In this course, we see how probability theory is used in statistics to formalize and facilitate the process of learning from data, which is called statistical inference. The three main aspects of statistical inference we will cover are point estimation, confidence intervals, and hypothesis tests. For each of these topics, we will provide the goals and motivation, basic definitions, general methodology, and commonly used tools, including inference for means, differences in means, proportions, and differences in proportions. The course is structured in five basic sections.

In this course, we see how probability theory is used in statistics to formalize and facilitate the process of learning from data, which is called statistical inference. The three main aspects of statistical inference we will cover are point estimation, confidence intervals, and hypothesis tests. For each of these topics, we will provide the goals and motivation, basic definitions, general methodology, and commonly used tools, including inference for means, differences in means, proportions, and differences in proportions. The course is structured in five basic sections.

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.

Not available at this time

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. (Gen. Ed. R2)

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. (Gen. Ed. R2)

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. (Gen. Ed. R2)

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. (Gen. Ed. R2)

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. (Gen. Ed. R2)

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. (Gen. Ed. R2)