# Search Course Outline

### 5 course outlines found

1

#### STATS 762

: Regression for Data Science
2024 Semester One (1243)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Subject: Statistics
Prerequisite: 15 points from STATS 210, 225, 707, and 15 points from ENGSCI 314, STATS 201, 207, 208
Restriction: STATS 330
2

#### STATS 762

: Regression for Data Science
2023 Semester One (1233)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
3

#### STATS 762

: Regression for Data Science
2022 Semester One (1223)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
4

#### STATS 762

: Regression for Data Science
2021 Semester One (1213)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
5

#### STATS 762

: Regression for Data Science
2020 Semester One (1203)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330