Mean (Average): The sum of all values divided by the number of values. It gives a central point of the data distribution.

 

Median: The middle value in a dataset when the numbers are arranged in order. It helps represent the center of skewed distributions.

 

Mode: The value that appears most frequently in a dataset. It is useful for categorical data.

 

Standard Deviation: A measure of the amount of variation or dispersion in a dataset. A low standard deviation means the data points tend to be close to the mean, while a high standard deviation means they are spread out.

 

Variance: The square of the standard deviation, it measures how much the data points differ from the mean.

 

Correlation: A statistical measure that describes the degree to which two variables move in relation to each other. It ranges from -1 to +1, where +1 means perfect positive correlation and -1 means perfect negative correlation.

 

Regression Analysis: A statistical method to determine the relationship between a dependent variable and one or more independent variables. Common forms include linear and logistic regression.

 

Multiple Regression: An extension of regression analysis where two or more independent variables are used to predict the dependent variable.

 

ANOVA (Analysis of Variance): A method used to compare the means of three or more samples to see if at least one is significantly different from the others.

 

Chi-Square Test: A statistical test used to determine if a significant relationship exists between two categorical variables.

 

T-Test: A test used to determine if there is a significant difference between the means of two groups, commonly used in comparing test scores, profits, etc.

 

Z-Test: Similar to a t-test but used when the sample size is large, and the population variance is known.

 

P-Value: A probability score that helps to determine the significance of your results in hypothesis testing. A low p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis.

 

Hypothesis Testing: A method for testing a claim or hypothesis about a parameter in a population, using sample data.

 

Confidence Interval: A range of values, derived from the sample data, that is likely to contain the true value of an unknown population parameter.

 

Sampling: The process of selecting a subset of individuals from a population to estimate characteristics of the whole population.

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Outliers: Data points that are significantly different from other observations in the dataset. These can affect the results of an analysis.

 

Time Series Analysis: A method used for analyzing data points collected or recorded at specific intervals over time to forecast future trends.

 

Bayesian Statistics: A method of statistical inference that uses Bayes' theorem to update the probability for a hypothesis as more evidence becomes available.

 

Cluster Analysis: A method used to group a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups.

 

Factor Analysis: A technique used to reduce the number of variables by identifying underlying factors that explain the pattern of correlations within the dataset.

 

Principal Component Analysis (PCA): A dimensionality-reduction technique used to reduce the complexity of datasets by transforming them into a set of uncorrelated variables called principal components.

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Monte Carlo Simulation: A computational algorithm that uses repeated random sampling to simulate and understand the behavior of complex systems or processes.

 

Logistic Regression: A regression model used for binary outcomes, often used in classification problems.

 

Kaplan-Meier Estimator: A non-parametric statistic used to estimate the survival function from lifetime data, often used in medical research.

 

MANOVA (Multivariate Analysis of Variance): An extension of ANOVA that allows for comparing more than one dependent variable across different groups.

 

Survival Analysis: A branch of statistics that analyzes time-to-event data, such as the time until a product fails or the time until a patient relapses.

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R-Squared (R²): A statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.

 

Cross-Validation: A technique for assessing how a predictive model performs by partitioning the data into subsets, training the model on one subset, and validating it on another.

 

Bootstrap Method: A resampling technique used to estimate statistics on a population by sampling a dataset with replacement.

 

This glossary provides an overview of essential statistical techniques used in data analysis for business decision-making, forecasting, and optimization.