1.) This is not an incomplete question. It has all the components of the question that was asked. Please help explain. Need help from a prob and stats expert: 

Let arr86 be a binary variable equal to 1 if a man was arrested during 1986, and zero
otherwise. You have a dataset of young men from California who were born in 1960 or
1961 and have at least one arrest prior to 1986. You also have data on the proportion of
prior arrests that led to a conviction (pcnv), the average sentence served from prior
convictions in months (avgsen), months spent in prison since age 18 prior to 1986
(tottime), months spent in prison in 1986 (ptime86), and the number of quarters (0 to 4)
that the man was legally employed in 1986 (qemp86). You estimate the following linear
probability model (standard errors are in parentheses):

The Standard Errors for the equation go in order for each independent variable of the equation, and are below. The equation is extended and is in two separte rows. All information is given.

arr86 = 0.441 – .162pcnv + .0061avgsen – .0023tottime
(.017) (.021) (.0065) (.005)

– .022ptime86 – .043qemp86
(.005) (.005)

n= 2725
R²=.0474

a) Interpret the coefficient on avgsen, commenting on both magnitude and significance. Based on these results, do longer sentences deter future crime?

b) What is the predicted probability of arrest for a man with no prior convictions, so that pcnv, avgsen, tottime, and ptime86 are all zero, who was also employed all four quarters in 1986?

c) Name an alternative model that could be used to estimate this regression instead. Describe one disadvantage of the alternative model.