- "Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
Understanding basic statistical concepts and methods for analyzing large datasets.
Descriptive statistics: Descriptive statistics is a branch of statistics that deals with the collection, analysis, interpretation, and presentation of quantitative data. It involves the use of summary measures, such as mean, median, mode, and standard deviation, to describe a set of data.
Inferential statistics: Inferential statistics is a branch of statistics that deals with the analysis and interpretation of data with the aim of making inferences or drawing conclusions about a sample from a larger population.
Sampling techniques: Sampling techniques are methods used to select a subset of individuals or items from a population for analysis or study. Common sampling techniques include simple random sampling, stratified sampling, and cluster sampling.
Probability distributions: Probability distributions are mathematical functions that describe the likelihood of different outcomes for a random event or variable. Commonly used probability distributions include the normal distribution, binomial distribution, and Poisson distribution.
Hypothesis testing: Hypothesis testing is a method used to test the validity of a statistical claim or hypothesis using statistical evidence. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and evaluating the evidence to determine whether to reject or accept the null hypothesis.
Correlation and regression analysis: Correlation and regression analysis are methods used to study the relationship between two or more variables. Correlation analysis measures the strength and direction of the relationship between two variables, while regression analysis is used to model the relationship between a dependent variable and one or more independent variables.
Data visualization: Data visualization is the graphical representation of data and information. It is used to communicate complex data insights and patterns in an intuitive and easy-to-understand format. Common data visualization techniques include bar charts, pie charts, scatterplots, and heat maps.
Data cleansing and preparation: Data cleansing and preparation is the process of cleaning, transforming, and structuring data for analysis. It involves identifying and correcting errors, removing duplicates, and formatting data in a consistent manner to ensure accuracy and consistency.
Data collection and storage: Data collection and storage is the process of collecting, storing, and managing data for analysis. It involves selecting relevant data sources, collecting data, and storing data in a structured format to facilitate analysis.
Ethics and privacy in data analysis: Ethics and privacy in data analysis are essential considerations when working with data. It involves ensuring that data is collected and used in an ethical and responsible manner, and that sensitive or private data is protected and anonymized to ensure confidentiality and privacy.
Descriptive Statistics: This type of analysis involves the summarization and interpretation of collected data. Descriptive statistics can include mean, median, mode, standard deviation, and other measures that describe the central tendency and variability of the data.
Inferential Statistics: This analysis is used to draw conclusions about a large population based on a sample. Statistical significance tests, hypothesis testing and confidence intervals are examples of inferential statistics.
Correlation Analysis: A statistical method that identifies the strength and direction of the relationship between two variables.
Regression Analysis: This is an analytical method to determine the strength and direction of the relationship between a dependent variable and one or more independent variables.
Time Series Analysis: This analysis involves studying data points collected over a period of time to identify patterns and trends.
Bayesian Analysis: This is statistical inference that involves updating prior beliefs based on new data.
Multivariate Analysis: This involves the analysis of data sets with multiple variables to discover patterns, correlations and trends among them.
Cluster Analysis: This involves grouping similar data points together based on their similarity.
Factor Analysis: This is a statistical method used to understand the underlying structure of a set of variables.
Survival Analysis: This involves analyzing the occurrence of events over time and the probability of the occurrence of such events.
Meta-analysis: This is a statistical analysis of separate but similar experiments or studies aimed at finding results for multiple research papers.
Network Analysis: This involves examining and analyzing the networks such as social media networks or employee networks to understand information flow patterns and relationships between different nodes.
- "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)."