Suppose we have a data set with five features, X$1=$ GPA, X$2=$ IQ$,$ X$3=$ Level $(1$ for College and $0$ for High School$),$ X$4=$ product between GPA and IQ$,$ and X$5=$ product between GPA and Level. We want to predict student$$s starting salary after graduation $($in thousands of dollars$).$ Suppose we use least squares to fit a linear regression model, and estimate the parameters of the model as follows: $\backslash$beta $0=50$; $\backslash$beta $1=$b$.$Explain the effect of the low values of $\backslash$beta $2$ and $\backslash$beta $4$ comparing to the high absolute values of $\backslash$beta $1,\backslash$beta $3,$ and $\backslash$beta $5.20$; $\backslash$beta $2=0\mathrm{.}07$; $\backslash$beta $3=35$; $\backslash$beta $4=0\mathrm{.}01$;