Regression analysis is conducted to associate variable X2-1 with the response variable Y-1. Portion of the Minitab results are shown below.Is the regression model adequate for the regression analysis? Justify. Identify the variance around the fitted values. Analysis of VarianceSourceRegressionX2-1ErrorLack-of-FitPure ErrorTotalModel Summary2.11316 40.48%CoefficientsTermConstantX2-1PercentFrequency9990S50101-5.0-4R-sq R-sq (adj) R-sq (pred)38.19%29.09%LILDF Adj SS Adj MS1178.95278.952116.10221110.07956.02227 195.053Coef SE Coef4.521.611.3450.320-2.5-2Normal Probability Plot0.0ResidualHistogram022.5ResidualT-Value2.824.20F-Value5.06Residual Plots for Y-1P-Value0.0090.000 1.00ResidualResidual5.02.50.0-2.5-5.05.0250.0P-Value0.0000.000-2.50.055VIF-5.0102 4 6Versus Fits12Fitted ValueVersus Orderالمين مال ماست و14www810 12 14 16 18 20 22 24 26 28Observation Order

This question is about conducting a regression analysis to assess the relationship between a…

Regression analysis is conducted to associate variable X2-1 with the response variable Y-1. Portion of the Minitab results are shown below. Is the regression model adequate for the regression analysis? Justify. Identify the variance around the fitted values.

Regression analysis is conducted to associate variable X2-1 with the response variable Y-1. Portion of the Minitab results are shown below. Is the regression model adequate for the regression analysis? Justify. Identify the variance around the fitted values.

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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Question

Regression analysis is conducted to associate variable X2-1 with the response variable Y-1. Portion of the Minitab results are shown below.
1. Is the regression model adequate for the regression analysis? Justify.
2. Identify the variance around the fitted values.

Analysis of Variance
Source
Regression
X2-1
Error
Lack-of-Fit
Pure Error
Total
Model Summary
2.11316 40.48%
Coefficients
Term
Constant
X2-1
Percent
Frequency
99
90
S
50
10
1
-5.0
-4
R-sq R-sq (adj) R-sq (pred)
38.19%
29.09%
LIL
DF Adj SS Adj MS
1
1
78.952
78.952
116.102
21
110.079
5
6.022
27 195.053
Coef SE Coef
4.52
1.61
1.345
0.320
-2.5
-2
Normal Probability Plot
0.0
Residual
Histogram
0
2
2.5
Residual
T-Value
2.82
4.20
F-Value
5.0
6
Residual Plots for Y-1
P-Value
0.009
0.000 1.00
Residual
Residual
5.0
2.5
0.0
-2.5
-5.0
5.0
25
0.0
P-Value
0.000
0.000
-2.5
0.055
VIF
-5.0
10
2 4 6
Versus Fits
12
Fitted Value
Versus Order
المين مال ماست و
14
www
8
10 12 14 16 18 20 22 24 26 28
Observation Order

Definition Definition Statistical method that estimates the relationship between a dependent variable and one or more independent variables. In regression analysis, dependent variables are called outcome variables and independent variables are called predictors.

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