Why we try to capture variability? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30UTC (7:30pm US/Eastern)What is a good way to measure how well a set of data fits to a set of functionsHow to transform factor scores of a PCA for a regression, in SPSS?Score Variance of supplementary individuals in PCAWhich variables should I transform, center, and/or standardize in my data for Principal Component Analysis?Where is the indeterminacy of factor values on this plot explaining factor analysis?Geometric understanding of PCA in the subject (dual) spacePairwise distance as measure of variabilityWhat 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 regression
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Why we try to capture variability?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30UTC (7:30pm US/Eastern)What is a good way to measure how well a set of data fits to a set of functionsHow to transform factor scores of a PCA for a regression, in SPSS?Score Variance of supplementary individuals in PCAWhich variables should I transform, center, and/or standardize in my data for Principal Component Analysis?Where is the indeterminacy of factor values on this plot explaining factor analysis?Geometric understanding of PCA in the subject (dual) spacePairwise distance as measure of variabilityWhat 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 regression
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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 1 hour ago
Karolis Koncevičius
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asked 5 hours ago
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 1 hour ago
Karolis Koncevičius
2,40341630
asked 5 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 1 hour ago
Karolis Koncevičius
2,40341630
asked 5 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 1 hour ago
Karolis Koncevičius
2,40341630
asked 5 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 1 hour ago
Karolis Koncevičius
2,40341630
asked 5 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 1 hour ago
Karolis Koncevičius
2,40341630
edited 1 hour ago
Karolis Koncevičius
2,40341630
edited 1 hour ago
Karolis Koncevičius
2,40341630
2,40341630
asked 5 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 5 hours ago
SatishSatish
112
asked 5 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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Check out our Code of Conduct.
add a comment |
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1 Answer
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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 4 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 4 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 4 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 4 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 4 hours ago
indigochildindigochild
1535
answered 4 hours ago
indigochildindigochild
1535
answered 4 hours ago
indigochildindigochild
1535
answered 4 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.
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