Use the below output from a linear regression to answer the next two questions. This is model to predict the clarity of a contact lens (higher value reflects a lense that is clearer).

Looking at the variables, which of the variables are statistically significant? Just choose one answer.

a.Temperature

b.Raw Material Impurity

c.Time in Process

d.A and B

e.B and C

Once you create a Value Stream Map, you can use an Affinity Diagram to do what? Circle just one answer.

a.Do statistical analysis and fit the data to a line

b.Organize ideas in logical groupings to help identify workstreams to investigate

c.Evaluate the risks associated with changing a process

d.Calculate the process time of the process being mapped

e.Understand the timeline for a new production line

\begin{tabular}{|l|r|} \hline \multicolumn{2}{|c|}{ Regression Statistics } \\ \hline Multiple R & 0.97629364 \\ \hline R Square & 0.95314928 \\ \hline Adjusted R Square & 0.94774343 \\ \hline Standard Error & 3.28892843 \\ \hline Observations & 30 \\ \hline \end{tabular} ANOVA \begin{tabular}{|l|r|r|r|r|} \hline & df & \multicolumn{1}{c|}{ SS } & \multicolumn{1}{c|}{ MS } & \multicolumn{1}{c|}{F} \\ \hline Regression & 3 & 5721.723362 & 1907.241121 & 176.3180429 \\ \hline Residual & 26 & 281.2433051 & 10.8170502 & \\ \hline Total & 29 & 6002.966667 & & \\ \hline \end{tabular} \begin{tabular}{|l|r|r|r|r|} \hline & Coefficients & Standard Error & \multicolumn{1}{c|}{t Stat } & P-value \\ \hline Intercept & 53.5268 & 10.9584 & 4.8846 & 0.0000 \\ \hline Temperature & 0.0456 & 0.0739 & 0.6167 & 0.5428 \\ \hline Time in Process & 1.9491 & 0.1885 & 10.3399 & 0.0000 \\ \hline Raw Material Impurity & 0.3357 & 0.0780 & 4.3041 & 0.0345 \\ \hline \end{tabular}