a) A Multiple Linear Regression Model in the form provided in Equation l is required to predict the amount of trips likely to be generated in the Bortianor vicinity in 2040. Y=a+2¡X1+æXz+æX3 (Equation 1) where Y= trips/household to work X1= number of cars owned by household X2 = income of household X3 = household size ao, ai, az and a3 =constants Using the corelation matrix obtained from a base year survey in Table 5, detemine all the possible equations that can be fomulated in the fom of Equation 1 out of which the best predictive model will be selected. Give reasons for your choice of equations. Assume all the independent variables are linearly related to the dependent variable and eachindependent variable is easily projected. Table 5: Comrelation matrix Household size Trips Car Income ownership Trips Car ownership Income Household size 1 0.65 1 0.90 0.89 1 0.97 0.25 0.18 1

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a) A Multiple Linear Regression Model in the form provided in Equation 1 is required to
predict the amount of trips likely to be generated in the Bortianor vicinity in 2040.
Y=a+ajX1+æXz+æX3 (Equation 1)
where Y= trips/household to work
X1= number of cars owned by household
X2 = income of household
X3 = household size
ao, ai, æ and a3 =constants
Using the corelation matrix obtained from a base year survey in Table 5, detemine
all the possible equations that can be fomulated in the fom of Equation 1 out of
which the best predictive model will be selected. Give reasons for your choice of
equations. Assume all the independent variables are linearly related to the dependent
variable and eachindependent variable is easily projected.
Table 5: Comrelation matrix
Trips
Household
size
Car
Income
ownership
Trips
Car ownership
Income
Household size
1
0.65
1
0.90
0.89
1
0.97
0.25
0.18
1
Transcribed Image Text:a) A Multiple Linear Regression Model in the form provided in Equation 1 is required to predict the amount of trips likely to be generated in the Bortianor vicinity in 2040. Y=a+ajX1+æXz+æX3 (Equation 1) where Y= trips/household to work X1= number of cars owned by household X2 = income of household X3 = household size ao, ai, æ and a3 =constants Using the corelation matrix obtained from a base year survey in Table 5, detemine all the possible equations that can be fomulated in the fom of Equation 1 out of which the best predictive model will be selected. Give reasons for your choice of equations. Assume all the independent variables are linearly related to the dependent variable and eachindependent variable is easily projected. Table 5: Comrelation matrix Trips Household size Car Income ownership Trips Car ownership Income Household size 1 0.65 1 0.90 0.89 1 0.97 0.25 0.18 1
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