As part of a study at a large university, data were collected on n= 224 freshmen computer science (CS) majors in a particular year. The researchers were interested in modeling y, a student's grade point average (GPA) after three semesters, as a function of the following independent variables (recorded at the time the students enrolled in the university): X1 = average high school grade in mathematics (HSM) × 2 = average high school grade in science (HSS) X 3 = average high school grade in English (HSE) XA = SAT mathematics score (SATM) X5 = SAT verbal score (SATV) A first-order model was fit to the data with the following results: SOURCE DF MS F VALUE PROB>F S MODEL 28.64 5.73 11.69 .0001 ERROR 218 106.82 0.49 TOTAL 223 135.46 ROOT MSE DEP MEAN 0.700 4.635 R-SQUARE ADJ R-SQ 0.211 0.193 I FOR O ERROR PARAMETER =0 PROB>ITI PARAMETER STANDARD VARIABLE ESTIMATE INTERCEPT 0.0001 0.0003 0.3432 2.327 0.039 5.817 X1 (HSM) X2 (HSS) X3 (HSE) 0.146 0.037 3.718 0.036 0.038 0.950 0. 1637 0. 1702 0.4915 Test to determine if the model is adequate for predicting GPA. Use a = .01. 0.055 0.00094 -0.00041 0.040 1.397 X4 (SATM) X5 (SATV) 0.00068 0.0059 1.376 -0.689

As part of a study at a large university, data were collected on n= 224 freshmen computer science (CS) majors in a particular year. The researchers were interested in modeling y, a student's grade point average (GPA) after three semesters, as a function of the following independent variables (recorded at the time the students enrolled in the university): X1 = average high school grade in mathematics (HSM) × 2 = average high school grade in science (HSS) X 3 = average high school grade in English (HSE) XA = SAT mathematics score (SATM) X5 = SAT verbal score (SATV) A first-order model was fit to the data with the following results: SOURCE DF MS F VALUE PROB>F S MODEL 28.64 5.73 11.69 .0001 ERROR 218 106.82 0.49 TOTAL 223 135.46 ROOT MSE DEP MEAN 0.700 4.635 R-SQUARE ADJ R-SQ 0.211 0.193 I FOR O ERROR PARAMETER =0 PROB>ITI PARAMETER STANDARD VARIABLE ESTIMATE INTERCEPT 0.0001 0.0003 0.3432 2.327 0.039 5.817 X1 (HSM) X2 (HSS) X3 (HSE) 0.146 0.037 3.718 0.036 0.038 0.950 0. 1637 0. 1702 0.4915 Test to determine if the model is adequate for predicting GPA. Use a = .01. 0.055 0.00094 -0.00041 0.040 1.397 X4 (SATM) X5 (SATV) 0.00068 0.0059 1.376 -0.689

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

Differential Equations

Have you ever observed the movement of the satellites and rockets or how the planets go around the sun in their orbits? How will you determine their motion? Definitely they follow some scientific laws and these kinds of problems are framed as differential equations.

Question

As part of a study at a large university, data were collected on n = 224 freshmen computer science (CS) majors in a particular year. The researchers were interested in modeling y, a student's grade point average
(GPA) after three semesters, as a function of the following independent variables (recorded at the time the students enrolled in the university):
X1 = average high school grade in mathematics (HSM)
X 2 = average high school grade in science (HSS)
X 3 = average high school grade in English (HSE)
X4 = SAT mathematics score (SATM)
X 5 = SAT verbal score (SATV)
A first-order model was fit to the data with the following results:
SOURCE
DF
SS
MS
F VALUE
PROB>F
MODEL
5
28.64
5.73
11.69
.0001
ERROR
218
106.82
0.49
TOTAL
223
135.46
0.700
4.635
ROOT MSE
R-SQUARE
0.211
0.193
DEP MEAN
ADJ R-SQ
PARAMETER STANDARD
T FOR 0:
VARIABLE
ESTIMATE
ERROR PARAMETER =0 PROB>ITI
INTERCEPT
X1 (HSM)
X2 (HSS)
X3 (HSE)
X4 (SATM)
2.327
0.039
5.817
0.0001
0.146
0.037
3.718
0.0003
0.036
0,038
0.950
0.3432
0.055
0.040
1.397
0. 1637
0.00094
0.00068
1.376
0.1702
X5 (SATV)
-0.00041
0.0059
-0.689
0.4915
Test to determine if the model is adequate for predicting GPA. Use a = .01.

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