒數迴歸：斜率 (表格) concept_8A8B68C9728E42A6BFDC6BD5CBDCCEC5

``````SLOPE.RECIPROCAL(metric_X, metric_Y)
``````

metric_X

metric_Y

## 正弦 (列) concept_21C8C3AA835947A28B53A4E756A7451E

``````SIN(metric)
``````

## T 分數 concept_80D2B4CED3D0426896B2412B4FC73BF7

Z 分數的別名，即平均值偏差除以標準差

## T 檢定 concept_A1F78F4A765348E38DBCAD2E8F638EB5

`X` 是 t 檢定的統計資料，且經常會是基於量度的公式 (例如 zscore)，並在每列進行評估。

1. 用其找出極端值：

code language-none
``````t_test( zscore(bouncerate), row-count-1, 2)
``````
2. 將其與 `if` 合併，以便忽略非常高或非常低的反彈率，然後統計其他項目上的造訪率：

code language-none
``````if ( t_test( z-score(bouncerate), row-count, 2) < 0.01, 0, visits )
``````

## 正切 concept_C25E00CB17054263AB0460D9EF94A700

``````TAN (metric)
``````

## Z 分數 (列) concept_96BEAC79476C49B899DB7E193A5E7ADD

Z 分數的方程式為：

NOTE
μ (mu) 和 σ (sigma) 會自動從量度中計算得出。

Z 分數 (量度)

## Z 檢定 concept_2A4ADD6B3AEB4A2E8465F527FAFC4C23

NOTE

recommendation-more-help
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