What are the different data analysis techniques?

Data analysis techniques allow scientists to check the collected data and implement conclusions or determination. Most techniques focus on the application of quantitative techniques to review data. Several more popular quantitative data analysis techniques include descriptive statistics, exploratory data analysis and confirming data analysis. The last two include the use of support or not supporting predetermined hypotheses. Groups that can use these techniques include individual scientists, students, businesses, government agencies and mathematics, including information and data.

Quantitative data analysis attempts to remove the distortion of research staff from the data collected. Strong use of statistics, probabilities or other mathematical techniques allows individuals to use standard data interpretation methods. When scientists try to use qualitative data analysis techniques - often based on an individual's personal background, preferred or basic principles of researchfor and thinking - the data collected may be incorrectly readable or incorrectly interpreted. Mathematical techniques are therefore less susceptible to these errors and often receive more individual individuals or scientists.

Descriptive statistical analysis separates or summarizes data into specific groups. Demography is a common set of descriptive statistics. Scientists collect information on the age of population, gender, household size, income, type of work and other information. Another type of descriptive statistical analysis is the percentage of completion of Quarterback while playing in a football match. If Quarterback complements six out of eight attempts to pass, the percentage of completion is 75 percent. The error in this technique is the inability to provide more information, such as the length of each passage.

reconnaissance data atechniky naysis often include the use of boxes, histograms, pareto graphs, scattering charts or STonka and leaves. The main purpose of this technique is to support the hypothesis from the researcher. For example, a researcher can desire to prove a hypothesis on the age range of owners who drive a specific type of car, such as minivan. For testing and supporting this hypothesis, the researcher collects information and creates a conspiracy to determine the number of owners to the extent. Statistics provide information to support or do not support the hypothesis and show how many secluded values ​​are in the data collected.

techniques confirming data analysis are the opposite of reconnaissance techniques. In these tests, the researcher tries to refute the zero hypothesis, which is generally accepted as a true majority of individuals. To refute the null hypothesis, the researcher collects specific information on the hypothesis and tests Avevztek, scattering, values ​​of P and reliability intervals. The reliability interval proved by confirming data analysis will provide information on how confident it wouldl Be a research worker about whether the zero hypothesis is true or false.