The Regression Outputs for Sales and Number of Salespeople Are

The regression outputs for sales and number of salespeople are shown below. Model summary
$$\begin{array} { | l | l | r | r | r | } \hline \text { Model } & \boldsymbol { R } & \boldsymbol { R } \text {-square } & \begin{array} { c } \text { Adjusted } \\\boldsymbol { R } \text {-square }\end{array} & \begin{array} { r } \text { Std. error of } \\\text { the estimate }\end{array} \\\hline 1 & .201 \text { (a) } & .04 & .342 & 56.823 \\\hline\end{array}$$ a Predictors: (Constant) , number of salespeople
ANOVA(b)
$$\begin{array} { | l | l | c | r | r | r | r | } \hline \text { Model } & & \begin{array} { c } \text { Sum of } \\\text { squares }\end{array} &\text{ df} & \text { Mean square } & \boldsymbol { F } & { \text { Sig. } } \\\hline 1 & \text { Regression } & 77152.238 & 1 & 77152.238 & 35.117 & .057 ( \mathrm { a } ) \\\hline & \text { Residual } & 61516.962 & 28 & 2197.034 & & \\\hline & \text { Total } & 138669.200 & 29 & & & \\\hline\end{array}$$ a Predictors: (Constant) , number of salespeople
B Dependent variable: Sales (A$'000)
Coefficients(a)
$$\begin{array} { | l | l | c | r | r | r | r| } \hline \text { Model } & &{ \begin{array} { c } \text { Unstandardised } \\\text { coefficients }\end{array} } & \begin{array} { c } \text { Standardised } \\\text { coefficients }\end{array} & { \boldsymbol { t } } & { \text { Sig. } } \\\hline & & \boldsymbol { B } & \text { Std. Error } & \text { Beta } & & \\\hline 1 & \text { (Constant) } & 72.612 & 9.203 & & 2.565 & .013 \\\hline & \text { Number of salespeople } & 35.623 & 3.296 & .201 & 5.926 & .064 \\\hline\end{array}$$ a Dependent variable: Sales (A$'000)
The above shows that:

A) for every one-unit increase in number of salespeople, average sales will increase by approximately 73 units
B) the regression results suggested a good model fit
C) the observed results occurred as a result of sampling error
D) the regression coefficient is significant