Why should you use R?
There exists several reasons why one should start using R. During the last decade R has become the leading tool for statistics, data analysis, and machine learning. By now, R represents a viable alternative to traditional statistical programs such as Stata, SPSS, SAS, and Matlab. The reasons for R’s success are manifold. Continue reading Why R?
What is seasonal adjustment?
Seasonal adjustment refers to a statistical technique that tries to quantify and remove the influences of predictable seasonal patterns to reveal nonseasonal changes in data that would otherwise be overshadowed by the seasonal differences. Seasonal adjustments provide a Continue reading Seasonal adjustment
The easiest way to compute clustered standard errors in R is the modified
summary(). I added an additional parameter, called
cluster, to the conventional
summary() function. This parameter allows to specify a variable that defines the group / cluster in your data. The summary output will return clustered standard errors. Here is the syntax:
summary(lm.object, cluster=c("variable")) Continue reading Clustered Standard Errors in R
The following R script creates an example dataset to illustrate the application of clustered standard errors. You can download the dataset here.
The script creates a dataset with a specific number of student test results. Individual students are identified via the variable
student_id . The variable
id_score comprises a student’s test score. In the test, students can score from 1 to 10 with 10 being the highest score possible. Continue reading Example data – Clustered Standard Errors
The following article tries to explain the Balance Statistic sometimes referred to as Saldo or Saldo Statistic. It is used as a quantification method for qualitative survey question. The benefit of applying the Balance Statistic arises when the survey is repeated over time as it tracks changes in respondents answers in a comprehensible way. The Balance Statistic is common in Business Tendency Surveys.
Continue reading Balance Statistic
Stochastic Independence versus Stochastic Dependence
In order to fully understand the Bayesian rule it is important to be familiar with some concepts of standard probability theory. Assume we have two events, let’s call them A and B. The probability that event A occurs is P(A) and the probability that event B occurs is P(B). If event A and event B are independent from each other, the probability that both events are occurring at the same time, also known as the joint probability .
Continue reading Stochastic Independence versus Stochastic Dependence
All cases in which manifestations have exactly two characteristics follow a Bernoulli distribution.
Typical examples are coin flip or medical treatment, which works or not.
Continue reading Bernoulli Distribution