Missing data are a frequently encountered problem in epidemiologic and clinical research.1, 2 One approach is to include in the analysis only those participants without missing observations (complete ...

Example 55.3: Regression with Quantitative and Qualitative Variables At times it is desirable to have independent variables in the model that are qualitative rather than quantitative. This is easily ...

Being able to make conclusions about data trends is one of the most important steps in both business and science. Through the magic of least sums regression, and with a few simple equations, we can ...

Given the data shown in Figure 1, suppose we wanted to estimate a score for someone who had spent exactly 2.3 hours on an essay. I’m sure most of us have experience in drawing lines of best fit, where ...

Example 55.4: Displaying Plots for Simple Linear Regression This example introduces the basic PROC REG graphics syntax used to produce a standard plot of data from the aerobic fitness data set ...

We’ll use data taken from a Pew Research Center 2016 post-election survey, and you can download the dataset for your own use here. We’ll discuss both bivariate regression, which has one outcome ...

To improve on the regression model, the researcher would have to try out other explanatory variables that could provide a more accurate fit to the data. If, for example, some students had seen the ...

Research data; Statistical methods ... then there may be high variation in the regression coefficient estimate between different samples—for example, when VIF > 10, the regression coefficients ...

Functional data analysis (FDA) in which each data sample is a function rather than a vector or a matrix has attracted a lot of attention in the statistics community in recent years. However, most of ...

An example of a stepwise regression using the backward elimination method would be an attempt to understand energy usage at a factory using variables such as equipment run time, equipment age ...