What are the disadvantages of having a left skewed distribution?2019 Community Moderator ElectionHow to deal with a skewed data-set having all the samples almost similar?What are the “extra nodes” in XGboost?What are the disadvantages of Azure's ML vs a pure code approach (R/SKlearn)What are the tools to plot cluster results?What is the best way to normalize histogram vectors to get distribution?What are the benefits of having ML in js?What data treatment/transformation should be applied if there are a lot of outliers and features lack normal distribution?What are the best practices for data formatting?What are the assumptions of linear regressionHistogram is extremely skewed to the left
Maximum likelihood parameters deviate from posterior distributions
Do I have a twin with permutated remainders?
How to format long polynomial?
Has there ever been an airliner design involving reducing generator load by installing solar panels?
meaning of に in 本当に?
Is it legal for company to use my work email to pretend I still work there?
Which country benefited the most from UN Security Council vetoes?
Horror movie about a virus at the prom; beginning and end are stylized as a cartoon
Watching something be written to a file live with tail
Does object always see its latest internal state irrespective of thread?
Today is the Center
How is the claim "I am in New York only if I am in America" the same as "If I am in New York, then I am in America?
Unable to deploy metadata from Partner Developer scratch org because of extra fields
What defenses are there against being summoned by the Gate spell?
Why does Kotter return in Welcome Back Kotter?
Why is 150k or 200k jobs considered good when there's 300k+ births a month?
Convert two switches to a dual stack, and add outlet - possible here?
A case of the sniffles
What's the point of deactivating Num Lock on login screens?
A newer friend of my brother's gave him a load of baseball cards that are supposedly extremely valuable. Is this a scam?
Important Resources for Dark Age Civilizations?
Revoked SSL certificate
Replacing matching entries in one column of a file by another column from a different file
What is a clear way to write a bar that has an extra beat?
What are the disadvantages of having a left skewed distribution?
2019 Community Moderator ElectionHow to deal with a skewed data-set having all the samples almost similar?What are the “extra nodes” in XGboost?What are the disadvantages of Azure's ML vs a pure code approach (R/SKlearn)What are the tools to plot cluster results?What is the best way to normalize histogram vectors to get distribution?What are the benefits of having ML in js?What data treatment/transformation should be applied if there are a lot of outliers and features lack normal distribution?What are the best practices for data formatting?What are the assumptions of linear regressionHistogram is extremely skewed to the left
$begingroup$
I'm currently working on a classification problem and I've a numerical column which is left skewed. i've read many posts where people are recommending to take log transformation or boxcox transformation to fix the left skewness.
So I was wondering what would happen If I left the skewness as it is and continue with my model building? Are there any advantages of fixing skewness for classification problem (knn, logistic regression)?
machine-learning python
$endgroup$
add a comment |
$begingroup$
I'm currently working on a classification problem and I've a numerical column which is left skewed. i've read many posts where people are recommending to take log transformation or boxcox transformation to fix the left skewness.
So I was wondering what would happen If I left the skewness as it is and continue with my model building? Are there any advantages of fixing skewness for classification problem (knn, logistic regression)?
machine-learning python
$endgroup$
add a comment |
$begingroup$
I'm currently working on a classification problem and I've a numerical column which is left skewed. i've read many posts where people are recommending to take log transformation or boxcox transformation to fix the left skewness.
So I was wondering what would happen If I left the skewness as it is and continue with my model building? Are there any advantages of fixing skewness for classification problem (knn, logistic regression)?
machine-learning python
$endgroup$
I'm currently working on a classification problem and I've a numerical column which is left skewed. i've read many posts where people are recommending to take log transformation or boxcox transformation to fix the left skewness.
So I was wondering what would happen If I left the skewness as it is and continue with my model building? Are there any advantages of fixing skewness for classification problem (knn, logistic regression)?
machine-learning python
machine-learning python
asked 3 hours ago
user214user214
20417
20417
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
There are issues that will depend on specific features of your data and analytic approach, but in general skewed data (in either direction) will degrade some of your model's ability to describe more "typical" cases in order to deal with much rarer cases which happen to take extreme values.
Since "typical" cases are more common than extreme ones in a skewed data set, you are losing some precision with the cases you'll see most often in order to accommodate cases that you'll see only rarely. Determining a coefficient for a thousand observations which are all between [0,10] is likely to be more precise than for 990 observations between [0,10] and 10 observations between [1,000, 1,000,000]. This can lead to your model being less useful overall.
"Fixing" skewness can provide a variety of benefits, including making analysis which depends on the data being approximately Normally distributed possible/more informative. It can also produce results which are reported on a sensible scale (this is very situation-dependent), and prevent extreme values (relative to other predictors) from over- or underestimating the influence of the skewed predictor on the predicted classification.
You can test this somewhat (in a non-definitive way, to be sure) by training models with varying subsets of your data: everything you've got, just as it is, your data without that skewed variable, your data with that variable but excluding values outside of the "typical" range (though you'll have to be careful in defining that), your data with the skewed variable distribution transformed or re-scaled, etc.
As for fixing it, transformations and re-scaling often make sense. But I cannot emphasize enough:
Fiddling with variables and their distributions should follow from properties of those variables, not your convenience in modelling.
Log-transforming skewed variables is a prime example of this:
- If you really think that a variable operates on a geometric scale,
and you want your model to operate on an arithmetic scale, then log
transformation can make a lot of sense. - If you think that variable operates on an arithmetic scale, but you
find its distribution inconvenient and think a log transformation
would produce a more convenient distribution, it may make sense to
transform. It will change how the model is used and interpreted,
usually making it more dense and harder to interpret clearly, but
that may or may not be worthwhile. For example, if you take the log of a numeric outcome and the log of a numeric predictor, the result has to be interpreted as an elasticity between them, which can be awkward to work with and is often not what is desired. - If you think that a log transformation would be desirable for a
variable, but it has a lot of observations with a value of 0, then
log transformation isn't really an option for you, whether it would
be convenient or not. (Adding a "small value" to the 0 observations
causes lots of problems-- take the logs of 1-10, and then 0.0 to
1.0).
$endgroup$
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
return StackExchange.using("mathjaxEditing", function ()
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
);
);
, "mathjax-editing");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
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
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
var $window = $(window),
onScroll = function(e)
var $elem = $('.new-login-left'),
docViewTop = $window.scrollTop(),
docViewBottom = docViewTop + $window.height(),
elemTop = $elem.offset().top,
elemBottom = elemTop + $elem.height();
if ((docViewTop elemBottom))
StackExchange.using('gps', function() StackExchange.gps.track('embedded_signup_form.view', location: 'question_page' ); );
$window.unbind('scroll', onScroll);
;
$window.on('scroll', onScroll);
);
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%2fdatascience.stackexchange.com%2fquestions%2f48711%2fwhat-are-the-disadvantages-of-having-a-left-skewed-distribution%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
$begingroup$
There are issues that will depend on specific features of your data and analytic approach, but in general skewed data (in either direction) will degrade some of your model's ability to describe more "typical" cases in order to deal with much rarer cases which happen to take extreme values.
Since "typical" cases are more common than extreme ones in a skewed data set, you are losing some precision with the cases you'll see most often in order to accommodate cases that you'll see only rarely. Determining a coefficient for a thousand observations which are all between [0,10] is likely to be more precise than for 990 observations between [0,10] and 10 observations between [1,000, 1,000,000]. This can lead to your model being less useful overall.
"Fixing" skewness can provide a variety of benefits, including making analysis which depends on the data being approximately Normally distributed possible/more informative. It can also produce results which are reported on a sensible scale (this is very situation-dependent), and prevent extreme values (relative to other predictors) from over- or underestimating the influence of the skewed predictor on the predicted classification.
You can test this somewhat (in a non-definitive way, to be sure) by training models with varying subsets of your data: everything you've got, just as it is, your data without that skewed variable, your data with that variable but excluding values outside of the "typical" range (though you'll have to be careful in defining that), your data with the skewed variable distribution transformed or re-scaled, etc.
As for fixing it, transformations and re-scaling often make sense. But I cannot emphasize enough:
Fiddling with variables and their distributions should follow from properties of those variables, not your convenience in modelling.
Log-transforming skewed variables is a prime example of this:
- If you really think that a variable operates on a geometric scale,
and you want your model to operate on an arithmetic scale, then log
transformation can make a lot of sense. - If you think that variable operates on an arithmetic scale, but you
find its distribution inconvenient and think a log transformation
would produce a more convenient distribution, it may make sense to
transform. It will change how the model is used and interpreted,
usually making it more dense and harder to interpret clearly, but
that may or may not be worthwhile. For example, if you take the log of a numeric outcome and the log of a numeric predictor, the result has to be interpreted as an elasticity between them, which can be awkward to work with and is often not what is desired. - If you think that a log transformation would be desirable for a
variable, but it has a lot of observations with a value of 0, then
log transformation isn't really an option for you, whether it would
be convenient or not. (Adding a "small value" to the 0 observations
causes lots of problems-- take the logs of 1-10, and then 0.0 to
1.0).
$endgroup$
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
add a comment |
$begingroup$
There are issues that will depend on specific features of your data and analytic approach, but in general skewed data (in either direction) will degrade some of your model's ability to describe more "typical" cases in order to deal with much rarer cases which happen to take extreme values.
Since "typical" cases are more common than extreme ones in a skewed data set, you are losing some precision with the cases you'll see most often in order to accommodate cases that you'll see only rarely. Determining a coefficient for a thousand observations which are all between [0,10] is likely to be more precise than for 990 observations between [0,10] and 10 observations between [1,000, 1,000,000]. This can lead to your model being less useful overall.
"Fixing" skewness can provide a variety of benefits, including making analysis which depends on the data being approximately Normally distributed possible/more informative. It can also produce results which are reported on a sensible scale (this is very situation-dependent), and prevent extreme values (relative to other predictors) from over- or underestimating the influence of the skewed predictor on the predicted classification.
You can test this somewhat (in a non-definitive way, to be sure) by training models with varying subsets of your data: everything you've got, just as it is, your data without that skewed variable, your data with that variable but excluding values outside of the "typical" range (though you'll have to be careful in defining that), your data with the skewed variable distribution transformed or re-scaled, etc.
As for fixing it, transformations and re-scaling often make sense. But I cannot emphasize enough:
Fiddling with variables and their distributions should follow from properties of those variables, not your convenience in modelling.
Log-transforming skewed variables is a prime example of this:
- If you really think that a variable operates on a geometric scale,
and you want your model to operate on an arithmetic scale, then log
transformation can make a lot of sense. - If you think that variable operates on an arithmetic scale, but you
find its distribution inconvenient and think a log transformation
would produce a more convenient distribution, it may make sense to
transform. It will change how the model is used and interpreted,
usually making it more dense and harder to interpret clearly, but
that may or may not be worthwhile. For example, if you take the log of a numeric outcome and the log of a numeric predictor, the result has to be interpreted as an elasticity between them, which can be awkward to work with and is often not what is desired. - If you think that a log transformation would be desirable for a
variable, but it has a lot of observations with a value of 0, then
log transformation isn't really an option for you, whether it would
be convenient or not. (Adding a "small value" to the 0 observations
causes lots of problems-- take the logs of 1-10, and then 0.0 to
1.0).
$endgroup$
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
add a comment |
$begingroup$
There are issues that will depend on specific features of your data and analytic approach, but in general skewed data (in either direction) will degrade some of your model's ability to describe more "typical" cases in order to deal with much rarer cases which happen to take extreme values.
Since "typical" cases are more common than extreme ones in a skewed data set, you are losing some precision with the cases you'll see most often in order to accommodate cases that you'll see only rarely. Determining a coefficient for a thousand observations which are all between [0,10] is likely to be more precise than for 990 observations between [0,10] and 10 observations between [1,000, 1,000,000]. This can lead to your model being less useful overall.
"Fixing" skewness can provide a variety of benefits, including making analysis which depends on the data being approximately Normally distributed possible/more informative. It can also produce results which are reported on a sensible scale (this is very situation-dependent), and prevent extreme values (relative to other predictors) from over- or underestimating the influence of the skewed predictor on the predicted classification.
You can test this somewhat (in a non-definitive way, to be sure) by training models with varying subsets of your data: everything you've got, just as it is, your data without that skewed variable, your data with that variable but excluding values outside of the "typical" range (though you'll have to be careful in defining that), your data with the skewed variable distribution transformed or re-scaled, etc.
As for fixing it, transformations and re-scaling often make sense. But I cannot emphasize enough:
Fiddling with variables and their distributions should follow from properties of those variables, not your convenience in modelling.
Log-transforming skewed variables is a prime example of this:
- If you really think that a variable operates on a geometric scale,
and you want your model to operate on an arithmetic scale, then log
transformation can make a lot of sense. - If you think that variable operates on an arithmetic scale, but you
find its distribution inconvenient and think a log transformation
would produce a more convenient distribution, it may make sense to
transform. It will change how the model is used and interpreted,
usually making it more dense and harder to interpret clearly, but
that may or may not be worthwhile. For example, if you take the log of a numeric outcome and the log of a numeric predictor, the result has to be interpreted as an elasticity between them, which can be awkward to work with and is often not what is desired. - If you think that a log transformation would be desirable for a
variable, but it has a lot of observations with a value of 0, then
log transformation isn't really an option for you, whether it would
be convenient or not. (Adding a "small value" to the 0 observations
causes lots of problems-- take the logs of 1-10, and then 0.0 to
1.0).
$endgroup$
There are issues that will depend on specific features of your data and analytic approach, but in general skewed data (in either direction) will degrade some of your model's ability to describe more "typical" cases in order to deal with much rarer cases which happen to take extreme values.
Since "typical" cases are more common than extreme ones in a skewed data set, you are losing some precision with the cases you'll see most often in order to accommodate cases that you'll see only rarely. Determining a coefficient for a thousand observations which are all between [0,10] is likely to be more precise than for 990 observations between [0,10] and 10 observations between [1,000, 1,000,000]. This can lead to your model being less useful overall.
"Fixing" skewness can provide a variety of benefits, including making analysis which depends on the data being approximately Normally distributed possible/more informative. It can also produce results which are reported on a sensible scale (this is very situation-dependent), and prevent extreme values (relative to other predictors) from over- or underestimating the influence of the skewed predictor on the predicted classification.
You can test this somewhat (in a non-definitive way, to be sure) by training models with varying subsets of your data: everything you've got, just as it is, your data without that skewed variable, your data with that variable but excluding values outside of the "typical" range (though you'll have to be careful in defining that), your data with the skewed variable distribution transformed or re-scaled, etc.
As for fixing it, transformations and re-scaling often make sense. But I cannot emphasize enough:
Fiddling with variables and their distributions should follow from properties of those variables, not your convenience in modelling.
Log-transforming skewed variables is a prime example of this:
- If you really think that a variable operates on a geometric scale,
and you want your model to operate on an arithmetic scale, then log
transformation can make a lot of sense. - If you think that variable operates on an arithmetic scale, but you
find its distribution inconvenient and think a log transformation
would produce a more convenient distribution, it may make sense to
transform. It will change how the model is used and interpreted,
usually making it more dense and harder to interpret clearly, but
that may or may not be worthwhile. For example, if you take the log of a numeric outcome and the log of a numeric predictor, the result has to be interpreted as an elasticity between them, which can be awkward to work with and is often not what is desired. - If you think that a log transformation would be desirable for a
variable, but it has a lot of observations with a value of 0, then
log transformation isn't really an option for you, whether it would
be convenient or not. (Adding a "small value" to the 0 observations
causes lots of problems-- take the logs of 1-10, and then 0.0 to
1.0).
answered 2 hours ago
Upper_CaseUpper_Case
1312
1312
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
add a comment |
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
Assume I've numeric column such as price and it's heavily left skewed. I'm thinking of using few basic classification algorithms. What should be my approach? Should I go for log transformation or boxcox transformation?
$endgroup$
– user214
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
$begingroup$
@user214 Left-skewed price information? That sounds interesting! (My research data is generally skewed hard to the right). There is always variation between study contexts, but I generally think of money as "geometric enough" that a log transformation is appropriate (or at least strongly defensible). Whether or not that's the ideal transformation is a very difficult question to answer, but log transformation is unlikely to be a problem for you here. You'll just need to remember that anything about that predictor will be reported on a log scale, and interpret accordingly.
$endgroup$
– Upper_Case
2 hours ago
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- 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');
var $window = $(window),
onScroll = function(e)
var $elem = $('.new-login-left'),
docViewTop = $window.scrollTop(),
docViewBottom = docViewTop + $window.height(),
elemTop = $elem.offset().top,
elemBottom = elemTop + $elem.height();
if ((docViewTop elemBottom))
StackExchange.using('gps', function() StackExchange.gps.track('embedded_signup_form.view', location: 'question_page' ); );
$window.unbind('scroll', onScroll);
;
$window.on('scroll', onScroll);
);
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%2fdatascience.stackexchange.com%2fquestions%2f48711%2fwhat-are-the-disadvantages-of-having-a-left-skewed-distribution%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');
var $window = $(window),
onScroll = function(e)
var $elem = $('.new-login-left'),
docViewTop = $window.scrollTop(),
docViewBottom = docViewTop + $window.height(),
elemTop = $elem.offset().top,
elemBottom = elemTop + $elem.height();
if ((docViewTop elemBottom))
StackExchange.using('gps', function() StackExchange.gps.track('embedded_signup_form.view', location: 'question_page' ); );
$window.unbind('scroll', onScroll);
;
$window.on('scroll', onScroll);
);
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');
var $window = $(window),
onScroll = function(e)
var $elem = $('.new-login-left'),
docViewTop = $window.scrollTop(),
docViewBottom = docViewTop + $window.height(),
elemTop = $elem.offset().top,
elemBottom = elemTop + $elem.height();
if ((docViewTop elemBottom))
StackExchange.using('gps', function() StackExchange.gps.track('embedded_signup_form.view', location: 'question_page' ); );
$window.unbind('scroll', onScroll);
;
$window.on('scroll', onScroll);
);
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');
var $window = $(window),
onScroll = function(e)
var $elem = $('.new-login-left'),
docViewTop = $window.scrollTop(),
docViewBottom = docViewTop + $window.height(),
elemTop = $elem.offset().top,
elemBottom = elemTop + $elem.height();
if ((docViewTop elemBottom))
StackExchange.using('gps', function() StackExchange.gps.track('embedded_signup_form.view', location: 'question_page' ); );
$window.unbind('scroll', onScroll);
;
$window.on('scroll', onScroll);
);
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