> I am trying to get started in statistical modeling and stats, as I am
> supposed to hold a stats course at my institution in the next semester.
> Having spent some time with S, and having looked at R, I hated the syntax of
> R, and would like to do as much as possible in Python. "statsmodels" seems
> to be the leading Python package for statistical modeling. But I have to
> admit, that with the documentation provided, I have a VERY hard time getting
> started.

> Can anyone recommend any literature on statistical modeling, which might
> help me getting up to speed?

> Thanks,
> thomas

>> I am trying to get started in statistical modeling and stats, as I am
>> supposed to hold a stats course at my institution in the next semester.
>> Having spent some time with S, and having looked at R, I hated the syntax of
>> R, and would like to do as much as possible in Python. "statsmodels" seems
>> to be the leading Python package for statistical modeling. But I have to
>> admit, that with the documentation provided, I have a VERY hard time getting
>> started.

>> Can anyone recommend any literature on statistical modeling, which might
>> help me getting up to speed?

> What field, what topics, what level?

> I use mostly econometrics, specialized textbooks and articles.

> In statistics, there are some books partially oriented towards R but should
> be useful for working with statsmodels.

> For many fields there are specifically written textbooks, where statsmodels
> might cover some range.

> One difference between textbooks and fields is for example the
> emphasis on experimental and categorical data versus
> non-experimental data and associated problems in econometrics.

> Hopefully someone can answer with books that they used.

> I can look up some general references but except for a few
> econometrics books I never worked through any of them.

> The book Skipper and I used most often as reference is
> http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
> 1st year PhD level in economics

> for econometrics in undergraduate economics, Stock Watson is good
> http://www.pearsonhighered.com/stock_watson/

> Fox and Weisberg books "sound" good
> I never read them, except for some appendices
> http://cran.r-project.org/web/packages/car/index.html
> http://socserv.mcmaster.ca/jfox/Books/Companion/index.html
> http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/in...
> http://www.stat.umn.edu/alr/

> Jonathan's course is useful for statistics
> http://www.stanford.edu/class/stats191/

> It would be good if we could collect some references for the documentation.
> I started a Latex bib file, but didn't get very far yet.

> Josef

>> Thanks,
>> thomas

>>> I am trying to get started in statistical modeling and stats, as I am
>>> supposed to hold a stats course at my institution in the next semester.
>>> Having spent some time with S, and having looked at R, I hated the syntax of
>>> R, and would like to do as much as possible in Python. "statsmodels" seems
>>> to be the leading Python package for statistical modeling. But I have to
>>> admit, that with the documentation provided, I have a VERY hard time getting
>>> started.

>>> Can anyone recommend any literature on statistical modeling, which might
>>> help me getting up to speed?

>> What field, what topics, what level?

>> I use mostly econometrics, specialized textbooks and articles.

>> In statistics, there are some books partially oriented towards R but should
>> be useful for working with statsmodels.

>> For many fields there are specifically written textbooks, where statsmodels
>> might cover some range.

>> One difference between textbooks and fields is for example the
>> emphasis on experimental and categorical data versus
>> non-experimental data and associated problems in econometrics.

>> Hopefully someone can answer with books that they used.

>> I can look up some general references but except for a few
>> econometrics books I never worked through any of them.

>> The book Skipper and I used most often as reference is
>> http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
>> 1st year PhD level in economics

>> for econometrics in undergraduate economics, Stock Watson is good
>> http://www.pearsonhighered.com/stock_watson/

>> Fox and Weisberg books "sound" good
>> I never read them, except for some appendices
>> http://cran.r-project.org/web/packages/car/index.html
>> http://socserv.mcmaster.ca/jfox/Books/Companion/index.html
>> http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/in...
>> http://www.stat.umn.edu/alr/

>> Jonathan's course is useful for statistics
>> http://www.stanford.edu/class/stats191/

>> It would be good if we could collect some references for the documentation.
>> I started a Latex bib file, but didn't get very far yet.

> ------------------
> just to have an incomplete list on special topics
> mostly advanced topics

> time series analysis
> ~~~~~~~~~~~~~~~

> Lütkepohl,  used for VAR
> http://www.springer.com/economics/econometrics/book/978-3-540-40172-8
> Hamilton, the classic
> http://press.princeton.edu/titles/5386.html

> microeconometrics
> ~~~~~~~~~~~~~~~
> Logit, ... in discrete
> Cameron Trivedi
> http://cameron.econ.ucdavis.edu/mmabook/mmaprograms.html

> robust
> ~~~~~
> Skipper used 1st edition
> http://www.amazon.com/Robust-Statistics-Wiley-Probability/dp/0470129905

> GLM generalized linear models
> ~~~~~~~~~~~~~~~~~~~~~~~~

> others
> ~~~~~
> Owen: empirical likelihood
> Li, Racine: kernel density estimation, kernel regression

> Stata, SAS, SPSS and R manuals for specific procedures and methods

> Josef

>> Josef

>>> Thanks,
>>> thomas

>>>> I am trying to get started in statistical modeling and stats, as I am
>>>> supposed to hold a stats course at my institution in the next semester.
>>>> Having spent some time with S, and having looked at R, I hated the syntax of
>>>> R, and would like to do as much as possible in Python. "statsmodels" seems
>>>> to be the leading Python package for statistical modeling. But I have to
>>>> admit, that with the documentation provided, I have a VERY hard time getting
>>>> started.

>>>> Can anyone recommend any literature on statistical modeling, which might
>>>> help me getting up to speed?

>>> What field, what topics, what level?

>>> I use mostly econometrics, specialized textbooks and articles.

>>> In statistics, there are some books partially oriented towards R but should
>>> be useful for working with statsmodels.

>>> For many fields there are specifically written textbooks, where statsmodels
>>> might cover some range.

>>> One difference between textbooks and fields is for example the
>>> emphasis on experimental and categorical data versus
>>> non-experimental data and associated problems in econometrics.

>>> Hopefully someone can answer with books that they used.

>>> I can look up some general references but except for a few
>>> econometrics books I never worked through any of them.

>>> The book Skipper and I used most often as reference is
>>> http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
>>> 1st year PhD level in economics

>>> for econometrics in undergraduate economics, Stock Watson is good
>>> http://www.pearsonhighered.com/stock_watson/

>>> Fox and Weisberg books "sound" good
>>> I never read them, except for some appendices
>>> http://cran.r-project.org/web/packages/car/index.html
>>> http://socserv.mcmaster.ca/jfox/Books/Companion/index.html
>>> http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/in...
>>> http://www.stat.umn.edu/alr/

>>> Jonathan's course is useful for statistics
>>> http://www.stanford.edu/class/stats191/

>>> It would be good if we could collect some references for the documentation.
>>> I started a Latex bib file, but didn't get very far yet.

>> ------------------
>> just to have an incomplete list on special topics
>> mostly advanced topics

>> time series analysis
>> ~~~~~~~~~~~~~~~

>> Lütkepohl,  used for VAR
>> http://www.springer.com/economics/econometrics/book/978-3-540-40172-8
>> Hamilton, the classic
>> http://press.princeton.edu/titles/5386.html

>> microeconometrics
>> ~~~~~~~~~~~~~~~
>> Logit, ... in discrete
>> Cameron Trivedi
>> http://cameron.econ.ucdavis.edu/mmabook/mmaprograms.html

>> robust
>> ~~~~~
>> Skipper used 1st edition
>> http://www.amazon.com/Robust-Statistics-Wiley-Probability/dp/0470129905

>> GLM generalized linear models
>> ~~~~~~~~~~~~~~~~~~~~~~~~

> Implementation reference and good, basic info

> http://www.stata.com/bookstore/generalized-linear-models-and-extensions/

>> others
>> ~~~~~
>> Owen: empirical likelihood
>> Li, Racine: kernel density estimation, kernel regression

>> Stata, SAS, SPSS and R manuals for specific procedures and methods

>> Josef

>>> Josef

>>>> Thanks,
>>>> thomas

>>>>> I am trying to get started in statistical modeling and stats, as I am
>>>>> supposed to hold a stats course at my institution in the next semester.
>>>>> Having spent some time with S, and having looked at R, I hated the syntax of
>>>>> R, and would like to do as much as possible in Python. "statsmodels" seems
>>>>> to be the leading Python package for statistical modeling. But I have to
>>>>> admit, that with the documentation provided, I have a VERY hard time getting
>>>>> started.

>>>>> Can anyone recommend any literature on statistical modeling, which might
>>>>> help me getting up to speed?

>>>> What field, what topics, what level?

>>>> I use mostly econometrics, specialized textbooks and articles.

>>>> In statistics, there are some books partially oriented towards R but should
>>>> be useful for working with statsmodels.

>>>> For many fields there are specifically written textbooks, where statsmodels
>>>> might cover some range.

>>>> One difference between textbooks and fields is for example the
>>>> emphasis on experimental and categorical data versus
>>>> non-experimental data and associated problems in econometrics.

>>>> Hopefully someone can answer with books that they used.

>>>> I can look up some general references but except for a few
>>>> econometrics books I never worked through any of them.

>>>> The book Skipper and I used most often as reference is
>>>> http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
>>>> 1st year PhD level in economics

>>>> for econometrics in undergraduate economics, Stock Watson is good
>>>> http://www.pearsonhighered.com/stock_watson/

>>>> Fox and Weisberg books "sound" good
>>>> I never read them, except for some appendices
>>>> http://cran.r-project.org/web/packages/car/index.html
>>>> http://socserv.mcmaster.ca/jfox/Books/Companion/index.html
>>>> http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/in...
>>>> http://www.stat.umn.edu/alr/

>>>> Jonathan's course is useful for statistics
>>>> http://www.stanford.edu/class/stats191/

>>>> It would be good if we could collect some references for the documentation.
>>>> I started a Latex bib file, but didn't get very far yet.

>>> ------------------
>>> just to have an incomplete list on special topics
>>> mostly advanced topics

>>> time series analysis
>>> ~~~~~~~~~~~~~~~

>>> Lütkepohl,  used for VAR
>>> http://www.springer.com/economics/econometrics/book/978-3-540-40172-8
>>> Hamilton, the classic
>>> http://press.princeton.edu/titles/5386.html

>>> microeconometrics
>>> ~~~~~~~~~~~~~~~
>>> Logit, ... in discrete
>>> Cameron Trivedi
>>> http://cameron.econ.ucdavis.edu/mmabook/mmaprograms.html

>>> robust
>>> ~~~~~
>>> Skipper used 1st edition
>>> http://www.amazon.com/Robust-Statistics-Wiley-Probability/dp/0470129905

>>> GLM generalized linear models
>>> ~~~~~~~~~~~~~~~~~~~~~~~~

>> Implementation reference and good, basic info

>> http://www.stata.com/bookstore/generalized-linear-models-and-extensions/

>>> others
>>> ~~~~~
>>> Owen: empirical likelihood
>>> Li, Racine: kernel density estimation, kernel regression

>>> Stata, SAS, SPSS and R manuals for specific procedures and methods

> (one more)

> on the web one of the best collections is by UCLA stats. I look at it
> every once in a while
> for example
> http://www.ats.ucla.edu/stat/dae/
> http://www.ats.ucla.edu/stat/examples/default.htm

> I wish we had those tutorials for statsmodels, (and had our main gaps filled)

> Josef

>>> Josef

>>>> Josef

>>>>> Thanks,
>>>>> thomas