In many applications, we want to explain a response series $Y_t$ using covariates while still accounting for autocorrelation. A standard approach is regression with ARMA errors...
Time series modeling involves analyzing data points collected or recorded at specific time intervals to understand underlying structures and make forecasts. Various models, such as Autoregressive (AR), Moving Average (MA), and their combinations (ARMA, ARIMA), are employed to capture different aspec...
The Yule-Walker equations are a set of linear relationships that tie the autocovariances/autocorrelations of a stationary autoregressive (AR $p$) process to its parameters. They are the work-horse for parameter estimation, diagnostic checking, and theoretical analysis of AR models...
The backward shift operator (denoted by $B$) is a powerful tool in time series analysis, used to simplify the notation and manipulation of time series models. The operator shifts the time index of a time series back by one period, making it useful in autoregressive, moving average, and mixed models...
Time series data consists of sequential observations collected over a period of time. This kind of data is prevalent in a range of fields such as finance, economics, climatology, and more. Time series analysis involves the exploration of this data to identify inherent structures such as patterns or ...
Simple linear regression is a statistical method used to model the relationship between a single dependent variable and one independent variable. It aims to find the best-fitting straight line through the data points, which can be used to predict the dependent variable based on the independent varia...
Correlation is a statistical measure that quantifies the strength and direction of the linear relationship between two variables. It is a fundamental concept in statistics, enabling researchers and analysts to understand how one variable may predict or relate to another. The most commonly used corre...
Covariance is a fundamental statistical measure that quantifies the degree to which two random variables change together. It indicates the direction of the linear relationship between variables...
Logistic regression is a statistical method used for modeling the probability of a binary outcome based on one or more predictor variables. It is widely used in various fields such as medicine, social sciences, and machine learning for classification problems where the dependent variable is dichotom...
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Confidence intervals (CIs) provide a range of values which are believed, with a certain degree of confidence, to contain a population parameter, like the mean or proportion. They are constructed from a sampled data set and offer an interval estimate for the parameter of interest...
Statistical inference often involves estimating population parameters and constructing confidence intervals based on sample data. Traditional methods rely on assumptions about the sampling distribution of estimators, such as normality and known standard errors. However, these assumptions may not hol...
When conducting multiple hypothesis tests simultaneously, the likelihood of committing at least one Type I error (falsely rejecting a true null hypothesis) increases. This increase is due to the problem known as the "multiple comparisons problem" or the "look-elsewhere effect". The methods to addres...
Statistical hypothesis testing is a method used in research to make inferences about populations based on sample data. Understanding the concepts of null and alternative hypotheses, as well as how to calculate and interpret p-values, is crucial for conducting robust and meaningful analyses. This sec...
Hypothesis testing is a core concept in statistics that allows researchers to evaluate assumptions about a population by examining sample data. In this process, we start with a null hypothesis, denoted by $H_0$, which represents a baseline or default position, and an alternative hypothesis, $H_a$, w...
Hypothesis testing is a tool in statistics that drives much of scientific research. It lets us draw conclusions about entire populations based on the information we collect from samples. You'll find it applied in many areas—from evaluating how well a new drug works in clinical trials to unraveling t...
Conditional Probability is the likelihood of an event occurring given that another event has already occurred. It is denoted as $P(A|B)$, representing the probability of event $A$ happening, assuming event $B$ has already taken place. This concept is crucial in understanding dependent events in prob...
Statistics is an empirical science, focusing on data-driven insights for real-world applications. This guide offers a concise exploration of statistical fundamentals, aimed at providing practical knowledge for data analysis and interpretation...
The law of total probability allows for the computation of the probability of an event A based on a set of mutually exclusive and exhaustive events. It's particularly useful when the overall sample space is divided into several distinct scenarios, or partitions, that cover all possible outcomes. The...
Probability trees are a visual representation of all possible outcomes of a probabilistic experiment and the paths leading to these outcomes. They are especially helpful in understanding sequences of events, particularly when these events are conditional on previous outcomes...
Expected Value (E), also known as the mean, is the long-run average of a random variable, representing the value we anticipate on average from repeated random draws from a population...
Bayesian and frequentist statistics are two distinct approaches to statistical inference. Both approaches aim to make inferences about an underlying population based on sample data. However, the way they interpret probability and handle uncertainty is fundamentally different...
Bayes' theorem provides a way to update our probability estimates for an event based on new evidence. It connects the conditional and marginal probabilities of events, allowing us to revise our predictions or hypotheses in light of additional information. The theorem is stated mathematically as...
Probability theory offers a structured approach to assessing the probability of events, allowing for logical and systematic reasoning about their likelihood...
Probability theory is based on a set of principles, or axioms, that define the properties of the probability measure. These axioms, first formalized by the Russian mathematician Andrey Kolmogorov, are the foundation upon which the entire framework of probability is built...
Geometric probability is a fascinating branch of probability theory where outcomes are associated with geometric figures and their measures—such as lengths, areas, and volumes—rather than discrete numerical outcomes. It often deals with continuous random variables and employs integral calculus to ca...
Descriptive statistics offer a summary of the main characteristics of a dataset or sample. They facilitate the understanding and interpretation of data by providing measures of central tendency, dispersion, and shape. In this section, we will discuss the essential concepts and measures in descriptiv...
The Taylor series is a fundamental tool in calculus and mathematical analysis, offering a powerful way to represent and approximate functions. By expanding a function around a specific point, known as the "center" or "point of expansion," we can express it as an infinite sum of polynomial terms deri...
Least Squares Regression is a fundamental technique in statistical modeling and data analysis used for fitting a model to observed data. The primary goal is to find a set of parameters that minimize the discrepancies (residuals) between the model’s predictions and the actual observed data. The "leas...
Regression analysis and curve fitting are important tools in statistics, econometrics, engineering, and modern machine-learning pipelines. At their core they seek a deterministic (or probabilistic) mapping $\widehat f: \mathcal X \longrightarrow \mathcal Y$ that minim...
Cubic spline interpolation is a refined mathematical tool frequently used within numerical analysis. It's an approximation technique that employs piecewise cubic polynomials, collectively forming a cubic spline. These cubic polynomials are specifically engineered to pass through a defined set of dat...
Gaussian Interpolation, often associated with Gauss’s forward and backward interpolation formulas, is a technique that refines polynomial interpolation for equally spaced data points. Rather than building the interpolating polynomial from one end of the data interval (as Newton’s forward or backward...
Thin Plate Spline (TPS) interpolation is a non‑parametric, spline‑based technique for fitting a smooth surface through scattered data in two or more spatial dimensions. In its classical 2‑D form one seeks a function $f\colon\mathbb R^{2}\to\mathbb R$ that passes through specified data points while m...
Lagrange Polynomial Interpolation is a widely used technique for determining a polynomial that passes exactly through a given set of data points. Suppose we have a set of $(n+1)$ data points $(x_0, y_0), (x_1, y_1), \ldots, (x_n, y_n)$ where all $x_i$ are distinct. The aim is to find a polynomial $L...
Linear interpolation is one of the most basic and commonly used interpolation methods. The idea is to approximate the value of a function between two known data points by assuming that the function behaves linearly (like a straight line) between these points. Although this assumption may be simplist...