Linear and multiple regression, analysis of residuals, transformations, variable and model selection including stepwise regression, and analysis of covariance. The course will stress the use of computer packages to solve real-world problems.

Prerequisites: PSTAT 10 and PSTAT 120B both with a minimum grade of C or better.

4

Units

Optional

Grading

1, 2

Passtime

None

Level Limit

Letters and science

College
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PANDEY P
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PHELP1525
W
13:00 PM - 13:50 PM
23 / 25

PHELP1525
W
14:00 PM - 14:50 PM
25 / 25 Full

PHELP1513
W
16:00 PM - 16:50 PM
24 / 25

PHELP1525
W
17:00 PM - 17:50 PM
19 / 25

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Spring 2024 . Pandey P
ILP 2101
T R
12:30 PM - 13:45 PM
Fall 2024 . Pandey P
ILP 1101
T R
17:00 PM - 18:15 PM
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PSTAT 126 Pandey P Winter 2025 Total: 96
PSTAT 126 Pandey P Fall 2024 Total: 173
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PSTAT 126
124 / 125 Enrolled
Regression Analysis
Saha Ray R
M W
14:00 PM - 15:15 PM
42.3% A
PSTAT 122
70 / 90 Enrolled
Design and Analysis of Experiments
Rituparna Sen 1.4
M W
12:30 PM - 13:45 PM
50.5% A
PSTAT 122
100 / 100 Full
Design and Analysis of Experiments
Saha Ray R
M W
09:30 AM - 10:45 AM
50.5% A
PSTAT 123
136 / 135 Full
Sampling Techniques
Sreenivasa Jammalamadaka 4.4
T R
14:00 PM - 15:15 PM
55.7% A
PSTAT 130
98 / 100 Enrolled
SAS Base Programming
Julie Swenson 4.3
T
09:30 AM - 10:45 AM
35.8% A
PSTAT 131
92 / 100 Enrolled
Introduction to Statistical Machine Learning
Katie Coburn 3.3
T R
14:00 PM - 15:15 PM
58.3% A
PSTAT 131
52 / 80 Enrolled
Introduction to Statistical Machine Learning
Katie Coburn 3.3
T R
17:00 PM - 18:15 PM
58.3% A