Why we try to capture variability? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)What is a good way to measure how well a set of data fits to a set of functionsScore Variance of supplementary individuals in PCAWhich variables should I transform, center, and/or standardize in my data for Principal Component Analysis?Aside from regression coefficients, what are commonly used approaches to measure one variable's “sensitivity” to another variable?Where is the indeterminacy of factor values on this plot explaining factor analysis?Geometric understanding of PCA in the subject (dual) spaceWhat is the fastest way to compute PC1 scores, without performing the whole PCA?Do the variables having high partial correlation also contributes to a high proportion of variance explained by the computed principal components?PCA: how to select eigen vectors corresponding to small eigenvalues for regressionwhat to say about low variability
How often does castling occur in grandmaster games?
How fail-safe is nr as stop bytes?
In musical terms, what properties are varied by the human voice to produce different words / syllables?
Random body shuffle every night—can we still function?
What is the difference between globalisation and imperialism?
What are the discoveries that have been possible with the rejection of positivism?
How to compare two different files line by line in unix?
How many time has Arya actually used Needle?
Should a wizard buy fine inks every time he want to copy spells into his spellbook?
Is there hard evidence that the grant peer review system performs significantly better than random?
Did any compiler fully use 80-bit floating point?
What order were files/directories output in dir?
Why can't I install Tomboy in Ubuntu Mate 19.04?
Is it fair for a professor to grade us on the possession of past papers?
How did Fremen produce and carry enough thumpers to use Sandworms as de facto Ubers?
What is Adi Shankara referring to when he says "He has Vajra marks on his feet"?
What to do with repeated rejections for phd position
How were pictures turned from film to a big picture in a picture frame before digital scanning?
Unit testing extension method adding view location expander
Is CEO the "profession" with the most psychopaths?
Co-worker has annoying ringtone
One-one communication
Lagrange four-squares theorem --- deterministic complexity
Do I really need to have a message in a novel to appeal to readers?
Why we try to capture variability?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)What is a good way to measure how well a set of data fits to a set of functionsScore Variance of supplementary individuals in PCAWhich variables should I transform, center, and/or standardize in my data for Principal Component Analysis?Aside from regression coefficients, what are commonly used approaches to measure one variable's “sensitivity” to another variable?Where is the indeterminacy of factor values on this plot explaining factor analysis?Geometric understanding of PCA in the subject (dual) spaceWhat is the fastest way to compute PC1 scores, without performing the whole PCA?Do the variables having high partial correlation also contributes to a high proportion of variance explained by the computed principal components?PCA: how to select eigen vectors corresponding to small eigenvalues for regressionwhat to say about low variability
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
I am new to Statistics and I have a Mathematics background. In Statistics, particularly in Linear Regression and Principal Component Analysis (PCA) so far what I have understood is that the main idea is to try to capture as much as possible variability present in the data. In linear regression, while calculating $ R^2 (R squared)$ measure we are checking the proportion of variability captured by our model and in PCA we are forming a new basis along which our data has the maximum possible variability. Is there any significant result behind this logic? I mean why we have to go after variability? Any help in this matter will be appreciated.
regression pca variability
edited 19 mins ago
Karolis Koncevičius
2,39341630
asked 3 hours ago
SatishSatish
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am new to Statistics and I have a Mathematics background. In Statistics, particularly in Linear Regression and Principal Component Analysis (PCA) so far what I have understood is that the main idea is to try to capture as much as possible variability present in the data. In linear regression, while calculating $ R^2 (R squared)$ measure we are checking the proportion of variability captured by our model and in PCA we are forming a new basis along which our data has the maximum possible variability. Is there any significant result behind this logic? I mean why we have to go after variability? Any help in this matter will be appreciated.
regression pca variability
edited 19 mins ago
Karolis Koncevičius
2,39341630
asked 3 hours ago
SatishSatish
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am new to Statistics and I have a Mathematics background. In Statistics, particularly in Linear Regression and Principal Component Analysis (PCA) so far what I have understood is that the main idea is to try to capture as much as possible variability present in the data. In linear regression, while calculating $ R^2 (R squared)$ measure we are checking the proportion of variability captured by our model and in PCA we are forming a new basis along which our data has the maximum possible variability. Is there any significant result behind this logic? I mean why we have to go after variability? Any help in this matter will be appreciated.
regression pca variability
edited 19 mins ago
Karolis Koncevičius
2,39341630
asked 3 hours ago
SatishSatish
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am new to Statistics and I have a Mathematics background. In Statistics, particularly in Linear Regression and Principal Component Analysis (PCA) so far what I have understood is that the main idea is to try to capture as much as possible variability present in the data. In linear regression, while calculating $ R^2 (R squared)$ measure we are checking the proportion of variability captured by our model and in PCA we are forming a new basis along which our data has the maximum possible variability. Is there any significant result behind this logic? I mean why we have to go after variability? Any help in this matter will be appreciated.
regression pca variability
regression pca variability
edited 19 mins ago
Karolis Koncevičius
2,39341630
asked 3 hours ago
SatishSatish
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 19 mins ago
Karolis Koncevičius
2,39341630
asked 3 hours ago
SatishSatish
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 19 mins ago
Karolis Koncevičius
2,39341630
edited 19 mins ago
Karolis Koncevičius
2,39341630
edited 19 mins ago
Karolis Koncevičius
2,39341630
2,39341630
asked 3 hours ago
SatishSatish
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
SatishSatish
112
asked 3 hours ago
SatishSatish
112
112
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Satish is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
In many cases the reason we use regression is to explain variability. In that sense, how much variability is explained is one of the key measures of success.
This may be more clear with an example. I recently worked on a project where we created a regression model to explain employee performance. We did this because our stakeholders (senior management) wanted to know why some employees were performing well and others weren't. That is, why do we see variance in employee performance?
Phrased this way it should be clear that a key performance metric for our model is how much variability it anticipates.
answered 3 hours ago
indigochildindigochild
1535
$endgroup$
add a comment |
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "65"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Satish is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f404014%2fwhy-we-try-to-capture-variability%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
In many cases the reason we use regression is to explain variability. In that sense, how much variability is explained is one of the key measures of success.
This may be more clear with an example. I recently worked on a project where we created a regression model to explain employee performance. We did this because our stakeholders (senior management) wanted to know why some employees were performing well and others weren't. That is, why do we see variance in employee performance?
Phrased this way it should be clear that a key performance metric for our model is how much variability it anticipates.
answered 3 hours ago
indigochildindigochild
1535
$endgroup$
add a comment |
$begingroup$
In many cases the reason we use regression is to explain variability. In that sense, how much variability is explained is one of the key measures of success.
This may be more clear with an example. I recently worked on a project where we created a regression model to explain employee performance. We did this because our stakeholders (senior management) wanted to know why some employees were performing well and others weren't. That is, why do we see variance in employee performance?
Phrased this way it should be clear that a key performance metric for our model is how much variability it anticipates.
answered 3 hours ago
indigochildindigochild
1535
$endgroup$
add a comment |
$begingroup$
In many cases the reason we use regression is to explain variability. In that sense, how much variability is explained is one of the key measures of success.
This may be more clear with an example. I recently worked on a project where we created a regression model to explain employee performance. We did this because our stakeholders (senior management) wanted to know why some employees were performing well and others weren't. That is, why do we see variance in employee performance?
Phrased this way it should be clear that a key performance metric for our model is how much variability it anticipates.
answered 3 hours ago
indigochildindigochild
1535
$endgroup$
In many cases the reason we use regression is to explain variability. In that sense, how much variability is explained is one of the key measures of success.
This may be more clear with an example. I recently worked on a project where we created a regression model to explain employee performance. We did this because our stakeholders (senior management) wanted to know why some employees were performing well and others weren't. That is, why do we see variance in employee performance?
Phrased this way it should be clear that a key performance metric for our model is how much variability it anticipates.
answered 3 hours ago
indigochildindigochild
1535
answered 3 hours ago
indigochildindigochild
1535
answered 3 hours ago
indigochildindigochild
1535
answered 3 hours ago
indigochildindigochild
1535
1535
add a comment |
add a comment |
Satish is a new contributor. Be nice, and check out our Code of Conduct.
Satish is a new contributor. Be nice, and check out our Code of Conduct.
Satish is a new contributor. Be nice, and check out our Code of Conduct.
Satish is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Cross Validated!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f404014%2fwhy-we-try-to-capture-variability%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
