Is a model fitted to data or is data fitted to a model? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)Is it OK to remove outliers from data?Using collaborative filtering to “clean” data and the other way aroundDivergence interpretationWhat is the distinction between “relationships” and “differences” (particularly in the context of group differences on a numeric attribute)?What exactly is called “principal component” in PCA?How to properly state the average of a discrete number in a report?What are Bayes Factors and how can they be used to demonstrate a null finding of “no difference” between two groups?What is the difference between Statistics and Data Science?What is the difference between manifold learning and non-linear dimensionality reduction?Statistics? Data Science?Difference between Multivariate Time Series data and Panel Data
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Is a model fitted to data or is data fitted to a model?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)Is it OK to remove outliers from data?Using collaborative filtering to “clean” data and the other way aroundDivergence interpretationWhat is the distinction between “relationships” and “differences” (particularly in the context of group differences on a numeric attribute)?What exactly is called “principal component” in PCA?How to properly state the average of a discrete number in a report?What are Bayes Factors and how can they be used to demonstrate a null finding of “no difference” between two groups?What is the difference between Statistics and Data Science?What is the difference between manifold learning and non-linear dimensionality reduction?Statistics? Data Science?Difference between Multivariate Time Series data and Panel Data
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$begingroup$
Is there a conceptual or procedural difference between fitting a model to data and fitting data to model? An example of the first wording can be seen in https://courses.washington.edu/matlab1/ModelFitting.html, and of the second in https://reference.wolfram.com/applications/eda/FittingDataToLinearModelsByLeast-SquaresTechniques.html.
terminology
edited Mar 24 at 8:18
Nick Cox
39.4k588132
asked Mar 24 at 2:20
enjayesenjayes
1187
$endgroup$
add a comment |
$begingroup$
Is there a conceptual or procedural difference between fitting a model to data and fitting data to model? An example of the first wording can be seen in https://courses.washington.edu/matlab1/ModelFitting.html, and of the second in https://reference.wolfram.com/applications/eda/FittingDataToLinearModelsByLeast-SquaresTechniques.html.
terminology
edited Mar 24 at 8:18
Nick Cox
39.4k588132
asked Mar 24 at 2:20
enjayesenjayes
1187
$endgroup$
7
$begingroup$
+1 I am not impressed by the second link, but I am entertained.
$endgroup$
– The Laconic
Mar 24 at 2:38
$begingroup$
Many models fits current data, but data typically fits best one model
$endgroup$
– Agnius Vasiliauskas
Mar 25 at 9:45
add a comment |
$begingroup$
Is there a conceptual or procedural difference between fitting a model to data and fitting data to model? An example of the first wording can be seen in https://courses.washington.edu/matlab1/ModelFitting.html, and of the second in https://reference.wolfram.com/applications/eda/FittingDataToLinearModelsByLeast-SquaresTechniques.html.
terminology
edited Mar 24 at 8:18
Nick Cox
39.4k588132
asked Mar 24 at 2:20
enjayesenjayes
1187
$endgroup$
Is there a conceptual or procedural difference between fitting a model to data and fitting data to model? An example of the first wording can be seen in https://courses.washington.edu/matlab1/ModelFitting.html, and of the second in https://reference.wolfram.com/applications/eda/FittingDataToLinearModelsByLeast-SquaresTechniques.html.
terminology
terminology
edited Mar 24 at 8:18
Nick Cox
39.4k588132
asked Mar 24 at 2:20
enjayesenjayes
1187
edited Mar 24 at 8:18
Nick Cox
39.4k588132
asked Mar 24 at 2:20
enjayesenjayes
1187
edited Mar 24 at 8:18
Nick Cox
39.4k588132
edited Mar 24 at 8:18
Nick Cox
39.4k588132
edited Mar 24 at 8:18
Nick Cox
39.4k588132
39.4k588132
asked Mar 24 at 2:20
enjayesenjayes
1187
asked Mar 24 at 2:20
enjayesenjayes
1187
asked Mar 24 at 2:20
enjayesenjayes
1187
1187
7
$begingroup$
+1 I am not impressed by the second link, but I am entertained.
$endgroup$
– The Laconic
Mar 24 at 2:38
$begingroup$
Many models fits current data, but data typically fits best one model
$endgroup$
– Agnius Vasiliauskas
Mar 25 at 9:45
add a comment |
7
$begingroup$
+1 I am not impressed by the second link, but I am entertained.
$endgroup$
– The Laconic
Mar 24 at 2:38
$begingroup$
Many models fits current data, but data typically fits best one model
$endgroup$
– Agnius Vasiliauskas
Mar 25 at 9:45
7
7
$begingroup$
+1 I am not impressed by the second link, but I am entertained.
$endgroup$
– The Laconic
Mar 24 at 2:38
$begingroup$
+1 I am not impressed by the second link, but I am entertained.
$endgroup$
– The Laconic
Mar 24 at 2:38
$begingroup$
Many models fits current data, but data typically fits best one model
$endgroup$
– Agnius Vasiliauskas
Mar 25 at 9:45
$begingroup$
Many models fits current data, but data typically fits best one model
$endgroup$
– Agnius Vasiliauskas
Mar 25 at 9:45
add a comment |
4 Answers
4
active
oldest
votes
$begingroup$
Pretty much every source or person I've ever interacted with except the Wolfram source you linked refers to the process as fitting a model to data. This makes sense, since the model is the dynamic object and the data is static (a.k.a. fixed and constant).
To put a point on it, I like Larry Wasserman's approach to this. In his telling, a statistical model is a collection of distributions. For example, the collection of all normal distributions:
$$ textNormal(mu, sigma) : mu, sigma in R, sigma > 0 $$
or the set of all Poisson distributions:
$$ textPoisson(lambda) : lambda in R, lambda > 0 $$
Fitting a distribution to data is any algorithm that combines a statistical model with a set of data (the data is fixed), and chooses exactly one of the distributions from the model as the one that "best" reflects the data.
The model is the thing that changes (sort of): we are collapsing it from an entire collection of possibilities into a single best choice. The data is just the data; nothing happens to it at all.
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
$endgroup$
add a comment |
$begingroup$
In the field of Rasch modelling it is common to fit the data to the model. The model is assumed to be correct and it is the analyst's job to find data which conform to it. The Wikipedia article on Rasch contains more details about the how and the why.
But I agree with others that in general in statistics we fit the model to the data because we can change the model but it is felt to be bad form to select or modify the data.
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
$endgroup$
add a comment |
$begingroup$
Typically, the observed data are fixed while the model is mutable (e.g. because parameters are estimated), so it is the model that is made to fit the data, not the other way around. (Usually people mean this case when they say either expression.)
When people say they fit data to a model I find myself trying to figure out what the heck did they do to the data?.
[Now if you're transforming data, that would arguably be 'fitting data to a model', but people almost never say that for this case.]
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
$endgroup$
5
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
1
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
1
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
$endgroup$
– BartoszKP
Mar 24 at 20:16
1
$begingroup$
+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
$endgroup$
– Christoph Hanck
Mar 25 at 15:37
add a comment |
$begingroup$
Usually, we assume our data corresponds to the "real world" and making any modifications means we are moving away from modelling the "real world". For example, one needs to take care removing outliers since even if it makes computation nicer, outliers were still part of our data.
When testing a model or estimating properties of an estimator using bootstrap or other resampling techniques, we may simulate new data using an estimated model and our original data. This makes the assumption that the model is correct, and we are not modifying our original data.
answered Mar 24 at 21:34
qwrqwr
249113
$endgroup$
add a comment |
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4 Answers
4
active
oldest
votes
4 Answers
4
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Pretty much every source or person I've ever interacted with except the Wolfram source you linked refers to the process as fitting a model to data. This makes sense, since the model is the dynamic object and the data is static (a.k.a. fixed and constant).
To put a point on it, I like Larry Wasserman's approach to this. In his telling, a statistical model is a collection of distributions. For example, the collection of all normal distributions:
$$ textNormal(mu, sigma) : mu, sigma in R, sigma > 0 $$
or the set of all Poisson distributions:
$$ textPoisson(lambda) : lambda in R, lambda > 0 $$
Fitting a distribution to data is any algorithm that combines a statistical model with a set of data (the data is fixed), and chooses exactly one of the distributions from the model as the one that "best" reflects the data.
The model is the thing that changes (sort of): we are collapsing it from an entire collection of possibilities into a single best choice. The data is just the data; nothing happens to it at all.
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
$endgroup$
add a comment |
$begingroup$
Pretty much every source or person I've ever interacted with except the Wolfram source you linked refers to the process as fitting a model to data. This makes sense, since the model is the dynamic object and the data is static (a.k.a. fixed and constant).
To put a point on it, I like Larry Wasserman's approach to this. In his telling, a statistical model is a collection of distributions. For example, the collection of all normal distributions:
$$ textNormal(mu, sigma) : mu, sigma in R, sigma > 0 $$
or the set of all Poisson distributions:
$$ textPoisson(lambda) : lambda in R, lambda > 0 $$
Fitting a distribution to data is any algorithm that combines a statistical model with a set of data (the data is fixed), and chooses exactly one of the distributions from the model as the one that "best" reflects the data.
The model is the thing that changes (sort of): we are collapsing it from an entire collection of possibilities into a single best choice. The data is just the data; nothing happens to it at all.
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
$endgroup$
add a comment |
$begingroup$
Pretty much every source or person I've ever interacted with except the Wolfram source you linked refers to the process as fitting a model to data. This makes sense, since the model is the dynamic object and the data is static (a.k.a. fixed and constant).
To put a point on it, I like Larry Wasserman's approach to this. In his telling, a statistical model is a collection of distributions. For example, the collection of all normal distributions:
$$ textNormal(mu, sigma) : mu, sigma in R, sigma > 0 $$
or the set of all Poisson distributions:
$$ textPoisson(lambda) : lambda in R, lambda > 0 $$
Fitting a distribution to data is any algorithm that combines a statistical model with a set of data (the data is fixed), and chooses exactly one of the distributions from the model as the one that "best" reflects the data.
The model is the thing that changes (sort of): we are collapsing it from an entire collection of possibilities into a single best choice. The data is just the data; nothing happens to it at all.
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
$endgroup$
Pretty much every source or person I've ever interacted with except the Wolfram source you linked refers to the process as fitting a model to data. This makes sense, since the model is the dynamic object and the data is static (a.k.a. fixed and constant).
To put a point on it, I like Larry Wasserman's approach to this. In his telling, a statistical model is a collection of distributions. For example, the collection of all normal distributions:
$$ textNormal(mu, sigma) : mu, sigma in R, sigma > 0 $$
or the set of all Poisson distributions:
$$ textPoisson(lambda) : lambda in R, lambda > 0 $$
Fitting a distribution to data is any algorithm that combines a statistical model with a set of data (the data is fixed), and chooses exactly one of the distributions from the model as the one that "best" reflects the data.
The model is the thing that changes (sort of): we are collapsing it from an entire collection of possibilities into a single best choice. The data is just the data; nothing happens to it at all.
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
edited Mar 25 at 23:55
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
answered Mar 24 at 4:44
Matthew DruryMatthew Drury
27.1k268108
27.1k268108
add a comment |
add a comment |
$begingroup$
In the field of Rasch modelling it is common to fit the data to the model. The model is assumed to be correct and it is the analyst's job to find data which conform to it. The Wikipedia article on Rasch contains more details about the how and the why.
But I agree with others that in general in statistics we fit the model to the data because we can change the model but it is felt to be bad form to select or modify the data.
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
$endgroup$
add a comment |
$begingroup$
In the field of Rasch modelling it is common to fit the data to the model. The model is assumed to be correct and it is the analyst's job to find data which conform to it. The Wikipedia article on Rasch contains more details about the how and the why.
But I agree with others that in general in statistics we fit the model to the data because we can change the model but it is felt to be bad form to select or modify the data.
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
$endgroup$
add a comment |
$begingroup$
In the field of Rasch modelling it is common to fit the data to the model. The model is assumed to be correct and it is the analyst's job to find data which conform to it. The Wikipedia article on Rasch contains more details about the how and the why.
But I agree with others that in general in statistics we fit the model to the data because we can change the model but it is felt to be bad form to select or modify the data.
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
$endgroup$
In the field of Rasch modelling it is common to fit the data to the model. The model is assumed to be correct and it is the analyst's job to find data which conform to it. The Wikipedia article on Rasch contains more details about the how and the why.
But I agree with others that in general in statistics we fit the model to the data because we can change the model but it is felt to be bad form to select or modify the data.
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
answered Mar 24 at 14:39
mdeweymdewey
12.6k72344
12.6k72344
add a comment |
add a comment |
$begingroup$
Typically, the observed data are fixed while the model is mutable (e.g. because parameters are estimated), so it is the model that is made to fit the data, not the other way around. (Usually people mean this case when they say either expression.)
When people say they fit data to a model I find myself trying to figure out what the heck did they do to the data?.
[Now if you're transforming data, that would arguably be 'fitting data to a model', but people almost never say that for this case.]
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
$endgroup$
5
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
1
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
1
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
$endgroup$
– BartoszKP
Mar 24 at 20:16
1
$begingroup$
+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
$endgroup$
– Christoph Hanck
Mar 25 at 15:37
add a comment |
$begingroup$
Typically, the observed data are fixed while the model is mutable (e.g. because parameters are estimated), so it is the model that is made to fit the data, not the other way around. (Usually people mean this case when they say either expression.)
When people say they fit data to a model I find myself trying to figure out what the heck did they do to the data?.
[Now if you're transforming data, that would arguably be 'fitting data to a model', but people almost never say that for this case.]
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
$endgroup$
5
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
1
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
1
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
$endgroup$
– BartoszKP
Mar 24 at 20:16
1
$begingroup$
+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
$endgroup$
– Christoph Hanck
Mar 25 at 15:37
add a comment |
$begingroup$
Typically, the observed data are fixed while the model is mutable (e.g. because parameters are estimated), so it is the model that is made to fit the data, not the other way around. (Usually people mean this case when they say either expression.)
When people say they fit data to a model I find myself trying to figure out what the heck did they do to the data?.
[Now if you're transforming data, that would arguably be 'fitting data to a model', but people almost never say that for this case.]
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
$endgroup$
Typically, the observed data are fixed while the model is mutable (e.g. because parameters are estimated), so it is the model that is made to fit the data, not the other way around. (Usually people mean this case when they say either expression.)
When people say they fit data to a model I find myself trying to figure out what the heck did they do to the data?.
[Now if you're transforming data, that would arguably be 'fitting data to a model', but people almost never say that for this case.]
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
answered Mar 24 at 8:13
Glen_b♦Glen_b
216k23421775
216k23421775
5
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
1
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
1
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
$endgroup$
– BartoszKP
Mar 24 at 20:16
1
$begingroup$
+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
$endgroup$
– Christoph Hanck
Mar 25 at 15:37
add a comment |
5
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
1
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
1
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
$endgroup$
– BartoszKP
Mar 24 at 20:16
1
$begingroup$
+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
$endgroup$
– Christoph Hanck
Mar 25 at 15:37
5
5
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
$begingroup$
Removing outliers would also (arguably) be "fitting data to a model".
$endgroup$
– Federico Poloni
Mar 24 at 8:34
1
1
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
$begingroup$
The phrasing might make sense if they're thinking of it as "fitting (data to a model)". That is, you're doing a process of fitting, and that process of fitting starts from data and transforms it to a model. I agree that's a less common/accurate interpretation versus the "(fitting X) to Y" parse, but I put it out there as a rationale as to why someone might logically say it.
$endgroup$
– R.M.
Mar 24 at 13:29
1
1
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
$endgroup$
– BartoszKP
Mar 24 at 20:16
$begingroup$
@FedericoPoloni Outliers are usually defined indepedently of the model that you later want to use. So even if we would want to call it fitting data, it would not be a model, but to something else.
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– BartoszKP
Mar 24 at 20:16
1
1
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+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
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– Christoph Hanck
Mar 25 at 15:37
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+1. There is a reason it's called "data" - it is what is given, see the Latin origin of the word: latindictionary.wikidot.com/verb:dare
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– Christoph Hanck
Mar 25 at 15:37
add a comment |
$begingroup$
Usually, we assume our data corresponds to the "real world" and making any modifications means we are moving away from modelling the "real world". For example, one needs to take care removing outliers since even if it makes computation nicer, outliers were still part of our data.
When testing a model or estimating properties of an estimator using bootstrap or other resampling techniques, we may simulate new data using an estimated model and our original data. This makes the assumption that the model is correct, and we are not modifying our original data.
answered Mar 24 at 21:34
qwrqwr
249113
$endgroup$
add a comment |
$begingroup$
Usually, we assume our data corresponds to the "real world" and making any modifications means we are moving away from modelling the "real world". For example, one needs to take care removing outliers since even if it makes computation nicer, outliers were still part of our data.
When testing a model or estimating properties of an estimator using bootstrap or other resampling techniques, we may simulate new data using an estimated model and our original data. This makes the assumption that the model is correct, and we are not modifying our original data.
answered Mar 24 at 21:34
qwrqwr
249113
$endgroup$
add a comment |
$begingroup$
Usually, we assume our data corresponds to the "real world" and making any modifications means we are moving away from modelling the "real world". For example, one needs to take care removing outliers since even if it makes computation nicer, outliers were still part of our data.
When testing a model or estimating properties of an estimator using bootstrap or other resampling techniques, we may simulate new data using an estimated model and our original data. This makes the assumption that the model is correct, and we are not modifying our original data.
answered Mar 24 at 21:34
qwrqwr
249113
$endgroup$
Usually, we assume our data corresponds to the "real world" and making any modifications means we are moving away from modelling the "real world". For example, one needs to take care removing outliers since even if it makes computation nicer, outliers were still part of our data.
When testing a model or estimating properties of an estimator using bootstrap or other resampling techniques, we may simulate new data using an estimated model and our original data. This makes the assumption that the model is correct, and we are not modifying our original data.
answered Mar 24 at 21:34
qwrqwr
249113
answered Mar 24 at 21:34
qwrqwr
249113
answered Mar 24 at 21:34
qwrqwr
249113
answered Mar 24 at 21:34
qwrqwr
249113
249113
add a comment |
add a comment |
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+1 I am not impressed by the second link, but I am entertained.
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– The Laconic
Mar 24 at 2:38
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Many models fits current data, but data typically fits best one model
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– Agnius Vasiliauskas
Mar 25 at 9:45