Kamakura’s Analytic Tools for Excel has a new off-campus home
Author: Wagner Kamakura
KATE (Kamakura’s Analytic Tools for Excel) has a new off-campus home
You will find KATE (plus tutorials) at www.KATExcel.com
This free suite of Excel AddIns allows you to run much more than linear regressions in Excel. Here is a list of analytic tools KATE has to offer:
Data extraction, transformation and visualization
- Data merge by keys – allows you to merge two sheets by up to three common key-columns..
- Scatterplot with labels – automatically add labels to a scatterplot, allowing you to jigger the labels to avoid crowding
- 3D Scatterplot – simple tool to produce and rotate 3D scatterplots; all you need is a label and three coordinates for each data point.
- ExtracText – simple tool for extracting, mining and coding words from text documents
- WordMap – produces a word and a document map representing the frequency and affinity of words in a sample of text documents
- K-means clustering – the good-old workhorse for classifying cases based on continuous data
- Latent Class Analysis – finite mixture modeling for categorical, ordinal and interval-scaled (i.e., Likert scale) data.
- Correspondence Analysis – space-reduction technique for categorical data more popular in Europe than in the US.
- Principal Components Analysis – another popular space-reduction technique, for continuous data.
- Dynamic Factor Analysis – similar to Principal Component Analysis, except that the factor scores represent smooth latent trends over time.
- Metric Multidimensional Scaling – a tool for unfolding a symmetric table of distances between cases into a multidimensional map.
- Stepwise Regression – a standard linear regression with stepwise selection of predictors.
- Logistic Regression – binary logistic regression.
- Local Geographic Regression – Estimates one regression for each data point, using data from its nearest-K neighbors (you must have geo-codes for each data point)
- Univariate PLS Regression – space reduction for a set of predictors while maximizing their fit to a continuous dependent variable.
- Multivariate PLS Regression – as extension of PLS Regression, for explaining multiple dependent variables using a set of predictors.
- Mixture Logit – random-coefficients Multinomial Logit choice model, producing individual-level estimates for the response coefficients.
- Stochastic Frontier Regression – linear regression with asymmetric errors; the regression line is fitted around the top or bottom of the cloud of points.
- Qualitative Data Envelopment Analysis – very flexible DEA tool that allows for qualitative inputs and outputs.
- Sliced Average Variance Estimation – space-reduction for a set of predictors while maximizing the fit to a binary dependent variable.
- Predictive Stepwise Linear Regression – stepwise predictor selection in a Linear Regression to maximize predictive fit in hold-out samples, rather than calibration fit
- Predictive Stepwise Logistic Regression – stepwise predictor selection in a Binary Logistic Regression to maximize predictive fit in hold-out samples.