[release-12.1.1] Transformations: Rename Regression Analysis to Trendline' (#108815)

Transformations: Rename Regression Analysis to Trendline (#108631)

* Rename regression analysis transformation

* fix a couple translations

* remove extra word

* Fix tests

* Change frame name to use regression

(cherry picked from commit ffb8f4ea0c)

docs/sources/panels-visualizations/query-transform-data/transform-data/index.md

@@ -1473,17 +1473,19 @@ If you have multiple types it will default to string type.
 
 {{< figure src="/media/docs/grafana/transformations/screenshot-grafana-11-2-transpose-transformation.png" class="docs-image--no-shadow" max-width= "1100px" alt="Before and after transpose transformation" >}}
 
-### Regression analysis
+### Trendline
 
 Use this transformation to create a new data frame containing values predicted by a statistical model. This is useful for finding a trend in chaotic data. It works by fitting a mathematical function to the data, using either linear or polynomial regression. The data frame can then be used in a visualization to display a trendline.
 
 There are two different models:
 
-- **Linear regression** - Fits a linear function to the data.
+- **Linear** - Fits a linear function to the data.
   {{< figure src="/static/img/docs/transformations/linear-regression.png" class="docs-image--no-shadow" max-width= "1100px" alt="A time series visualization with a straight line representing the linear function" >}}
-- **Polynomial regression** - Fits a polynomial function to the data.
+- **Polynomial** - Fits a polynomial function to the data.
   {{< figure src="/static/img/docs/transformations/polynomial-regression.png" class="docs-image--no-shadow" max-width= "1100px" alt="A time series visualization with a curved line representing the polynomial function" >}}
 
+> **Note:** This transformation was previously called regression analysis.
+
 [Table panel]: ref:table-panel
 [Calculation types]: ref:calculation-types
 [sparkline cell type]: ref:sparkline-cell-type

public/app/features/transformers/docs/content.ts

@@ -1576,25 +1576,27 @@ ${buildImageContent(
     },
   },
   regression: {
-    name: 'Regression analysis',
+    name: 'Trendline',
     getHelperDocs: function (imageRenderType: ImageRenderType = ImageRenderType.ShortcodeFigure) {
       return `
 Use this transformation to create a new data frame containing values predicted by a statistical model. This is useful for finding a trend in chaotic data. It works by fitting a mathematical function to the data, using either linear or polynomial regression. The data frame can then be used in a visualization to display a trendline.
 
 There are two different models:
 
-- **Linear regression** - Fits a linear function to the data.
+- **Linear** - Fits a linear function to the data.
 ${buildImageContent(
   '/static/img/docs/transformations/linear-regression.png',
   imageRenderType,
   'A time series visualization with a straight line representing the linear function'
 )}
-- **Polynomial regression** - Fits a polynomial function to the data.
+- **Polynomial** - Fits a polynomial function to the data.
 ${buildImageContent(
   '/static/img/docs/transformations/polynomial-regression.png',
   imageRenderType,
   'A time series visualization with a curved line representing the polynomial function'
 )}
+
+> **Note:** This transformation was previously called regression analysis.
   `;
     },
   },

public/app/features/transformers/regression/regression.test.ts

@@ -10,7 +10,7 @@ import {
 
 import { ModelType, RegressionTransformer, RegressionTransformerOptions } from './regression';
 
-describe('Regression transformation', () => {
+describe('Trendline transformation', () => {
   it('it should predict a linear regression to exactly fit the data when the data is f(x) = x', () => {
     const source = [
       toDataFrame({
@@ -45,7 +45,7 @@ describe('Regression transformation', () => {
           name: 'Linear regression',
           fields: [
             { name: 'time', type: FieldType.time, values: [0, 1, 2, 3, 4, 5], config: {} },
-            { name: 'value predicted', type: FieldType.number, values: [0, 1, 2, 3, 4, 5], config: {} },
+            { name: 'value', type: FieldType.number, values: [0, 1, 2, 3, 4, 5], config: {} },
           ],
           length: 6,
         }),
@@ -86,7 +86,7 @@ describe('Regression transformation', () => {
           name: 'Linear regression',
           fields: [
             { name: 'time', type: FieldType.time, values: [0, 1, 2, 3, 4, 5], config: {} },
-            { name: 'value predicted', type: FieldType.number, values: [1, 1, 1, 1, 1, 1], config: {} },
+            { name: 'value', type: FieldType.number, values: [1, 1, 1, 1, 1, 1], config: {} },
           ],
           length: 6,
         }),

public/app/features/transformers/regression/regression.ts

@@ -37,7 +37,7 @@ export const DEGREES = [
 
 export const RegressionTransformer: SynchronousDataTransformerInfo<RegressionTransformerOptions> = {
   id: DataTransformerID.regression,
-  name: 'Regression analysis',
+  name: 'Trendline',
   description: 'Create a new data frame containing values predicted by a statistical model.',
   operator: (options, ctx) => (source) =>
     source.pipe(map((data) => RegressionTransformer.transformer(options, ctx)(data))),
@@ -126,7 +126,7 @@ export const RegressionTransformer: SynchronousDataTransformerInfo<RegressionTra
         fields: [
           { name: xField.name, type: xField.type, values: predictionPoints, config: {} },
           {
-            name: `${getFieldDisplayName(yField, predictFromFrame, frames)} predicted`,
+            name: `${getFieldDisplayName(yField, predictFromFrame, frames)}`,
             type: yField.type,
             values: predictionPoints.map((x) => result.predict(x - normalizationSubtrahend)),
             config: {},

public/locales/en-US/grafana.json

@@ -8665,9 +8665,9 @@
       "type": {
         "loki": {
           "indexed-label_one": "Indexed label",
-          "indexed-label_other": "Indexed labels",
+          "indexed-label_other": "Indexed label",
           "parsedl-label_one": "Parsed field",
-          "parsedl-label_other": "Parsed fields",
+          "parsedl-label_other": "Parsed field",
           "structured-metadata_one": "Structured metadata",
           "structured-metadata_other": "Structured metadata"
         }