Forget about Excel. It is hopeless for any serious work. (If you need convincing, see Bruce McCullough’s articles on Excel.) Any research student in the quantitative sciences should be using a matrix language for computation. I recommend students use R. It is free, has a big user base, and has zillions of add-on packages.

### Blog posts about R

- Animated plots in R and LaTeX
- ARIMA models with long lags
- Automatic time series forecasting in Granada
- Backcasting in R
- Batch forecasting in R
- Blog aggregators
- Building R packages for Windows
- Coherent population forecasting using R
- Comparing HoltWinters() and ets()
- COMPSTAT2012
- Computational Actuarial Science with R
- Constants and ARIMA models in R
- Cover of my forecasting textbook
- CrossValidated Journal Club
- CrossValidated launched!
- Data visualization
- Data visualization videos
- Debugging in R
- Detecting seasonality
- Different results from different software
- Econometrics and R
- Errors on percentage errors
- Estimating a nonlinear time series model in R
- ETS models now in EViews 8
- European talks. June-July 2014
- Exponential smoothing and regressors
- Facts and fallacies of the AIC
- Fast computation of cross-validation in linear models
- Feedback on OTexts covers please
- Finding an R function
- Fitting models to short time series
- Flat forecasts
- Forecast estimation, evaluation and transformation
- forecast package v4.0
- Forecasting annual totals from monthly data
- Forecasting the Olympics
- Forecasting time series using R
- Forecasting weekly data
- Forecasting with daily data
- Forecasting with long seasonal periods
- Forecasting with R
- Forecasting within limits
- Forecasting workshop: Switzerland, June 2011
- Forecasts and ggplot
- Free books on statistical learning
- Global energy forecasting competitions
- Happy World Statistics Day!
- Hierarchical forecasting with hts v4.0
- How to avoid annoying a referee
- Initializing the Holt-Winters method
- Internet surveys
- Interview for the Capital of Statistics
- Interviews
- Job at Center for Open Science
- Judgmental forecasting experiment
- Kaggle on TV
- Learning R by video
- Looking for a new post-doc
- Major changes to the forecast package
- Makefiles for R/LaTeX projects
- Measuring time series characteristics
- More StackExchange sites
- More time series data online
- My forecasting book now on Amazon
- My new forecasting book is finally finished
- My new forecasting textbook
- New in forecast 5.0
- New jobs in business analytics at Monash
- Online course on forecasting using R
- Organization and R
- Out-of-sample one-step forecasts
- Plotting the characteristic roots for ARIMA models
- Publishing an R package in the Journal of Statistical Software
- Questions on my online forecasting course
- R books
- R graph with two y-axes
- R help links
- R help on StackOverflow
- R workshop
- Reflections on UseR! 2013
- Removing white space around R figures
- RStudio: just what I’ve been looking for
- Seven forecasting blogs
- SimpleR tips, tricks and tools
- Six places left for the forecasting workshop
- Slides from my online forecasting course
- Specifying complicated groups of time series in hts
- Stack exchange for statistical analysis needs you!
- Statistical Analysis StackExchange site now available
- Statistical tests for variable selection
- Testing for trend in ARIMA models
- The ARIMAX model muddle
- The art of R programming
- The forecast mean after back-transformation
- Thoughts on the Ljung-Box test
- Three jobs at Monash
- Time series cross-validation: an R example
- Time series data in R
- Time Series Data Library now on DataMarket
- Time series packages on R
- Twenty rules for good graphics
- Unit root tests and ARIMA models
- Using old versions of R packages
- Varian on big data
- Variations on rolling forecasts
- What should we call the stats Q&A site?
- What you wish you knew before you started a PhD
- Why are some things easier to forecast than others?
- Why R is better than Excel for teaching statistics
- Workflow in R

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