Lab Project MAT523 PDF

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nullnullFACULTY OF COMPUTER AND MATHEMATICAL SCIENCES CENTRE OF MATHEMATICS STUDIESGROUP PROJECT EVALUATIONProject Title: Solving The Problem Using Excelnullnull***Score Indicator: Learning outcome: system of linear equation including least squares solutions Demonstrate communication skill in writin...

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Linear Algebra II (MAT523) – Group Project

FACULTY OF COMPUTER AND MATHEMATICAL SCIENCES CENTRE OF MATHEMATICS STUDIES GROUP PROJECT EVALUATION Project Title: Solving The Problem Using Excel

***Score Indicator: Learning outcome: Demonstrate communication skill in writing mathematical solutions to any system of linear equation including least squares solutions to an inconsistent linear system Very Weak

Weak

Satisfactory

Good

Very Good

1

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5

Assessment criteria for written report Title Data Elaboration Mathematical Work

Score

The project title is suitable The data is relevant, and the source is clearly stated The data is clearly elaborated The formulations are clearly stated The mathematical workflow is systematically written The mathematical solutions are correctly written

Results

The result is systematically presented TOTAL

/35

Linear Algebra II (MAT523) – Group Project

GROUP 2 The grip strength (hundred kg per inches) of a person depends on the person’s age. From a random sample of 12 males, the following data were obtained. Age

Grip Strength

15

2.9

16

2.7

28

2.6

61

1.4

53

4.5

43

2.0

16

3.2

25

3.3

28

2.0

34

2.7

37

2.5

41

1.4

Linear Algebra II (MAT523) – Group Project

Using MATLAB/EXCEL, a) find the best line that fits the data.

The best line that fits the data is 𝑦 = -0.0132𝑥 – 3.0363 𝑅2= 0.0506.

b) find polynomial (quadratic) curve that best represents the data.

Polynomial curve that best represents the data is 𝑦 = 0.0004x2 – 0.043𝑥 – 3.4899. 𝑅2= 0.0611.

Linear Algebra II (MAT523) – Group Project

c) which of the two models is better in interpolating the data set? Explain your answer. 𝑅2 𝑓𝑜𝑟 𝑏𝑒𝑠𝑡 𝑓𝑖𝑡 𝑙𝑖𝑛𝑒 𝑖𝑠 0.0506 𝑅2 𝑓𝑜𝑟 𝑝𝑜𝑙𝑦𝑛𝑜𝑚𝑖𝑎𝑙 𝑐𝑢𝑟𝑣𝑒 𝑖𝑠 0.0611. Therefore, polynomial curve model is better in interpolating the data set since it has larger residual value which is 0.0611 compared to best fit line model where the residual value is 0.0506....