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How to Interpret Results Using ANOVA Test?

ANOVA stands for Analysis Of Variance. Ronald Fisher founded ANOVA in the year 1918. The name Analysis Of Variance was derived based on the approach in which the method uses the variance to determine the means, whether they are different or equal.

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Researchers use this statistical method to test for differences between two or more means and to investigate general differences, rather than specific ones, among the means. It assesses the significance of one or more factors by comparing the response variable means at different factor levels.

The null hypothesis states that all population means are equal. The alternative theory proves that at least one population mean is different.

It provides a way to test various null hypotheses at the same time.

General Purpose

The reason for performing this is to see whether any difference exists between the groups on some variable. Today researchers are using ANOVA in many ways. The usage of this depends on the research design.

A t-test can be used to compare two samples, but it is best to use another method when there are more than two samples to be compared.

Assumptions

There are four main assumptions are as follows:

The expected values of the errors are zero

The variances of all the errors are equal to each other

The errors are independent

 The data follows a normal distribution.

ANOVA Types

Following are the different types explained in detail:

1. One Way between groups

H₀: µ₁ = µ₂ = µ₃ = ….. = µₓ

Where µ means group means, and x represents the number of groups. One Way gives a significant result. One way is an omnibus test statistic, which will not let you know which groups differed. To understand the specific group or groups that differed from others, you need to do a post hoc test.

Example of one way ANOVA:

20 people are selected to test the effect of five different exercises. 20 people are divided into 4 groups with 5 members each. Their weights are recorded after a few days. The impact of the exercises on the 5 groups of men is compared. Her weight is the only factor.

Assumptions:

The dependent variable is typically distributed in each group.

Variances are homogeneous.

Independence of observations.

2. One Way ANOVA repeated measures

Repeated measures ANOVA is more or less equal to One Way ANOVA but used for complex groupings.

Repeated measures investigate the following:

Changes in mean scores over three or more time points.

Differences in mean scores under different conditions.

Example of repeated measures:

Research the effect of a 6-month exercise program on weight-reducing in some individuals. You calculate the weight at three points during training to develop a time course for any exercise effect.

You might indulge the same individual in eating a different type of weight-reducing food and rating them according to taste.

In this example, researchers measure the same set of people multiple times on the same dependent variable.

3. Two way between groups

The two-way ANOVA compares the mean difference between groups split into two factors. A two-way ANOVA’s main objective is to find out if there is any interaction between the two independent variables on the dependent variables. It also lets you know whether the effect of one of your independent variables on the dependent variable is the same for all the values of your other independent variable.

Example:

The research on the effect of fertilizers on the yield of rice. You apply five fertilizers of different quality on five plots of land, each cultivating rice. The output from each plot of land is recorded, and the difference between each plot is observed. Here the effect of the fertility of the plots can also be studied. Thus there are two factors, fertilizer and fertility.

Assumptions:

Before starting with your two-way ANOVA, your data should pass through six assumptions to ensure that the data you have is sufficient for performing two-way ANOVA.

The six assumptions are listed below:

Your dependent variable should be measured at the continuous level.

Your two independent variables should contain two or more categorical separate groups for each.

You should have independence of observations.

Avoid any outliers.

The dependent variable should follow a normal distribution for each combination of groups formed by the two independent variables.

Homogeneity of variances.

4. Two-way repeated measures

Two-way repeated measures the mean differences between the groups split into two within the independent variables. A two-way repeated action is often used in research where a dependent variable is measured more than twice under two or more conditions.

Example:

A health researcher wants to find the best way to reduce chronic joint pain suffered by people. The researcher selects two different types of treatments to reduce the level of pain. The two types of treatments are known as ‘conditions.’ Treatment A is a massage program, and Treatment B is an acupuncture program. The researchers give both treatments to all patients for 8 weeks.

Researchers test the patients at three points: the program’s beginning, middle, and end.

The researcher selects 30 patients to take part in the research. But when the first 15 patients undergo Treatment A, the other 15 undergo Treatment B, and vice versa.

At the end of 8 weeks, the researcher uses two-way repeated measures ANOVA to determine if there is any change in the pain due to the interaction between the type of treatment and at which point.

Assumptions:

Your data should pass five assumptions for a two-way repeated measures ANOVA to give the exact result.

To perform statistical analysis, you need to measure your dependent variable at the continuous level.

Your two within-subject factors should consist of at least two definite related groups.

There should be no outliers.

Each combination of related groups should have a normally distributed dependent variable.

The differences between all combinations of related groups should be equal.

Parametric and Non Parametric ANOVA Test

If you have complete information about the population through its parameters, then you can perform a parametric test to analyze the data.

When you have categorical data, you cannot use the ANOVA method; you must use the Chi-square test, which deals with ANOVA interaction.

Hypothesis Testing Procedure – One-way ANOVA

1. Check any necessary assumptions and write a null and alternative hypothesis.

To perform one-way ANOVA, certain assumptions should be there.

The assumptions are as follows.

Each sample is an independent random sample.

The distribution of the response variable follows a normal distribution

The population variances are equal across responses for the group levels. It can be found by dividing the most significant sample standard deviation by a minor sample standard. If it is not greater than two, then assume that the population variances are equal.

2. Calculate an appropriate test statistic

One-way ANOVA uses F-test statistics. Hand calculations require many steps to compute the F ratio, but statistical software like SPSS will calculate the F ratio for you and produce the ANOVA source table.

ANOVA table will give you information about the variability between groups and within groups. The table will provide you with all of the formulae.

Below is an example of a one-way ANOVA table:

Source SS DF MS F

Treatments SST k-1 SST/(k-1) MST/MSE

Error SSE N-k SSE/(N-k)

Total (Corrected) SS N-1

SST means the Sum of squares of treatments, and SSE means the Sum of squares of errors.

DFT, k-1, means degrees of freedom for treatment, and DFE, N-k, means Degrees of freedom for errors.

3. Determine a p-value associated with the test statistic

4. Determine between the null and alternative hypothesis

If the null hypothesis is false, MST should be more significant than MSE.

5. Give a conclusion

Based on your result, write a conclusion per your ANOVA research question.

Multiple Comparison Tests

If you find a significant difference between the groups unrelated to sampling error, you must run several t-tests to test the means between the groups. Researchers conduct several tests to control the type one error rate.

Scheffe’s Test

Modified Bonferroni test

Dunnette’s test

Tukey’s test

Calculations

You can perform ANOVA calculations in three ways: doing hand calculations, using an Excel sheet, or using SPSS software. Let us learn about all the calculations in detail below.

1. ANOVA hand calculations

Step 1

Compute CM

CM = (Total of all observations)2/NTotal

Step 2

Total SS = Sum of squares of all observations – CM

Step 3

Compute SST (Sum of Squares for Treatment)

SST = ∑3i=1 T2i/ni – CM

Step 4

Compute SSE (Sum of Squares for errors)

SSE = SS (Total) – SST

Step 5

Compute MST, MSE, and their ratio F

MST = SST/k-1

MSE = SSE/N-k

F = MST/MSE

2. ANOVA using Excel

To perform a single-factor ANOVA in Excel, follow these simple steps:

Go to Data Tab.

The Excel sheet will display the result for you.

The null hypothesis is rejected if F is greater than the F crit.

3. ANOVA using SPSS

First, download the SPSS software to perform the ANOVA. Here we can see how to perform a One way ANOVA using SPSS.

To perform statistical analysis in SPSS, it is necessary to represent the independent variable numerically. In the sample data set, MAJOR is a string. So first, convert the string variable into a numerical variable. Once your conversion is over, you are ready to do the ANOVA.

Open the SPSS software.

A one-way ANOVA dialog box appears on the screen.

On the left side of the dialog box, you will see a list of all the dependent variables that you measured. Move it into the Dependent list on the right side using the upper arrow button.

In the same way, move the independent variable in the left side list to the Factor box on the right side.

The SPSS output window will appear with six major sections

Descriptive section

Test of Homogeneity of Variances

ANOVA

Multiple Comparisons

Grade Point Average

Graph

Things to be Considered when Running an ANOVA

Data level and assumptions play a crucial role in ANOVA.

To analyze the data appropriately, the researcher should determine whether the data is crossed or nested. If the data is crossed, then each group will receive all aspects.

When the data is nested, applying a different ANOVA method to each group is necessary to analyze the data correctly.

It is more important to calculate the ANOVA effect size. The effect size can tell you the degree to which the null hypothesis is false. A medium effect size is always preferable.

I hope this article gave you a brief overview and interpretation results using it.

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How To Test The Name Of Tag Of An Element Using Protractor?

We will learn to use protractor to test the name of the tag of an element. Protractor is an end-to-end testing framework for Angular and AngularJS applications. Protractor is built on top of WebDriverJS, which is a JavaScript implementation of the WebDriver API, and supports multiple browsers such as Chrome, Firefox, and Safari. It is popular among developers and testers because it provides a simple and effective way to test Angular applications without having to write complex code.

Learning how to test the name of the tag of an element using a Protractor is a crucial aspect of automated testing for web applications. By testing the name of the tag, we can ensure that the elements on the page are properly constructed and functioning as intended. This can help to catch errors and bugs early in the development process, leading to more robust and reliable web applications.

We will take two different examples to test the name of the tag of an element so that you will be able to understand the topic clearly.

Test the tag name of a single element

Test the tag names of multiple elements

Test the tag Name of a Single Element

Let suppose we have one button with the id “btn”. We can find the button using the id and then test the name of the tag using a protractor.

Syntax

Users can follow the below syntax to use the protractor to test the tag name of single element.

expect(elementFinder.getTagName()).toEqual(expectedTagName);

Here elementFinder is the Protractor ElementFinder object that represents the HTML element you want to test. And expectedTagName is a string that represents the expected name of the tag for the HTML element.

Example

In the below example, we have created 3 files – chúng tôi chúng tôi and test.js.

We have a chúng tôi file in a folder conf, and the tests folder contains chúng tôi and test.js.

chúng tôi

This file is the configuration file for Protractor. It sets the browser capabilities to Chrome, specifies the Jasmine framework, and specifies the location of the test script file. It also sets a default timeout interval and the base URL for the tests. The onPrepare function is used to set the browser’s reset URL.

exports.config = { directConnect: true, capabilities: { 'browserName': 'chrome' }, framework: 'jasmine', /* Spec patterns are relative to the current working directory when protractor is called */ specs: ['../tests/test.js'], jasmineNodeOpts: { defaultTimeoutInterval: 30000 } , baseUrl: "file://" + __dirname + "/", onPrepare: function () { browser.resetUrl = "file://"; } } ;

test.html

This HTML page contains a button element with an id of “btn”.

chúng tôi

describe('Testing the name of tag of an element', function() { beforeEach(function() { browser.waitForAngularEnabled(false); browser.get('../tests/test.html'); }); it('should check the tag name of button element', function() { let buttonTag = element(by.id('btn') ); expect(buttonTag.getTagName()).toEqual('button'); }); });

Command to run configuration file

protractor conf.js(file path)

We can see that we are getting all the tests passed, and there is no error.

Test the tag Names of Multiple Elements

Let say we have multiple div elements with different IDs and the same class. Then we do not need to check the tag name for each element using IDs. We can simply check the tag names of multiple elements using the class in the protractor.

Example

This is an example of using a Protractor to test the name of tag when there are multiple elements having the same tag.

test.html

This HTML page contains three div elements with unique IDs and the same class of “collection”.

chúng tôi

describe('Testing tag names of elements', function() { beforeEach(function() { browser.waitForAngularEnabled(false); browser.get('../tests/test.html') ; }); it('should check the tag names of all div elements', function() { let divTags = element.all(by.css('.collection')); /* For each div element, verify that the tag name is 'div' */ divTags.each(function(element) { expect(element.getTagName()).toEqual('div'); } ); } ); } );

In conclusion, by testing the name of the tag of an element using Protractor, we can easily catch errors and bugs and check whether the web page is properly constructed and functioning. Through the provided examples, we can understand how Protractor can be used to test the tag names of single and multiple elements, making it an essential tool for web application development and testing.

How To Perform Dunn S Test In Python

Dunn’s test is a statistical technique for comparing the means of several samples. When it’s required to compare the means of numerous samples to identify which ones are noticeably different from one another, Dunn’s test is frequently employed in a range of disciplines, including biology, psychology, and education. We shall examine Dunn’s test in−depth in this article, along with a python implementation.

What is Dunn’s Test?

Dunn’s test is a statistical analysis used to compare the means of numerous samples. It is a form of multiple comparison test used to compare the means of more than two samples to identify which samples differ substantially from one another.

When the assumption of normality is violated, Dunn’s test is sometimes used with the non-parametric Kruskal−Wallis test to compare the means of several samples. If there are any notable differences between the sample means, the Kruskal−Wallis test is employed to find them. The means of the samples are compared pairwise to identify which samples are substantially different from one another and if there are significant differences. Dunn’s test is then used to compare the means of the samples.

Performing Dunn’s test in Python

To run Dunn’s test in Python, we can use the scikit-posthocs library’s posthoc dunn() method.

The code below demonstrates how to use this function −

Syntax sp.posthoc_dunn(data, p_adjust = 'bonferroni')

Bartlett’s test statistic and the p-value are returned by this function after it receives an array of data.

Parameters

p_adjust is a p−value adjustment method

To demonstrate the test in Python, consider the following scenario: Researchers want to discover if three different fertilizers result in various degrees of plant growth. They choose 30 different plants at random and divide them into three groups of ten, each with a different fertilizer. They measure the height of each plant at the end of one month.

Algorithm

Installing scikit-posthocs library

Specifying the data of growth of 10 plants groupwise

Merging all the 3 groups in one data

Performing Dunn’s test using a Bonferonni correction for the p-values

Example

Using the scikit-posthocs lib to run Dunn’s test is demonstrated here −

!pip install scikit-posthocs #specify the growth of the 10 plants in each group group1 = [9, 10, 16, 9, 10, 5, 7, 13, 10, 9] group2 = [16, 19, 15, 17, 19, 11, 6, 17, 11, 9] group3 = [7, 9, 5, 8, 8, 14, 11, 9, 14, 8] data = [group1, group2, group3] #perform Dunn's test using a Bonferonni correction for the p-values import scikit_posthocs as sp sp.posthoc_dunn(data, p_adjust = 'bonferroni') Output The adjusted p-value for the distinction between groups 1 and 2 is 0.115458. The adjusted p-value for the distinction between groups 1 and 3 is 1.000000. The adjusted p-value for the distinction between groups 2 and 3 is 0.27465. Conclusion

Dunn’s test is widely used in a number of domains, including biology, psychology, and education, where the means of numerous samples must be compared to discover whether samples are substantially different from one another. It is especially beneficial when the assumption of normalcy is violated because it is a non-parametric test that does not rely on this assumption.

Dunn’s test can be used in education to compare the means of many samples of data from various schools or classes in order to find whether schools or classrooms have substantially different means. It might be used, for example, to compare the average test scores of different schools or the average grades of different classrooms.

Apple Reports First Quarter Results

Apple Reports First Quarter Results

Mac sales increased a total of 23 percent, selling 4.13 million. iPod sales saw a seven percent decline, though, with 19.45 million sold. The iPad, however, saw another increase, as it moved an additional 7.33 million units.

We’ll say few words here on how any of this has a bearing on the future of the company and etc., but expect a large amount of thoughts on the subject in the near future.

Check out the full press release below:

Press Release

Apple Reports First Quarter Results

Record Mac, iPhone, iPad Sales Drive Highest Revenue and Earnings Ever

Revenue Grows 71 Percent; Earnings Grow 78 Percent

“We had a phenomenal holiday quarter with record Mac, iPhone and iPad sales,” said Steve Jobs, Apple’s CEO. “We are firing on all cylinders and we’ve got some exciting things in the pipeline for this year including iPhone 4 on Verizon which customers can’t wait to get their hands on.”

“We couldn’t be happier with the performance of our business, generating $9.8 billion in cash flow from operations during the December quarter,” said Peter Oppenheimer, Apple’s CFO. “Looking ahead to the second fiscal quarter of 2011, we expect revenue of about $22 billion and we expect diluted earnings per share of about $4.90.”

Apple will provide live streaming of its Q1 2011 financial results conference call beginning at 2:00 p.m. PST on January 18, 2011 at chúng tôi This webcast will also be available for replay for approximately two weeks thereafter.

© 2011 Apple Inc. All rights reserved. Apple, the Apple logo, Mac, Mac OS and Macintosh are trademarks of Apple. Other company and product names may be trademarks of their respective owners.

Apple Inc.

UNAUDITED CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS

(in millions, except share amounts which are reflected in thousands and per share amounts)

Three Months Ended

December 25, 2010

December 26, 2009

Net sales $ 26,741 $ 15,683

Cost of sales (1) 16,443 9,272

Gross margin 10,298 6,411

Operating expenses:

Research and development (1) 575 398

Selling, general and administrative (1) 1,896 1,288

Total operating expenses 2,471 1,686

Operating income 7,827 4,725

Other income and expense 136 33

Income before provision for income taxes 7,963 4,758

Provision for income taxes 1,959 1,380

Net income $ 6,004 $ 3,378

Earnings per common share:

Basic $ 6.53 $ 3.74

Diluted $ 6.43 $ 3.67

Shares used in computing earnings per share:

Basic 919,294 903,542

Diluted 933,154 919,783

(1) Includes stock-based compensation expense as follows:

Cost of sales $ 52 $ 37

Research and development $ 113 $ 74

Selling, general and administrative $ 134 $ 94

Apple Inc.

UNAUDITED CONDENSED CONSOLIDATED BALANCE SHEETS

(in millions, except share amounts)

December 25, 2010 September 25, 2010

ASSETS:

Current assets:

Cash and cash equivalents $ 10,734 $ 11,261

Short-term marketable securities 16,243 14,359

Accounts receivable, less allowances of $62 and $55, respectively 6,027 5,510

Inventories 885 1,051

Deferred tax assets 1,724 1,636

Vendor non-trade receivables 4,847 4,414

Other current assets 3,467 3,447

Total current assets 43,927 41,678

Long-term marketable securities 32,730 25,391

Property, plant and equipment, net 5,868 4,768

Goodwill 741 741

Acquired intangible assets, net 522 342

Other assets 2,954 2,263

Total assets $ 86,742 $ 75,183

LIABILITIES AND SHAREHOLDERS’ EQUITY:

Current liabilities:

Accounts payable $ 14,301 $ 12,015

Accrued expenses 5,953 5,723

Deferred revenue 3,541 2,984

Total current liabilities 23,795 20,722

Deferred revenue – non-current 1,216 1,139

Other non-current liabilities 7,065 5,531

Total liabilities 32,076 27,392

Commitments and contingencies

Shareholders’ equity:

Common stock, no par value; 1,800,000,000 shares authorized;

921,035,475 and 915,970,050 shares issued and outstanding, respectively

11,502

10,668

Retained earnings 43,050 37,169

Accumulated other comprehensive income/(loss) 114 (46 )

Total shareholders’ equity 54,666 47,791

Total liabilities and shareholders’ equity $ 86,742 $ 75,183

Apple Inc.

UNAUDITED CONDENSED CONSOLIDATED STATEMENTS OF CASH FLOWS

(in millions)

Three Months Ended

December 25, 2010

December 26, 2009

Cash and cash equivalents, beginning of the period $ 11,261 $ 5,263

Operating activities:

Net income 6,004 3,378

Adjustments to reconcile net income to cash generated by operating activities:

Depreciation, amortization and accretion 356 209

Stock-based compensation expense 299 205

Deferred income tax expense 823 425

Changes in operating assets and liabilities:

Accounts receivable, net (517 ) 271

Inventories 166 (121 )

Vendor non-trade receivables (433 ) (95 )

Other current and non-current assets (558 ) (369 )

Accounts payable 2,346 956

Deferred revenue 634 606

Other current and non-current liabilities 653 316

Cash generated by operating activities 9,773 5,781

Investing activities:

Purchases of marketable securities (19,575 ) (12,922 )

Proceeds from maturities of marketable securities 3,279 6,216

Proceeds from sales of marketable securities 6,853 3,199

Payments for acquisition of property, plant and equipment (1,214 ) (376 )

Payments for acquisition of intangible assets (49 ) (5 )

Other (23 ) (70 )

Cash used in investing activities (10,729 ) (3,958 )

Financing activities:

Proceeds from issuance of common stock 208 374

Excess tax benefits from stock-based compensation 454 252

Taxes paid related to net share settlement of equity awards (233 ) (103 )

Cash generated by financing activities 429 523

(Decrease)/increase in cash and cash equivalents (527 ) 2,346

Cash and cash equivalents, end of the period $ 10,734 $ 7,609

Supplemental cash flow disclosure:

Cash paid for income taxes, net $ 826 $ 980

Apple Inc.

Q1 2011 Unaudited Summary Data

How To Install Msixbundle Using Powershell

The MSIXbundle comes with great benefits over having different MSIX packages for specific architectures. It is a more scalable way to distribute your applications. One way to install MSIXBundle is by using Windows PowerShell. The bundle allows PowerShell to install the resources compatible with and targeted for your PC’s specific system architecture. Once the MSIX packages are bundled together, you only need to specify the location or path of your distribution, and PowerShell will handle the rest.

In this article, we discuss how to install MSIXBundle using Windows PowerShell. Like Appx and AppxPackage, you can use MSIXBundle to sideload or install Windows apps from third-party clouds on your Windows computer. This is quite helpful for some apps not in the Microsoft Store. You can also use MSIXBundle to install Windows apps that show errors or when the servers are not responding.

What is an MSIXBundle and how do you open it?

An MSIXBundle is a Windows app package format used to distribute Universal Windows Platform (UWP) and desktop applications for Windows 11/10. It consists of various MSIX packages bundled together and can support specific architectures in your systems, such as x64, ARM, or x86. For instance, the MSIXBundle format allows users to have one MSIX package for two installer versions, x86, and x64; these are put together in one package called a bundle.

Users can open the MSIXBundle by decompressing the file using a PKZip utility program. Like other MSIX package formats, MSIXBundle is compressed in a PKZip file. Once you decompress the file, the MSIXBundle package resources are replaced by MSIX packages that are put together in a bundle. Let us now see how you can install the MSIX bundle using Windows PowerShell.

How to install MSIXBundle using PowerShell

If you continuously get errors when you try to install and run MSIXBundle on Windows, or you want to sideload MS Store apps on several computers, you can use PowerShell to run some commands as an administrator. To install MSIXBundle using PowerShell, use the following easy steps:

On the Windows search box, type PowerShell and select Run as administrator.

Copy and paste the following PowerShell command and then hit Enter to initiate app installation: Add-AppxPackage -Path $AppFilePath

Add-AppxPackage -Path C:tempfilename.msixbundle

Wait until the Windows PowerShell finishes the process, it will indicate the progress.

You can now go to the Start menu and launch the app.

Earlier, we said that you could use the MSIX bundle to install third-party apps that are not in the Microsoft Store. You can follow the steps above, but you must first enable Developer Mode to allow you to install UWP programs from non-MS sites. Follow the steps below to enable the Developer Mode option:

Go to the Windows Settings app and then select Privacy & Security.

Select For Developers and toggle on the button next to Developer Mode.

We hope you can now install the MSIX bundle using Windows PowerShell.

Read: How to install unsigned .Appx app package using PowerShell

How do I install the MSIX packaging tool?

If you want to install the MSIX packaging tool, go to the Microsoft Store and then head to the description page, then select Install to start the process. However, ensure that the Microsoft account you are using is the same as the one for the Windows Insider Program. You can also download the MSIX packaging tool in the enterprise for offline use in the Microsoft Store for Business.

Read: The ms-appinstaller protocol has been disabled

How to install MSIX without the Microsoft Store?

To install an MSIX package without the Microsoft Store, you can use third-party sources like GitHub and then use Windows PowerShell to install it using the steps we discussed above. However, unless the app you want to install is completely missing from the store, installing MSIX from the MS Store is the best way to go.

How To Run Speed Test From The Command Line To Check Internet Connection Speed

The excellent curl and wget tools provide for a simple way to test the speed of an internet connection directly from the command line. Curl is bundled with most unix variations, but Mac users who want to use the wget trick will first need to grab wget for OS X in order for this to work, wget is a simple terminal utility used to download files from the web and ftp and it’s handy to have around for a variety of uses making it worthwhile to have anyway. Curl should be preinstalled on every unix flavor that is even vaguely modern, including all versions of Mac OS X and linux.

Test Internet Connection Speed from the Command Line

This is a fairly simple trick to check download speeds using the official SpeedTest servers, making it a quick and effect means to check an active internet connection. There are two ways to use this, one utilizing curl, the other uses wget.

Run SpeedTest with curl from the Command Line to Determine Internet Connection Download Speeds

The first trick is to use curl, which is able to download remote files from just about anywhere, retrieve headers, and perform tons of other nifty actions. Curl is bundled with every version of Unix and OS X ever made which makes this a nearly universal command to test download speeds on just about any unix-based computer:

The download speed will show as well as elapsed time to complete the download. Here is what this looks like running in a terminal:

The “Test10.zip” file is being sent to /dev/null so don’t worry about taking up disk space with a useless test file.

If you think you’ll use the curl trick often, consider adding it to your profile as an alias:

You’ll probably notice the command itself is quite similar to the wget command string to perform a similar action, so it’s really a matter of preference.

Testing Connection Speed from the Command Line with wget

If you’re already familiar with the command line you know what to do, but others can install wget, then launch Terminal (found in /Applications/Utilities/) and paste the following command string into the terminal:

Look to the righthand side of wget as it runs and you’ll see the connection speed (1.36m/s in the screen shot example). Because wget is pointing the downloaded file at /dev/null it won’t actually take up any hard drive space, so there is no concern about running this command repeatedly.

This uses the same SpeedTest servers that are available to mobile users through the Speed Test app, it can make for a decent way to directly compare connection speeds on a broadband connection vs cellular, without having to access the SpeedTest Flash-based web apps, and without having to compile any additional command line software.

Plan on using this trick often? Consider adding a simple alias to .bash_profile:

Using an alias is obviously shorter and easier to remember, making it a bit more useful for scripts, automation, remote testing, and just for those of us who like to poke around in the Terminal.

This trick comes to us from @climagic on Twitter, be sure to follow @osxdaily there too if you haven’t done so yet.

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