SKU/Artículo: AMZ-B0DG35YRTB
Statistics Skills Practice Workbook (Math Magicians)
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.97 kg
0.97 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Delve into the world of statistics with this exhaustive workbook that covers the entire spectrum of statistical methods and applications. Each chapter is meticulously crafted to provide clear explanations, followed by practical exercises that reinforce your comprehension. The inclusion of Python code snippets further aids in implementing the statistical techniques discussed, offering you hands-on experience. Key Features: - In-Depth Coverage: Comprehensive explanations of fundamental and advanced statistical concepts. - Practical Exercises: Engage with extensive exercises that challenge and enhance your statistical skills. - Problem-Solving Focus: Build confidence in solving complex statistical problems with guided solutions. - Progressive Learning Curve: Structured to evolve from basic to advanced topics, catering to all levels of expertise. What You Will Learn: - Fundamentals of descriptive statistics, including central tendency and variability - Basic principles of probability theory - Application of Bayes' theorem in decision-making - Analysis of discrete and continuous random variables - Implications of the law of large numbers - Understanding and application of the central limit theorem - Properties and significance of the normal distribution - Modelling binary outcomes with binomial distribution - Rare event modeling using Poisson distribution - Hypothesis testing for population inference - Implementation of Z-tests and T-tests for sample comparison - ANOVA for assessing variance among groups - Utilization of chi-squared tests in categorical data analysis - Determining correlation and distinguishing correlation from causation - Exploring simple and multiple linear regression - Utilizing logistic and polynomial regression for prediction - Criteria for model selection, including AIC and BIC - Time series analysis techniques and applications - Autoregressive models for temporal data analysis - Smoothing data using moving averages - Application of ARIMA models for time series forecasting - Dimensionality reduction with principal components analysis - Exploration of factor analysis for underlying relationships - Cluster analysis and techniques like K-means and hierarchical clustering - Naive Bayes classifier for categorical data classification - Fundamentals of support vector machines - Decision tree algorithms for complex data analysis - Understanding ensemble methods with random forests and boosting algorithms - Association rule learning and the apriori algorithm - Stochastic processes and applications of Markov chains - Simulation techniques with Monte Carlo simulation - Application of bootstrap methods in statistics - Nonparametric statistical methods for flexible data analysis
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