Regression Analysis: y versus x1, X2, ×3, X4 Summary Output Regression Statistics R Square 0.802 Adjusted R Square 0.787 Standard Error 9.025 Observations 60 ANOVA df SS MS F Regression 4 18,088.5 4,522.1 55.52 0.000 Residual 55 4,479.7 81.4 Total 59 22,568.2 Standard Coefficients Error Stats P-Value Constant -55.93 24.22 -2.31 0.025 X1 0.01049 0.021 0.5 0.619 X2 -0.1072 0.03503 -3.06 0.003 X3 0.57922 0.07633 7.59 0.000 X4 -0.8695 0.1498 -5.81 0.000 Round all numbers to 2 decimal places, e.g. 1.75.) he number of predictors is: he regression model is : X1+ X2+
Regression Analysis: y versus x1, X2, ×3, X4 Summary Output Regression Statistics R Square 0.802 Adjusted R Square 0.787 Standard Error 9.025 Observations 60 ANOVA df SS MS F Regression 4 18,088.5 4,522.1 55.52 0.000 Residual 55 4,479.7 81.4 Total 59 22,568.2 Standard Coefficients Error Stats P-Value Constant -55.93 24.22 -2.31 0.025 X1 0.01049 0.021 0.5 0.619 X2 -0.1072 0.03503 -3.06 0.003 X3 0.57922 0.07633 7.59 0.000 X4 -0.8695 0.1498 -5.81 0.000 Round all numbers to 2 decimal places, e.g. 1.75.) he number of predictors is: he regression model is : X1+ X2+
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
q18
2nd picture is scrolled version so you can see the rest of the questions
Transcribed Image Text:Study the regression output that follows. How many predictors are there? What is the equation of the regression
model?
Regression Analysis: y versus X1, X2, X3, X4
Summary Output
Regression Statistics
R Square
0.802
Adjusted R Square
0.787
Standard Error
9.025
Observations
60
ANOVA
df
SS
MS
F
Regression
4
18,088.5
4,522.1
55.52
0.000
Residual
55
4,479.7
81.4
Total
59
22,568.2
Standard
t
Coefficients
Error
Stats
P-Value
Constant
-55.93
24.22
-2.31
0.025
X1
0.01049
0.021
0.5
0.619
X2
-0.1072
0.03503
-3.06
0.003
X3
0.57922
0.07633
7.59
0.000
X4
-0.8695
0.1498
-5.81
0.000
(Round all numbers to 2 decimal places, e.g. 1.75.)
The number of predictors is:
k =
The regression model is :
=
+
X1+
X2+
Transcribed Image Text:Study the regression output that follows. How many predictors are there? What is the equation of the regression
model?
Regression Analysis: y versus X1, X2, X3, X4
Summary Output
Regression Statistics
R Square
0.802
Adjusted R Square
0.787
Standard Error
9.025
Observations
60
ANOVA
df
SS
MS
F
Regression
4
18,088.5
4,522.1
55.52
0.000
Residual
55
4,479.7
81.4
Total
59
22,568.2
Standard
Coefficients
Error
Stats
P-Value
Constant
-55.93
24.22
-2.31
0.025
X1
0.01049
0.021
0.5
0.619
X2
-0.1072
0.03503
-3.06
0.003
X3
0.57922
0.07633
7.59
0.000
X4
-0.8695
0.1498
-5.81
0.000
(Round all numbers to 2 decimal places, e.g. 1.75.)
The number of predictors is:
k =
The regression model is :
X1+
X2+
X3+
X4
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