Outline
Introduction
Topics Covered in HWC 601 – Statistics II
Probability Theory
Sampling Theory
Hypothesis Testing
Analysis of Variance (ANOVA)
Correlation and Regression Analysis
Non-parametric Statistics
Time Series Analysis
Applications of Statistics in Various Fields
Conclusion
FAQs
Statistics is a crucial subject that plays an important role in various fields, including medicine, engineering, economics, and social sciences. HWC 601 – Statistics II is an advanced course that builds on the concepts learned in HWC 501 – Statistics I. This course covers topics such as probability theory, sampling theory, hypothesis testing, analysis of variance (ANOVA), correlation and regression analysis, non-parametric statistics, and time series analysis. By the end of this course, students will have a deep understanding of statistical concepts and their applications in real-world scenarios.
Probability Theory
Probability theory is a branch of mathematics that deals with the study of random events. It is used to determine the likelihood of an event occurring in a given situation. Probability theory is essential in statistical analysis, as it provides a mathematical foundation for many statistical concepts.
In this course, students will learn about different types of probabilities, including conditional probability and Bayes’ theorem. They will also learn about the importance of probability theory in statistical inference.
Sampling Theory
Sampling theory is a branch of statistics that deals with the selection of a subset of individuals from a larger population. Sampling is essential in statistical analysis because it is often impractical or impossible to measure an entire population.
In this course, students will learn about different sampling methods and their advantages and disadvantages. They will also learn about sampling distributions and the central limit theorem.
Hypothesis Testing
Hypothesis testing is a statistical method used to test
whether a hypothesis about a population is true or not. It involves comparing a sample statistic to a population parameter to determine whether the sample is likely to have come from the population of interest.
In this course, students will learn about null and alternative hypotheses, type I and type II errors, and the significance level. They will also learn about one-sample and two-sample t-tests, and the chi-squared test.
Analysis of Variance (ANOVA)
Analysis of variance (ANOVA) is a statistical method used to compare the means of two or more groups. It is used to determine whether there is a significant difference between the groups. ANOVA is an extension of the t-test, which is used to compare the means of two groups.
In this course, students will learn about one-way and two-way ANOVA, as well as post-hoc tests.
Correlation and Regression Analysis
Correlation and regression analysis are statistical methods used to examine the relationship between two or more variables. Correlation analysis is used to determine whether there is a relationship between two variables, while regression analysis is used to predict the value of one variable based on the value of another variable.
In this course, students will learn about different types of correlation, including Pearson’s correlation and Spearman’s correlation. They will also learn about linear regression analysis, multiple regression analysis, and model diagnostics.
Non-parametric Statistics
Non-parametric statistics are statistical methods that do not assume a specific probability distribution for the population. They are used when the data do not meet the assumptions of parametric statistics, such as normality.
In this course, students will learn about different non-parametric statistical tests, including the Wilcoxon rank-sum test, the Kruskal-Wallis test, and the Friedman test.
Time Series Analysis
Time series analysis is a statistical method used to analyze time-dependent data. It is used to model the behavior of a variable over time and to make predictions about future values.
In this course, students will learn about the different components of time series, including trend and seasonal components. They will also learn about ARIMA models, which are commonly used for time series analysis.
Statistics has applications in a wide range of fields, including medicine, business, social sciences, and engineering. In medicine and healthcare, statistics is used to analyze clinical trial data and to make decisions about patient care. In business and economics, statistics is used to analyze market trends and to make predictions about future sales. In social sciences, statistics is used to study the behavior of individuals and groups. In engineering and technology, statistics is used to analyze data from experiments and to make decisions about product design.
Conclusion
In conclusion, HWC 601 – Statistics II is an advanced course that covers a wide range of statistical concepts and their applications in various fields. By the end of this course, students will have a deep understanding of statistical theory and will be able to apply it to real-world problems. Understanding statistics is essential for future career opportunities in a wide range of fields.