Month Year Units

Jan $139,807$

Feb $140,084$

Mar $147,441$

Apr $147,298$

May $149,211$

Jun $151,475$

Jul $146,467$

Aug $145,208$

Sep $144,800$

Oct $146,989$

Nov $142,163$

Dec $144,182$

Jan $242,227$

Feb $245,422$

Mar $254,073$

Apr $250,926$

May $253,571$

Jun $254,920$

Jul $254,449$

Aug $256,079$

Sep $252,177$

Oct $250,087$

Nov $248,513$

Dec $249,278$

Jan $348,134$

Feb $354,887$

Mar $361,065$

Apr $353,350$

May $359,467$

Jun $359,370$

Jul $355,086$

Aug $359,348$

Sep $354,472$

Oct $353,162$

Nov $348,793$

Dec $346,956$ Develop a multiple regression model with categorical variables that

incorporate seasonality for forecasting sales, where December is the

reference month.

Units $=,+(,)*$ Year $+(-3416)*Jan+(-8)*$ Feb

$+(,)*Mar+,$

$($Round to the nearest integer as needed.$)$ Now develop the best subset model by iteratively eliminating all

insignificant variables, using $=0\mathrm{.}10$ for the level of significance. Select the

correct choice below and fill in the answer boxes to complete your choices.

$($Round to the nearest integer as needed.$)$

A$.$

Units $=,+(,)*$ Year $+1,$

B$.$

Units $=,+(,)*$ Year $+(,)*$ Jan $+(,)*$ Feb $+(,)*$ Apr

$+(,)*$ May $+(,)*$ Jun $+(,)*$ Jul $+(,)*Sep+1,$

C$.$

Units $=,+(,)*Jan+(,)*Feb+(,)*$ Mar $+1$