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Mar 10
1. Descriptive statistics: Descriptive statistics involve summarizing and describing the characteristics of a dataset. This includes measures such as mean, median, mode, standard deviation, and range.
2. Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data. This includes hypothesis testing, confidence intervals, and regression analysis.
3. Parametric statistics: Parametric statistics assume that the data follows a specific distribution, such as a normal distribution. Examples of parametric statistics include t-tests, ANOVA, and linear regression.
4. Non-parametric statistics: Non-parametric statistics do not make assumptions about the distribution of the data. Examples of non-parametric statistics include the Mann-Whitney U test, Wilcoxon signed-rank test, and Kruskal-Wallis test.
5. Descriptive inferential statistics: Descriptive inferential statistics combine elements of both descriptive and inferential statistics to provide a comprehensive analysis of a dataset. This may involve calculating descriptive statistics and using them to make inferences about the population.
6. Bayesian statistics: Bayesian statistics involve using prior knowledge or beliefs to update and revise probabilities based on new evidence. This approach is particularly useful in situations where there is limited data available.
7. Time series analysis: Time series analysis involves analyzing data collected over time to identify patterns, trends, and relationships. This may involve techniques such as autoregressive integrated moving average (ARIMA) modeling and exponential smoothing.
8. Spatial statistics: Spatial statistics involve analyzing data that is geographically referenced. This may involve techniques such as spatial autocorrelation, spatial interpolation, and cluster analysis.
2. Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data. This includes hypothesis testing, confidence intervals, and regression analysis.
3. Parametric statistics: Parametric statistics assume that the data follows a specific distribution, such as a normal distribution. Examples of parametric statistics include t-tests, ANOVA, and linear regression.
4. Non-parametric statistics: Non-parametric statistics do not make assumptions about the distribution of the data. Examples of non-parametric statistics include the Mann-Whitney U test, Wilcoxon signed-rank test, and Kruskal-Wallis test.
5. Descriptive inferential statistics: Descriptive inferential statistics combine elements of both descriptive and inferential statistics to provide a comprehensive analysis of a dataset. This may involve calculating descriptive statistics and using them to make inferences about the population.
6. Bayesian statistics: Bayesian statistics involve using prior knowledge or beliefs to update and revise probabilities based on new evidence. This approach is particularly useful in situations where there is limited data available.
7. Time series analysis: Time series analysis involves analyzing data collected over time to identify patterns, trends, and relationships. This may involve techniques such as autoregressive integrated moving average (ARIMA) modeling and exponential smoothing.
8. Spatial statistics: Spatial statistics involve analyzing data that is geographically referenced. This may involve techniques such as spatial autocorrelation, spatial interpolation, and cluster analysis.
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