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Different types of quantitative analysis tools include graphs, linear regressions and hypothesis testing. These tools provide analysts with statistical methods of organizing and examining data. These tools are useful for analyzing survey results, historical data or financial numbers. They can also be used for forecasting or determining the probability of a particular event happening. Such quantitative analysis tools require the analyst to have basic mathematical skills and can be done in most spreadsheet software.
Graphs are a way of visually organizing data in order to gain better insight into what the numbers show and to easily detect patterns. These graphs for quantitative analysis can be found in the form of bars, lines and dots. The most common type of graph for quantitative data is the histogram. A histogram is a bar graph that is constructed by arranging the data into ranges. For instance, it can be used to create a bar graph to show the number of sales per month for products in different price ranges. Since quantitative analysis tools cannot be used for qualitative data, a business cannot use a histogram for categorizing products in groups based on names, colors or locations.
Linear regressions are a popular quantitative analysis tool that is used to determine the relationship between two sets of related data. If the analyst determines the data to have a strong correlation, the data can be graphed so that predictions can be made. For example, if there is a strong correlation between the number of daily Web site visitors and the advertisement revenue, then the analyst can determine how many visitors per month are needed for the Web site to earn a targeted amount of advertisement revenue. When needing to make forecasts based on the results of several variables, a multiple regression analysis can be done through the use of more advanced calculations.
Hypothesis testing is used by businesses when determining the probability of an event happening under specific conditions. It is generally done by collecting customer data from surveys and then using hypothesis testing quantitative analysis tools to determine the likelihood of a member of the general population to have the same response or characteristic. The accuracy of hypothesis testing depends largely on the size of the sample population, randomly selecting from the population, accuracy of the questions, and errors in collecting the information. This is most commonly used by marketers to test a new product or gain insight into public opinion about current offerings.
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