{"id":223826,"date":"2020-03-13T09:04:40","date_gmt":"2020-03-13T03:34:40","guid":{"rendered":"https:\/\/www.qcsglobal.com\/marketing\/5-statistical-analysis-methods-that-take-data-to-the-next-level\/"},"modified":"2020-04-18T17:10:41","modified_gmt":"2020-04-18T11:40:41","slug":"5-statistical-analysis-methods-that-take-data-to-the-next-level","status":"publish","type":"post","link":"https:\/\/qcsglobal.com\/blogs\/5-statistical-analysis-methods-that-take-data-to-the-next-level\/","title":{"rendered":"5 Statistical Analysis Methods That Take Data to the Next Level"},"content":{"rendered":"


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The average business has radically changed over the last decade.<\/p>\n

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Whether it\u2019s the equipment used at desks or the software used to communicate, very few things look the same as they once were.<\/p>\n

Something else that is completely different is how much data we have at our fingertips. What was once scarce is now a seemingly overwhelming amount of data. But, it\u2019s only overwhelming if you don\u2019t know how to analyze your business\u2019s data to find true and insightful meaning.<\/p>\n

So, how do you go from point A, having a vast amount of data, to point B, being able to accurately interpret that data? It all comes down to using the right methods for statistical analysis<\/a>, which is how we process and collect samples of data to uncover patterns and trends.<\/p>\n

For this analysis, there are five to choose from: mean, standard deviation, regression, hypothesis testing, and sample size determination.<\/p>\n

Interested in learning something specific about these five methods? Jump ahead to:<\/p>\n

Mean<\/a>
Standard deviation<\/a>
Regression<\/a>
Hypothesis testing<\/a>
Sample size determination<\/a><\/p>\n

The 5 methods for performing statistical analysis<\/h2>\n

There\u2019s no denying that the world is becoming obsessed with big data<\/a>, no matter if you\u2019re a data scientist or not. Because of this, you need to know where to start. These five methods are basic, yet effective, in coming to accurate data-driven conclusions.<\/p>\n

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1. Mean<\/h3>\n

The first method that\u2019s used to perform the statistical analysis is mean, which is more commonly referred to as the average. When you\u2019re looking to calculate the mean, you add up a list of numbers and then divide that number by the items on the list.<\/p>\n

When this method is used it allows for determining the overall trend of a data set, as well as the ability to obtain a fast and concise view of the data. Users of this method also benefit from the simplistic and quick calculation.<\/p>\n

The statistical mean is coming up with the central point of the data that\u2019s being processed. The result is referred to as the mean of the data provided. In real life, people typically use mean to in regards to research, academics, and sports. Think of how many times a player\u2019s batting average is discussed in baseball; that\u2019s their mean.<\/p>\n

How to find the mean<\/h4>\n

To find the mean of your data, you would first add the numbers together, and then divide the sum by how many numbers are within the dataset or list.<\/p>\n

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As an example, to find the mean of 6, 18, and 24, you would first add them together.
6 + 18 + 24 = 48<\/p>\n

<\/span>Then, divide by how many numbers in the list (3).
<\/span>48 \/ 3 = 16<\/p>\n

<\/span>The mean is 16.<\/span><\/p>\n<\/blockquote>\n

The downside<\/h4>\n

When using mean is great, it\u2019s not recommended as a standalone statistical analysis method. This is because doing so can potentially ruin the complete efforts behind the calculation, seeing as it is also related to the mode (the value that occurs most often) and median (the middle) in some data sets.<\/p>\n

When you\u2019re dealing with a large number of data points with either a high number of outliers (a data point that differs significantly from others) or an inaccurate distribution of data, the mean doesn\u2019t give the most accurate results in statistical analytics for a specific decision.<\/p>\n

<\/a><\/p>\n

2. Standard deviation<\/h3>\n

Standard deviation is a method of statistical analysis that measures the spread of data around the mean.<\/p>\n

When you\u2019re dealing with a high standard deviation, this points to data that\u2019s spread widely from the mean. Similarly, a low deviation shows that most data is in line with the mean and can also be called the expected value of a set.<\/p>\n

Standard deviation is mainly used when you need to determine the dispersion of data points (whether or not they\u2019re clustered).<\/p>\n

LEARN MORE: <\/strong>Clustering is a data mining technique<\/a> that groups large quantities of data together based on their similarities.<\/p>\n

Let\u2019s say you\u2019re a marketer who recently conducted a customer survey. Once you get the results of the survey, you\u2019re interested in measuring the reliability of the answers in order to predict if a larger group of customers might have the same answers. If a low standard deviation occurs, it would show that the answers can be projected to a larger group of customers.<\/p>\n

How to find the standard deviation<\/h4>\n

The formula to calculate the standard deviation is:<\/p>\n

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\u03c32 = \u03a3(x \u2212 \u03bc)2\/n<\/p>\n<\/blockquote>\n

In this formula:<\/p>\n