Dowell Normality Test API
Elevate your data analysis with Dowell’s Normality Test
Dowell
Normality Test API
Welcome to the Dowell Normality Test API documentation! Uncover the secrets hidden within your datasets with our powerful API that performs a rigorous normality test using the a combination of standard statistical test method. Whether you’re a data enthusiast, researcher, or analyst, this API empowers you to understand the distribution characteristics of your data like never before.
Key Features
1.Precise Normality Assessment
Curious about the normality of your dataset? Our API employs the Dowell method to precisely determine whether your data follows a normal distribution.
2.Customizable Parameters
Adjust key parameters like allowable error, individual deviation, and more to tailor the test to your dataset’s unique characteristics.
3.Comprehensive Analysis
Dive into the nitty-gritty details! Get a comprehensive analysis of your dataset, including insights on normality status, curve alignment, slope deviations, and evidence counters. Obtain a comprehensive analysis of the normality test, including various metrics for each series in the dataset.
Metrics include
- Normality Status: Indicates whether the dataset follows a normal distribution.
- Area Under the Curve (AUC) Z-Scores: The area under the curve (AUC) Z-scores.The Z-score represents the number of standard deviations a data point is away from the mean.
- Actual Area: The area under the dataset’s probability density function curve.
- Rectangle Area: The area under the histogram of the dataset. The total area under the normal distribution curve is equal to 1. Since the curve extends infinitely in both directions, it’s not possible to compute the area for the entire curve.
- Individual Deviation: An individual deviation in this context would refer to how much a single data point differs from the mean, in terms of standard deviations.A positive score indicates that the data point is above the mean, while a negative score indicates that the data point is below the mean.
- Kurtosis Classification: Classification of the dataset’s kurtosis (peakedness) as “Leptokurtic,” “Platykurtic,” or “Mesokurtic.”
- Slopes and Slope Percentage Deviations: Slopes of the cumulative distribution function and their deviation percentages.
- Evidence Counters: Statistical checks for various features, including:
- Mean equals to Median, with deviations considered.
- Mean equals to Mode, with deviations considered for both.
- Mode equals to Median.
- Skewness equals to 0, with standard deviation deviation considered.
- Kurtosis curve alignment.
- Points in Range1, Range2, and Range3 checks.
- Satisfies sigmoid function whose mirror image will give a bell-shaped curve.
- Rotational Symmetry check.
Getting started
Step 1: Obtain the API key
Before diving into the API integration, make sure that you have the necessary API key. You can generate your API key through the DoWell API Key Software
Step 2: Set the base URL
To make requests to the API, set the base url as https://100023.pythonanywhere.com/public
Syep 3: Structure Your Requests
Craft your API requests by refering to the guidelines mentioned in the Postman Documentation
Step 4: Handle Errors Gracefully The API follows standard HTTP status codes for error handling. Be sure to implement error handling in your code to gracefully manage potential errors and informative error messages.
Step 5: Parse API Responses When you receive a response from the API, ensure you parse the data accurately to extract the relevant information you need.
Error Handling
The API may return the following error responses:
400 Bad Request: If the request payload is invalid or missing required fields.500 Internal Server Error: If an unexpected error occurs during the calculation process
Postman Documentation
For detailed API documentation, including endpoint descriptions, request and response examples, and authentication details, please refer to the API documentation
Dowell Normality Test API Demostrative Scenarios
In the following scenarios, Dowell will furnish comprehensive instructions on obtaining the Service key and guide you through the steps to use the API. You’ll find examples in various formats such as Python, PHP, React, Flutter, and WordPress in the tabs below. Feel free to explore the examples in each tab for practical insights.
Dowell Normality Test API Use Cases
Discover the magic of DoWell’s Normality Test API! From predicting stock market trends to unlocking secrets in genetic research and ensuring top-notch manufacturing quality, this tool is a game-changer. Dive into our captivating video to witness real-world applications that will leave you amazed and inspired!
Frequently Asked Questions (FAQs) about Dowell Normality Test API
1. What parameters can be adjusted when using the Dowell Normality Test API?
Key parameters such as allowable error, individual deviation, number of bins for the histogram, and slope percentage deviation can be adjusted to tailor the test to the characteristics of your dataset.
2. What does the "Normality Status" metric indicate in the analysis results?
The “Normality Status” metric indicates whether the dataset follows a normal distribution or not based on the test performed by the API.
3. How does the API handle error responses?
The API follows standard HTTP status codes for error handling. A 400 Bad Request may occur if the request payload is invalid or missing required fields, while a 500 Internal Server Error may occur if an unexpected error occurs during the calculation process.
4. Can I perform a normality test on multiple datasets using the API?
Yes, you can perform normality tests on multiple datasets by providing unique process IDs for each dataset in your API requests.
5. What is the significance of the "Area Under the Curve (AUC) Z-Scores" metric?
The “Area Under the Curve (AUC) Z-Scores” metric represents the number of standard deviations a data point is away from the mean, helping to assess the distribution of the dataset.
6. What kind of support is available if I encounter any issues while using the API?
If you encounter any issues, have questions, or need assistance with Dowell Normality Test API, you can contact the support team for prompt assistance. Contact us at Dowell@dowellresearch.uk
7. What does the "Kurtosis Classification" metric indicate?
The “Kurtosis Classification” metric categorizes the dataset’s kurtosis (peakedness) as “Leptokurtic,” “Platykurtic,” or “Mesokurtic,” providing insights into the shape of the distribution.
8. What is the purpose of setting the base URL when making requests to the API?
Setting the base URL allows you to make requests to the API endpoint, enabling seamless integration with your application or analysis environment.
9. How does the API compute the "Rectangle Area" metric?
The “Rectangle Area” metric represents the area under the histogram of the dataset, providing insights into the distribution’s shape and characteristics.
10. What other APIs does Dowell UX Living Lab provide besides Normality Test API?
Dowell UX Living Lab offers a wide range of APIs to enhance user experience and streamline various processes. To explore the full list of APIs available, including Dowell Email, Dowell Newsletter, Samanta Content Evaluator, and many more.
For more details and to explore the complete list of APIs provided by Dowell UX Living Lab, please visit our documentation page.
How To Get The API Key
- Access the Dowellstore website through this link: https://dowellstore.org and login. Once on the website, navigate to the API service section and activate the DOWELL NORMALITY TEST API service. The API Key you need can be found in the dashboard, provided there as the service key. For more information follow the instructions in the videos found at the link below.
[How to get API key and redeem your voucher Step-by-Step Tutorial] - You can also find a step-by-step guide on how to get the API key and activate the API(s) by following this link Get The Service Key
- Then set the API KEY as part of the url parameter as shown below. Replace with your actual API key. The URL format: https://www.socialmediaautomation.uxlivinglab.online/api/v1/generate-sentences/
- Note: Make sure to activate your API from Dowell API Key System link provided above.
Python Example
The Python code provided facilitates interaction with the Dowell Normality Test API by sending an HTTP POST request. It allows users to customize analysis parameters, enabling precise assessment of normality and providing detailed insights into deviation and various dataset metrics
import requests
url = 'https://100023.pythonanywhere.com/public'
api_key = 'your-api-key' # Replace with your actual API key
data = {
"api_key": api_key,
"Process_id": 8020,
"allowable_error": 5,
"ind_dev": 1,
"bins": 100,
"slope_percentage_deviation": 50
}
response = requests.post(url, json=data)
print(response.json())
PHP Example
In the PHP code snippet, an HTTP POST request is constructed to interface with the Dowell Normality Test API. Users can adjust parameters for tailored analysis, which yields comprehensive results. These results include valuable information about normality, deviation, and additional dataset metrics.
$url = 'https://100023.pythonanywhere.com/public';
$api_key = 'your-api-key'; // Replace with your actual API key
$data = [
"api_key" => $api_key,
"Process_id" => 8020,
"allowable_error" => 5,
"ind_dev" => 1,
"bins" => 100,
"slope_percentage_deviation" => 50
];
$options = [
'http' => [
'header' => "Content-type: application/json\r\n",
'method' => 'POST',
'content' => json_encode($data)
]
];
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
echo $result;
React Example
The React code leverages the Fetch API to initiate an HTTP POST request to the Dowell Normality Test API. This request can be configured with specific analysis parameters, resulting in a detailed analysis. The results, displayed within a React component, offer insights into dataset normality, deviation, and various metrics.
import React, { useState, useEffect } from 'react';
const MyComponent = () => {
const [response, setResponse] = useState(null);
useEffect(() => {
const fetchData = async () => {
const url = 'https://100023.pythonanywhere.com/public';
const api_key = 'your-api-key'; // Replace with your actual API key
const data = {
api_key: api_key,
Process_id: 8020,
allowable_error: 5,
ind_dev: 1,
bins: 100,
slope_percentage_deviation: 50
};
const response = await fetch(url, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(data),
});
const result = await response.json();
setResponse(result);
};
fetchData();
}, []);
return (
<div>
<pre>{JSON.stringify(response, null, 2)}</pre>
</div>
);
};
Flutter Example
The Flutter code facilitates communication with the Dowell Normality Test API through an HTTP POST request. It allows for customization of analysis parameters, leading to a comprehensive analysis of data. The results include valuable information on normality, deviation, and various metrics associated with the dataset.
import 'dart:convert';
import 'package:http/http.dart as http;
void fetchData() async {
final url = Uri.parse('https://100023.pythonanywhere.com/public');
final api_key = 'your-api-key'; // Replace with your actual API key
final data = {
"api_key": api_key,
"Process_id": 8020,
"allowable_error": 5,
"ind_dev": 1,
"bins": 100,
"slope_percentage_deviation": 50
};
final response = await http.post(
url,
headers: {'Content-Type': 'application/json'},
body: jsonEncode(data),
);
final result = jsonDecode(response.body);
print(result);
}
void main() {
fetchData();
}
WordPress Example
Step 1: Set up the API name, unique ID and the base url(below). It’s a prerequisite to have WP-GET API plugin installed in your wordpress website
Step 2: Set up the api endpoint and the request body. the endpoint should have the api key and request body should have the necessary body post fields
Step 3: Test endpoint to get Json response from the API.
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