- "Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
This is the application of statistical models and algorithms to analyze and interpret datasets.
Descriptive statistics: Statistical measures that describe the central tendency, variability, and distribution of a dataset. It includes measures such as mean, median, mode, standard deviation, and range.
Inferential statistics: Statistical methods used to make inferences about a population based on a sample. It includes hypothesis testing, confidence intervals, and regression analysis.
Probability: The measure of the likelihood of an event occurring. It plays a vital role in statistical analysis as many concepts and techniques rely on probability theory.
Sampling techniques: The process of selecting a subset of observations from a population. Various sampling methods like random sampling, stratified sampling, cluster sampling, and convenience sampling are used.
Data visualization: Graphical representation of data to extract insights and patterns. It includes histograms, box plots, scatter plots, and bar graphs.
Data cleaning and preparation: The process of identifying and correcting errors, missing values, and outliers in the dataset. It also includes data transformation like normalizing, scaling, and encoding categorical variables.
Correlation and regression: The relationship between two or more variables. Correlation measures the degree of association between the variables, whereas regression models the relationship between the dependent and independent variables.
Time series analysis: Statistical methods used to analyze time-dependent data. It includes forecasting, trend analysis, and seasonality analysis.
Hypothesis testing: A statistical method used to determine whether there is sufficient evidence to support or reject a claim about a population.
Multivariate analysis: The analysis of multiple variables simultaneously. It includes techniques like factor analysis, Principal Component Analysis, and Cluster Analysis.
Experimental design: The process of designing experiments to test the effectiveness of different treatments. It includes randomization, blocking, and factorial designs.
Statistical software: The tools and software used to perform statistical analysis. Some of the popular software includes SPSS, SAS, R, and Excel.
Data mining and machine learning: The process of extracting useful patterns and insights from large datasets using advanced algorithms. It includes techniques like classification, regression, decision trees, and neural networks.
Descriptive analysis: This type of analysis is used to describe the basic features of a dataset, such as the mean, median, and mode.
Inferential analysis: This type of analysis is used to draw conclusions about a population based on a sample of data.
Hypothesis testing: This analysis is used to test a hypothesis about a population. It is commonly used to determine whether a treatment has a significant effect on a target variable compared to a control group.
Regression analysis: This type of analysis is used to model the relationship between a dependent variable and one or more independent variables.
Time series analysis: This analysis is used to analyze data that changes over time. It is used to forecast future trends and patterns in the data.
Bayesian analysis: A statistical method that involves assigning probabilities to events based on experience and evidence.
Clustering analysis: A statistical method that uses algorithms to group similar data points into clusters.
Factor analysis: This analysis is used to reduce the number of variables in a dataset while retaining the underlying relationships between them.
Survival analysis: This analysis is used to determine the likelihood of an event occurring at a specific time, such as the probability of a patient surviving a disease.
ANOVA analysis: This analysis is used to compare means across multiple groups to determine whether they are significantly different from one another.
Multivariate analysis: A statistical analysis method that involves the measurement and analysis of multiple variables at once.
Discriminant analysis: A statistical analysis method that involves distinguishing between two or more groups based on their characteristics.
Network analysis: A type of statistical analysis that is used to analyze how nodes within a network are connected and how information is transmitted between them.
Principal component analysis: A statistical analysis method that decomposes a dataset into its main components in order to identify patterns within the data.
Chi-squared analysis: A statistical analysis method used to determine whether there is a significant association between two categorical variables.
- "Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
- "It is assumed that the observed data set is sampled from a larger population."
- "Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "In this context, inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."
- "Inferential statistical analysis infers properties of a population, while descriptive statistical analysis is solely concerned with properties of the observed data."
- "Inferential statistical analysis infers properties of a population, for example by... deriving estimates."
- "It is assumed that the observed data set is sampled from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "Inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."
- "Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
- "Descriptive statistics is solely concerned with properties of the observed data."
- "Descriptive statistics... does not rest on the assumption that the data come from a larger population."
- "It is assumed that the observed data set is sampled from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "Inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."