SPSS Tutorial for Data Analysis - SPSS for Beginners
Comprehensive SPSS tutorial covering various statistical analyses, from basic tests to advanced regression techniques. Ideal for beginners seeking practical data analysis skills using IBM SPSS Statistics.
Biostatistics and Design of Experiments
The course encompasses topics such as distribution of data, sample size, tests of significance, data reduction, regression analysis, comparison of performance of drugs in clinical trials, design of experiments, screening and second order designs.
Working with SPSS
Learn to use SPSS for data analysis: from data entry and import to advanced statistical techniques like ANOVA, regression, and correlation. Master creating charts and formatting outputs.
Quantitative Methods
Explore regression analysis and difference-in-differences methodology using R, with a focus on the Minimum Legal Drinking Age example.
Biostatistics Using R Course
Learn biostatistics with R: from basics to advanced topics like probability, inferential statistics, mixed modeling, and regression analysis. Gain practical skills for data analysis in biological sciences.
Statistics
Comprehensive exploration of statistical concepts, from basics to advanced topics, covering descriptive and inferential statistics, probability, distributions, hypothesis testing, and various statistical tests.
Biostatistics and Research Methodology
Comprehensive exploration of biostatistics and research methodology, covering statistical concepts, sampling techniques, hypothesis testing, and research design for pharmacy students.
Data Mining for Beginners - Learn Data Mining
This video teaches Data Mining and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.
The Network Structure of Police Misconduct
Explore police misconduct networks in Chicago, examining individual and institutional factors influencing co-misconduct patterns. Gain insights into potential police reform strategies.
Machine Learning
Comprehensive Hindi course covering machine learning fundamentals, algorithms, and practical implementations using Python, with real-world examples and solved problems.
Multiple Antenna Communications
Explore advanced wireless communication techniques including MIMO, beamforming, and channel modeling. Gain insights into spatial diversity, multiplexing, and hybrid approaches for improved signal processing and data transmission.
Using Language Models to Understand Wage Premia
Explore how NLP techniques predict salaries from job postings, revealing wage premia for various characteristics in the digital economy labor market.
Deep Generative Models - Lecture 2: Background
Explore fundamental concepts and background for deep generative models, covering essential topics to build a strong foundation in this advanced machine learning field.
Deep Generative Models - Energy Based Models - Lecture 14
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
Deep Generative Models - Energy Based Models - Lecture 11
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
The Universal Principle of Risk Management - Pooling and Hedging Risks
Explore probability theory, statistics, and financial analysis methods essential for risk management and understanding the time value of money in financial markets.
Linear Regression and Linear Models
Learn powerful statistical tools: linear regression and models. Predict continuous values using various data types. Covers least squares, multiple regression, t-tests, ANOVA, and R implementation.
Data Analytics with Python
Learn Python-based data analytics, covering statistics, probability, hypothesis testing, regression, ANOVA, clustering, and decision trees. Gain practical skills for data-driven decision making and statistical analysis.
Statistics and Machine Learning in R
If you want to do statistics and machine learning in R, this playlist is for you, and you only. No one else is allowed to watch it.
Introduction to Probability and Statistics
This course is aimed at being a pre-term or a preparatory course for probability and statistics. It is very basic and introduces the students to the understanding of data and measuring associations. It also introduces the concepts of probability and dist…
Linear Regression Analysis and Forecasting
The accuracy of forecasting depends upon the goodness of obtained model. What are the steps and checks required to obtain a good model and in turn, how to do forecasting is being aimed to be taught in this course.