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Linear Stat Models

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Review of Estimation, Hypothesis Testing

Incomplete

02W02

Review of working with R-package

Incomplete

03W03

Least square estimation, estimable linear functions

Incomplete

04W04

Normal equations

Incomplete

05W05

Best Linear Unbiased Estimates (BLUEs).

Incomplete

06W06

Gauss-Markov Theorem.

Incomplete

07W07

Degrees of freedom. Fundamental Theorems of Least Square.

Incomplete

08W08

Testing of linear hypotheses.

Incomplete

09W09

One-way and two-way classification models

Incomplete

010W10

ANOVA and ANCOVA.

Incomplete

011W11

Nested models. Multiple comparisons

Incomplete

012W12

Introduction to random effect models.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

Analysis of Variance (ANOVA)

Open this first for the core logic and traps.

Dive in

1 min

Gauss-Markov Theorem

Open this first for the core logic and traps.

Dive in

1 min

Generalized Linear Models (GLM)

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

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Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // @IITMadrasBSDegreeProgramme

Uplink 12ms

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Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSMA3012

BSc Degree

4 Credits

Linear Statistical Models

To introduce linear statistical models and their applications in estimation and testing. The course will illustrate concepts with specific example...

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Execution Protocol

Module 0

Topic

Module 1

Review of Estimation, Hypothesis Testing

Module 2

Review of working with R-package

Module 3

Least square estimation, estimable linear functions

Module 4

Normal equations

Module 5

Best Linear Unbiased Estimates (BLUEs).

Module 6

Gauss-Markov Theorem.

Module 7

Degrees of freedom. Fundamental Theorems of Least Square.

Module 8

Testing of linear hypotheses.

Module 9

One-way and two-way classification models

Module 10

ANOVA and ANCOVA.

Module 11

Nested models. Multiple comparisons

Module 12

Syllabus

Topic

Review of Estimation, Hypothesis Testing

Review of working with R-package

Least square estimation, estimable linear functions

Normal equations

Best Linear Unbiased Estimates (BLUEs).

Gauss-Markov Theorem.

Degrees of freedom. Fundamental Theorems of Least Square.

Testing of linear hypotheses.

One-way and two-way classification models

ANOVA and ANCOVA.

Nested models. Multiple comparisons

Introduction to random effect models.

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Linear Statistical Models

Execution Protocol

Topic

Review of Estimation, Hypothesis Testing

Review of working with R-package

Least square estimation, estimable linear functions

Normal equations

Best Linear Unbiased Estimates (BLUEs).

Gauss-Markov Theorem.

Degrees of freedom. Fundamental Theorems of Least Square.

Testing of linear hypotheses.

One-way and two-way classification models

ANOVA and ANCOVA.

Nested models. Multiple comparisons

Introduction to random effect models.

Video Archive

Intelligence Hub