Linear regression with a continuous predictor is set up to measure the constant relationship between that predictor and a continuous outcome.
This relationship is measured in the expected change in the outcome for each one-unit change in the predictor.
One big assumption in this kind of model, though, is that this rate of change is the same for every value of the predictor. It’s an assumption we need to question, though, because it’s not a good approach for a lot of relationships.
Segmented regression allows you to generate different slopes and/or intercepts for different segments of values of the continuous predictor. This can provide you with a wealth of information that a non-segmented regression cannot.
In this webinar, we will cover (more…)
It’s that time of year: flu season.
Let’s imagine you have been asked to determine the factors that will help a hospital determine the length of stay in the intensive care unit (ICU) once a patient is admitted.
The hospital tells you that once the patient is admitted to the ICU, he or she has a day count of one. As soon as they spend 24 hours plus 1 minute, they have stayed an additional day.
Clearly this is count data. There are no fractions, only whole numbers.
To help us explore this analysis, let’s look at real data from the State of Illinois. We know the patients’ ages, gender, race and type of hospital (state vs. private).
A partial frequency distribution looks like this: (more…)
In a simple linear regression model, how the constant (a.k.a., intercept) is interpreted depends upon the type of predictor (independent) variable.
If the predictor is categorical and dummy-coded, the constant is the mean value of the outcome variable for the reference category only. If the predictor variable is continuous, the constant equals the predicted value of the outcome variable when the predictor variable equals zero.
Removing the Constant When the Predictor Is Categorical
When your predictor variable X is categorical, the results are logical. Let’s look at an example. (more…)
The concept of a statistical interaction is one of those things that seems very abstract. Obtuse definitions, like this one from Wikipedia, don’t help:
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.
First, we know this is true because we read it on the internet! Second, are you more confused now about interactions than you were before you read that definition? (more…)
In a previous post , Using the Same Sample for Different Models in Stata, we examined how to use the same sample when comparing regression models. Using different samples in our models could lead to erroneous conclusions when interpreting results.
But excluding observations can also result in inaccurate results.
The coefficient for the variable “frequent religious attendance” was negative 58 in model 3 (more…)
I received a question recently about R Commander, a free R package.
R Commander overlays a menu-based interface to R, so just like SPSS or JMP, you can run analyses using menus. Nice, huh?
The question was whether R Commander does everything R does, or just a small subset.
Unfortunately, R Commander can’t do everything R does. Not even close.
But it does a lot. More than just the basics.
So I thought I would show you some of the things R Commander can do entirely through menus–no programming required, just so you can see just how unbelievably useful it is.
Since R commander is a free R package, it can be installed easily through R! Just type install.packages("Rcmdr") in the command line the first time you use it, then type library("Rcmdr") each time you want to launch the menus.
Data Sets and Variables
Import data sets from other software:
- SPSS
- Stata
- Excel
- Minitab
- Text
- SAS Xport
Define Numerical Variables as categorical and label the values
Open the data sets that come with R packages
Merge Data Sets
Edit and show the data in a data spreadsheet
Personally, I think that if this was all R Commander did, it would be incredibly useful. These are the types of things I just cannot remember all the commands for, since I just don’t use R often enough.
Data Analysis
Yes, R Commander does many of the simple statistical tests you’d expect:
- Chi-square tests
- Paired and Independent Samples t-tests
- Tests of Proportions
- Common nonparametrics, like Friedman, Wilcoxon, and Kruskal-Wallis tests
- One-way ANOVA and simple linear regression
What is surprising though, is how many higher-level statistics and models it runs:
- Hierarchical and K-Means Cluster analysis (with 7 linkage methods and 4 options of distance measures)
- Principal Components and Factor Analysis
- Linear Regression (with model selection, influence statistics, and multicollinearity diagnostic options, among others)
- Logistic regression for binary, ordinal, and multinomial responses
- Generalized linear models, including Gamma and Poisson models
In other words–you can use R Commander to run in R most of the analyses that most researchers need.
Graphs
A sample of the types of graphs R Commander creates in R without you having to write any code:
- QQ Plots
- Scatter plots
- Histograms
- Box Plots
- Bar Charts
The nice part is that it does not only do simple versions of these plots. You can, for example, add regression lines to a scatter plot or run histograms by a grouping factor.
If you’re ready to get started practicing, click here to learn about making scatterplots in R commander, or click here to learn how to use R commander to sample from a uniform distribution.
