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IE 345

Regression Applications and Forecasting in Engineering

Mechanical & Industrial Engr

3 credit hoursUsually offered: Fall, Spring
Avg GPA
3.03
Easiness
2.70
Total regs
135

Grade Distribution

Fall 2022–Fall 2025 · Percentages use A–F and W outcomes

Avg GPA
3.03
A
42.7%
56
B
34.4%
45
C
17.6%
23
D
3.1%
4
F
0.8%
1
W
1.5%
2
A–F + W
131
Other outcomes
4
All registrations
135

Quick Insights

Avg GPA
3.03
Easiness
2.70
C or better
96.1%
Among A–F outcomes
D or better
99.2%
Among A–F outcomes
Withdrawal rate
1.5%
Among A–F and W outcomes
Most common grade
A
Letter grades only
Outcomes counted
131
A–F and W outcomes

IE 345 by Professor

Ranked by historical GPA for 2024SP–2026SP · A–F outcomes only · minimum 20 graded outcomes

#1
Tejada Lopez, Carla A
3.10
RMP
NR
Graded
39
Total regs
41
#2
Huang, Jida
3.00
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Tejada Lopez, Carla A

Top GPA signal with 39 grades.

Avg GPA 3.10Open profile →
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About This Class

Single and multiple regression analysis of variance, examination of residuals, introduction to time series analysis, and analytical forecasting techniques; application to engineering system. Course Information: Prerequisite(s): IE 342 .

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Powered by real course, professor, and campus data.

Methodology
RMP
NR
Graded
33
Total regs
33
#
Professor
Avg GPA
Student rating
Graded
Total regs
  • 1
    Tejada Lopez, Carla A
    3.10
    NR
    39
    41
  • 2
    Huang, Jida
    3.00
    NR
    33
    33
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