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metesananaliz

Statistical Analysis Methods

istatistiksel analiz yöntemleri

Statistical analysis methods refer to various techniques used to examine data sets, make sense of them, and draw conclusions. Each method is used based on a specific data type or purpose for analysis. Depending on the nature and size of the data set and the questions to be analyzed, choosing the appropriate statistical method is essential. Some commonly used statistical analysis methods are:


  1. Descriptive Statistics: These are methods used to describe the basic features of the data set. These methods include statistical measures such as mean, median, mode, standard deviation, variance, percentiles, quartiles, and ranges.

  2. Statistical Distribution Tests: These are methods used to test the suitability of the data set to a particular distribution. Tests such as regular distribution, t-test, chi-square, F, and Kolmogorov-Smirnov tests fall into this category.

  3. Parametric and Non-parametric Tests: These tests determine differences between two or more groups. While parametric tests are based on the assumption that the data fits a particular distribution, non-parametric tests do not require this assumption. Examples include t-test, ANOVA (analysis of variance), Mann-Whitney U test, and Kruskal-Wallis test.

  4. Correlation Analysis: A method to determine the relationship between two or more variables. Methods such as the Pearson correlation coefficient and Spearman correlation coefficient are used.

  5. Regression Analysis: This is a method used to examine the effect of an independent variable on the dependent variable. There are methods such as linear, logistic, multiple, and polynomial regression.

  6. Time Series Analysis: It is a method used to analyze data observed over time. Techniques such as trend analysis, seasonal analysis, stationarity tests, and ARIMA (autoregressive integrated moving average) modeling fall into this category.

  7. Factor Analysis: A multivariate statistical method is used to understand the structure and relationships between many variables. Principal component analysis, covariance matrix, and correlation matrix analysis fall into this category.

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