The foundational course Statistics and Probability (STA301) exposes students to the ideas, procedures, and applications of probability theory and statistics. Designed with intermediate learners in mind, this course offers a thorough grasp of statistical principles and methods, along with an appreciation of their applicability in a variety of domains, including science, engineering, business, and the social sciences.
STA301 explores the fundamentals of both descriptive and inferential statistics. Students set out to investigate subjects including gathering, organizing, summarizing, and interpreting data. Students learn how statistics is used to analyze and make sense of data, which helps them solve problems and make educated decisions. This is accomplished through both theoretical education and hands-on exercises.
The presentation and analysis of data using measures like measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and graphical representations (histograms, box plots, scatter plots) are among the main topics of STA301. In order to spot trends, patterns, and outliers, students study how to summarize and visualize data.
Another essential element of STA301 is probability theory. The basic ideas of probability, such as sample spaces, events, probability axioms, and probability distributions, are taught to students. In addition to learning how to compute probabilities and expected values for a variety of random variables, they investigate other probability distribution types, such as discrete and continuous distributions.
Inferential statistics, which involves drawing conclusions and forecasts about populations from sample data, is covered in STA301. Precise estimate methods, including parametric and non-parametric tests, as well as interval and point estimation, are taught to students. They know how to evaluate the validity of inferences made from sample data and evaluate the dependability of statistical results.
Regression analysis and correlation, which entail modeling and examining the connection between variables, are also covered in the course. In addition to learning how to analyze correlation and regression coefficients, students also study correlation analysis, multiple regression, and simple linear regression. They investigate how regression analysis can be used to forecast results and comprehend how variables relate to one another.
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