Apply the following steps on this wind quality dataset $($here Download here$)$:

step $1$: In excel regression, use "quality" as Y variable, and use all other columns as X variables by selecting the whole block as the input x range, click on label, output the results starting from O$1,$ and generating NO plots.

step $2$: Copy the winequality$-$red sheet to create a duplicate worksheet,

step $3$: In the duplicated sheet, from the excel output, identify variables with p$-$value greater than $0\mathrm{.}05,$ which means the corresponding variables are not predictive. $($Btw$,8\mathrm{.}37$E$-06$ means $0\mathrm{.}000000837).$ Remove those variables by deleting those colums. Also delete the excel output portion.

Repeat Step $1-3,$ until all variables remaining are significantly predictive, i$.$e$.,$ with p$-$value less than $0\mathrm{.}05.$ This might or might not require more than two iterations.

a$.$ In your final model, what factors have positive influence on quality and what factors have negative influence on quality?

b$.$ use your final model to predict the quality of this wine with the profile below:

fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol

$7\mathrm{.}00\mathrm{.}88020\mathrm{.}07115320\mathrm{.}99783\mathrm{.}310\mathrm{.}469\mathrm{.}8$