Enhance Your Learning with Stats Data Analysis and Interpretation Flash Cards for quick learning
The study of collecting, organizing, analyzing, interpreting, and presenting data.
Methods used to summarize and describe the main features of a dataset, such as measures of central tendency and variability.
The likelihood of an event occurring, expressed as a number between 0 and 1.
The process of selecting a subset of individuals or items from a larger population to gather information and make inferences about the population as a whole.
The probability distribution of a statistic based on a random sample from a population.
A statistical method used to make inferences about a population based on sample data, by testing a hypothesis about the population parameter.
The hypothesis that there is no significant difference or relationship between variables in a population.
The hypothesis that there is a significant difference or relationship between variables in a population.
Rejecting the null hypothesis when it is actually true, also known as a false positive.
Failing to reject the null hypothesis when it is actually false, also known as a false negative.
A statistical method used to model the relationship between a dependent variable and one or more independent variables.
A regression model that assumes a linear relationship between the dependent variable and a single independent variable.
A regression model that assumes a linear relationship between the dependent variable and multiple independent variables.
A statistical method used to compare means between two or more groups to determine if there are any significant differences.
An ANOVA test used to compare means between three or more groups.
An ANOVA test used to compare means between two or more groups, considering two independent variables.
A statistical method used to analyze and forecast data collected over time, such as stock prices or weather patterns.
A long-term increase or decrease in the data over time.
Regular and predictable patterns that repeat at fixed intervals within the data.
Statistical methods that do not rely on assumptions about the underlying probability distribution of the data.
A nonparametric test used to compare the medians of two independent groups.
A nonparametric test used to compare the medians of three or more independent groups.
Statistical methods used to analyze data with multiple variables, such as factor analysis or cluster analysis.
A multivariate analysis method used to identify underlying factors or dimensions in a dataset.
A multivariate analysis method used to group similar individuals or items together based on their characteristics.
A fundamental concept in statistics that states that the sampling distribution of the mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.
A range of values within which the true population parameter is estimated to lie, with a certain level of confidence.
A measure of the strength and direction of the linear relationship between two variables, ranging from -1 to 1.
An observation that significantly deviates from the other observations in a dataset.
A measure of the asymmetry of a probability distribution.
A measure of the peakedness or flatness of a probability distribution.
A statistical test used to determine if there is a significant association between two categorical variables.
The number of independent pieces of information available to estimate a parameter or test a hypothesis.
The probability of obtaining a test statistic as extreme as the observed value, assuming the null hypothesis is true.
An extraneous variable that is related to both the independent and dependent variables, leading to a spurious association.
A measure of the joint variability between two random variables.
A measure of the proportion of the variance in the dependent variable that can be explained by the independent variables in a regression model.
The difference between the observed value and the predicted value in a regression model.
A situation where the effect of one variable on the outcome is mixed with the effect of another variable, making it difficult to determine their individual contributions.
The process of determining whether a cause-effect relationship exists between two variables.
The probability of correctly rejecting the null hypothesis when it is false, also known as the sensitivity of a statistical test.
A method of partitioning the sum of squares in an analysis of variance (ANOVA) model, which accounts for the unique contribution of each independent variable.
An experimental design that involves manipulating two or more independent variables to study their combined effects on the dependent variable.
The effect of one independent variable on the dependent variable that depends on the level of another independent variable.
A dimensionality reduction technique used to transform a dataset into a lower-dimensional space while preserving most of the original information.
A sampling method where the population is divided into clusters, and a random sample of clusters is selected for analysis.
A sampling method where the population is divided into homogeneous subgroups called strata, and a random sample is selected from each stratum.
A sampling method where every nth individual or item is selected from a population after a random starting point.
A study design where participants are randomly assigned to either an experimental group or a control group to evaluate the effectiveness of a treatment or intervention.
The probability that a confidence interval will contain the true population parameter, often expressed as a percentage.
A result that is unlikely to occur by chance alone, typically defined as having a p-value below a certain threshold (e.g., 0.05).
A measure of the magnitude of the difference or relationship between variables, independent of sample size.
The difference between a sample statistic and the true population parameter it represents, due to random variation in the sampling process.
A measure that represents the center or average of a distribution, such as the mean, median, or mode.
The extent to which data points in a distribution differ from each other, often measured by the standard deviation or variance.
A symmetric probability distribution that follows a bell-shaped curve, characterized by its mean and standard deviation.
A probability distribution that is not symmetric and has a longer tail on one side than the other.
A statistical measure that describes the strength and direction of a linear relationship between two continuous variables.
A variable that is related to both the independent and dependent variables and is included in a statistical model to control for its effects.
A situation where two or more independent variables in a regression model are highly correlated, making it difficult to determine their individual effects.
The assumption that the variance of the errors in a regression model is constant across all levels of the independent variables.
A violation of the assumption of homoscedasticity, where the variance of the errors in a regression model varies across different levels of the independent variables.
The examination of the residuals in a regression model to assess the model's assumptions and identify any patterns or outliers.
A range of values around the predicted values in a regression model that represents the uncertainty of the predictions.
A technique used to assess the performance of a predictive model by splitting the data into training and testing sets, and evaluating the model on the testing set.
A situation where a predictive model performs well on the training data but poorly on new, unseen data, due to capturing noise or irrelevant patterns in the training data.
A situation where a predictive model is too simple to capture the underlying patterns in the data, resulting in poor performance on both the training and testing data.
A study design that collects data from a population at a single point in time to examine relationships or differences between variables.
A study design that collects data from a population over an extended period of time to examine changes or trends in variables.
A statistical method used to analyze time-to-event data, such as the time until death or the occurrence of a specific event.
Data in survival analysis where the event of interest has not occurred for some individuals, either because they were lost to follow-up or the study ended before the event could occur.
A regression model used to predict the probability of a binary outcome based on one or more independent variables.