#Stata histograms full#

The last line resulting from the above вЂњinspectвЂќ function warns us that something may be wrong with this dataset.

In addition, the вЂњinspectвЂќ function also allows a cursory check for when something may be wrong with the dataset: Observe, for example, the histograms that are quickly generated by inspecting the variables of price, mpg, headroom, and trunk:ДЄll histograms are readily available in one output, and the need for opening a slew of. A caveat: вЂњinspectвЂќ is not a replacement or substitute for вЂњsummarizeвЂќ and вЂњtabulateвЂќ-its purpose is not analytical but it does allow you to quickly gain familiarity with unknown data and gain insight into the values stored in a variable. It provides negative, positive, zero, missing, unique values, integer and non-integer values, and a histogram of the variables. The вЂњinspectвЂќ command in Stata provides detailed information on the numeric variables, which makes it great for a quick observation of Likert-scaled data. In retrospect, a much simpler way to solve this problem was to use the вЂњinspectвЂќ command. png files that we subsequently had to click through and match to our Excel presentation sheet.

#Stata histograms full#

Cue the lines of coding and looping to generate a folder full of. Our first gut reaction was to graph the density and distribution of the data into a histogram or box plot. On our Excel presentation sheet, we were to choose from the following options to describe the populationвЂ™s view regarding each variable: right-skewed (generally very dissatisfied), left-skewed (generally very satisfied), U-shaped (most were either very dissatisfied or very satisfied, with few being neutral), or normal-shaped (most were neutral, with few being either very dissatisfied or very satisfied). The data originated from a satisfaction survey (1=very dissatisfied 5=very satisfied). On a recent project, the client wanted an idea of the skew of each of a large number of variables.