Why we try to capture variability?
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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 18 mins ago
Karolis Koncevičius
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SatishSatish
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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 18 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.
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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 18 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 18 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 18 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.
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edited 18 mins ago
Karolis Koncevičius
2,39341630
edited 18 mins ago
Karolis Koncevičius
2,39341630
edited 18 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.
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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
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$begingroup$
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 |
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