Based on the Output 1, would you suspect the multicollinearityproblem in this model? Show all the necessary procedures. To study the demand for Dettol hand sanitizer in Malaysia in 2020, you aregiven the following model:Model (2):C; = B1 + B2X21 + B3X3¡ + B4X4¡ + µiwhereX2iX3iXạiConsumption of Dettol hand sanitizer (Malaysian ringgit)Number of Covid-19 infected MalaysianPrice of Dettol hand sanitizer (Malaysian ringgit)Price of Lifebuoy hand sanitizer (Malaysian ringgit)Hierror termThe regression output is stated as following:Output 1Dependent Variable: C¡Method: Least SquaresSample: 1 100VariableCoefficient Std. Errort-StatisticProb.X2iX3iX4iIntercept0.00950.00422.26080.0381-0.59690.1484-4.02260.00100.17660.06142.87520.011038.25683.280211.66270.0000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterion0.5441R-squaredAdjusted R-squared 0.9301S.E. of regressionSum squared residual 1271.796Log likelihoodF-statistic8.46660.4876921.1680231.1720571.1694161.9866891.7183-3945.67167.6051Hannan-Quinn criter.Durbin-Watson statProb(F-statistic)0.0001|| || || || ||

defination: Multicollinearity is a statistical concept where independent variables in a model are…

Answered: Based on the Output 1, would you…

Based on the Output 1, would you suspect the multicollinearity problem in this model? Show all the necessary procedures.

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

Based on the Output 1, would you suspect the multicollinearity
problem in this model? Show all the necessary procedures.

To study the demand for Dettol hand sanitizer in Malaysia in 2020, you are
given the following model:
Model (2):
C; = B1 + B2X21 + B3X3¡ + B4X4¡ + µi
where
X2i
X3i
Xại
Consumption of Dettol hand sanitizer (Malaysian ringgit)
Number of Covid-19 infected Malaysian
Price of Dettol hand sanitizer (Malaysian ringgit)
Price of Lifebuoy hand sanitizer (Malaysian ringgit)
Hi
error term
The regression output is stated as following:
Output 1
Dependent Variable: C¡
Method: Least Squares
Sample: 1 100
Variable
Coefficient Std. Error
t-Statistic
Prob.
X2i
X3i
X4i
Intercept
0.0095
0.0042
2.2608
0.0381
-0.5969
0.1484
-4.0226
0.0010
0.1766
0.0614
2.8752
0.0110
38.2568
3.2802
11.6627
0.0000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
0.5441
R-squared
Adjusted R-squared 0.9301
S.E. of regression
Sum squared residual 1271.796
Log likelihood
F-statistic
8.4666
0.487692
1.168023
1.172057
1.169416
1.986689
1.7183
-3945.671
67.6051
Hannan-Quinn criter.
Durbin-Watson stat
Prob(F-statistic)
0.0001
|| || || || ||

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