In addition to the VBA code itself, each macro is accompanied by a very specific explanation of how each application is structured and what the purpose of each statement is. Instead of trying to re-invent the wheel, in this tutorial I show you 5 of the most common examples of macros to delete blank rows or rows with empty cells quickly and easily. In fact, you can find many different suggestions online. There are several ways to create a macro that deletes blank rows or rows with blanks cells. You can do this, among others, using macros. The main point is: you want to be able to delete blank rows or rows that have blank cells quickly and easily. This includes, for example, when you import an Excel worksheet into other applications, such as Access.
Blank rows can generate several different problems. In this tutorial, I focus on a very “dangerous”, annoying and common situation when working with data: the existence of blank rows and cells in data sets. Data scientists spend a substantial amount of time collecting and preparing data.Īs you're probably aware, the process of cleaning up data with Excel can be automated to a certain extent using tools such as Text functions or macros. This is how you calculate mean, median and mode in Excel.You're probably aware of how annoying and time-consuming cleaning up data can be. Now, the average wage becomes $2,451.67/hour, a wage that no one earns! By contrast, the median is not significantly changed by this one outlier, it is $50.50/hour.Īgree, the median gives a better idea of what people typically earn because it is not so strongly affected by abnormal salaries. Well, in this particular case the mean and median give similar numbers.īut let's see what happens if we extend the list of wages by including a celebrity who earns, say, about $30 million/year, which is roughly $14,500/hour.
The median wage is $47/hour, and it is the police officer who earns it (1/2 wages are lower, and 1/2 are higher).
Now, let's calculate the average (mean): add up the above numbers and divide by 5: (20+26+47+54+63)/5=42. Please have a look at a few sample salaries for common jobs: Why? The best way to understand this would be from an example. Since the mean is greatly affected by skewed data and outliers (non-typical values that are significantly different from the rest of the data), median is the preferred measure of central tendency for an asymmetrical distribution.įor instance, it is generally accepted that the median is better than the mean for calculating a typical salary. For a skewed distribution (where there are a small number of extremely high or low values), the three measures of central tendency may be different. Which measure to use mostly depends on the type of data you are working with as well as your understanding of the "typical value" you are attempting to estimate.įor a symmetrical distribution (in which values occur at regular frequencies), the mean, median and mode are the same. Generally, there is no "best" measure of central tendency. In situations when there are two or more modes in your data set, the Excel MODE function will return the lowest mode. For our sample data set, the formula goes as follows: In Microsoft Excel, you can calculate a mode by using the function of the same name, the MODE function. The mean is calculated by adding up a group of numbers and then dividing the sum by the count of those numbers.įor example, to calculate the mean of numbers is 2. They all are valid measures of central location, but each gives a different indication of a typical value, and under different circumstances some measures are more appropriate to use than others.Īrithmetic mean, also referred to as average, is probably the measure you are most familiar with. The three main measures of central tendency are Mean, Median and Mode. Sometimes, they are also classified as summary statistics. For this purpose, you can use the so-called measures of central tendency that represent a single value identifying the central position within a data set or, more technically, the middle or center in a statistical distribution. When analyzing numerical data, you may often be looking for some way to get the "typical" value.
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