Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Articles by Jay Singh
Page 4 of 9
How to Read PACF Graph for Time Series?
Time series analysis is essential in finance, economics, and marketing. The Partial Autocorrelation Function (PACF) is a powerful tool for identifying direct relationships between observations at different time lags. This article explains how to read and interpret PACF graphs step-by-step. What is PACF? The Partial Autocorrelation Function (PACF) measures the direct correlation between an observation and its lagged values, while controlling for the effects of intermediate lags. Unlike the regular autocorrelation function (ACF) which shows all correlations, PACF isolates the direct relationship by removing indirect effects. PACF is particularly useful for determining the order of Autoregressive (AR) ...
Read MoreHow to implement a gradient descent in Python to find a local minimum?
Gradient descent is a prominent optimization approach in machine learning for minimizing a model's loss function. In simple terms, it involves repeatedly adjusting the model's parameters until the optimal values are found that minimize the loss function. The algorithm works by taking small steps in the direction of the negative gradient of the loss function − the path of steepest descent. The learning rate is a hyperparameter that controls the algorithm's trade-off between speed and accuracy by determining the step size. Many machine learning algorithms like linear regression, logistic regression, and neural networks use gradient descent for training models ...
Read MoreHow to calculate the prediction accuracy of logistic regression?
Logistic regression is a statistical approach for examining the connection between a dependent variable and one or more independent variables. It is a form of regression analysis frequently used for classification tasks when the dependent variable is binary (i.e., takes only two values). Finding the link between the independent factors and the likelihood that the dependent variable will take on a certain value is the aim of logistic regression. Since it enables us to predict the likelihood of an event occurring based on the values of the independent variables, logistic regression is a crucial tool in data analysis and ...
Read MoreA complete guide to resampling methods
Resampling is a statistical technique for generating additional data samples to make inferences about populations or underlying processes. These methods are widely used when estimating population parameters from limited data or when traditional assumptions don't hold. Common resampling approaches include bootstrapping, jackknifing, and permutation testing, which help estimate standard errors, confidence intervals, and p-values without relying on distributional assumptions. What is Bootstrapping? Bootstrapping involves repeatedly sampling from a dataset with replacement to create new samples of the same size as the original. Each bootstrap sample is used to calculate a statistic of interest, and the distribution of these ...
Read MoreHow To Perform Welchís Anova In Python?
Welch's ANOVA is an extension of the standard ANOVA test that allows for different sample sizes and variances. When samples being compared have unequal variances or sample sizes, the standard ANOVA test may not be appropriate. In such situations, Welch's ANOVA should be performed as it provides a more robust alternative. What is Welch's ANOVA? Welch's ANOVA is a variant of the ANOVA test used to compare the means of two or more samples. While standard ANOVA assumes equal variances across all groups (homoscedasticity), Welch's ANOVA relaxes this assumption and can handle unequal variances effectively. Unlike the ...
Read MoreHow To Perform An Ancova In Python?
ANCOVA (Analysis of Covariance) is a statistical method that combines ANOVA with regression analysis. It compares group means while controlling for the effects of continuous variables called covariates, providing more accurate group comparisons by adjusting for confounding variables. What is ANCOVA? ANCOVA extends traditional ANOVA by including one or more continuous covariates in the model. This allows researchers to: Control for variables that might influence the dependent variable Reduce error variance and increase statistical power Make more precise comparisons between groups For example, when testing a new blood pressure medication, you might want to ...
Read MoreHow To Find A P-Value From A Z-Score In Python?
Obtaining a p-value from a z-score is a common statistical procedure. The z-score represents how many standard deviations a value is from the mean of a normal distribution. The p-value indicates the probability of observing a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. This article explains how to calculate p-values from z-scores in Python using the scipy.stats module. What is a P-value? A p-value is the probability that a test statistic will be at least as extreme as the observed one, assuming the null hypothesis is true. The ...
Read MoreHow To Calculate Studentized Residuals In Python?
Studentized residuals are typically used in regression analysis to identify potential outliers in the data. An outlier is a point that is significantly different from the overall trend of the data, and it can have a significant influence on the fitted model. By identifying and analyzing outliers, you can better understand the underlying patterns in your data and improve the accuracy of your model. What are Studentized Residuals? The term "studentized residuals" refers to a particular class of residuals that have had their standard deviations divided by an estimate. Regression analysis residuals are used to describe the discrepancy ...
Read MoreHow To Perform Dunnís Test In Python?
Dunn's test is a statistical technique for comparing the means of several samples. When it's required to compare the means of numerous samples to identify which ones are noticeably different from one another, Dunn's test is frequently employed in a range of disciplines, including biology, psychology, and education. We shall examine Dunn's test in-depth in this article, along with a Python implementation. What is Dunn's Test? Dunn's test is a statistical analysis used to compare the means of numerous samples. It is a form of multiple comparison test used to compare the means of more than two samples ...
Read MoreHow to Perform Bartlettís Test in Python?
Many statistical tests and procedures assume that data is normally distributed and has equal variances. Bartlett's test is a statistical hypothesis test that determines whether two or more samples have equal variances. This test is essential for validating assumptions before applying parametric statistical methods like ANOVA. What is Bartlett's Test? Bartlett's test examines whether samples from different groups have statistically equal variances (homoscedasticity). It tests the null hypothesis that all population variances are equal against the alternative hypothesis that at least one variance differs significantly. The test is commonly used as a preliminary check before performing ANOVA. ...
Read More