Figure $3$: Lifestyle, bounce rate, and conversion rate

$\backslash$table$[[,$Acquisition,Online Behavior,Purchase$],[$Lifestyle$,$All Users,New Users,$\backslash$table$[[$Users Who$],[$Viewed$],[$Only One$],[$Page$]],\backslash$table$[[$Bounce$],[$Rate$]],\backslash$table$[[$Pages$],[$Viewed per$],[$User$]],\backslash$table$[[$User$],[$Duration $($on$],[$avg$.)$ in$],[$Seconds$]],\backslash$table$[[$Users Who$],[$Were$],[$Engaged in a$],[$Transaction$]],\backslash$table$[[$Ecommerce$],[$REnversion$],[$Rate$]],,],[\backslash$table$[[$Avid Shoppers$]],500,200,100,,4,80,25,$$$2,000,,],[\backslash$table$[[$kitchen$],[$strangers$]],2000,1000,75,,5,140,350,$$$20,000,,],[\backslash$table$[[$constraint$],[$shoppers$]],300,100,75,,3,30,10,$$$1,000,,],[\backslash$table$[[$hurried$],[$shoppers$]],700,500,100,,3,80,100,$$$10,000,,],[\backslash$table$[[$unfettered$],[$shoppers$]],500,100,50,,2,40,15,$$$2,000,,]]$

If you drill into the female group, what would be bounce rates for each lifestyle? $($Use the data in Figure $3.)$

$\backslash$table$[[,$A$,$B$,$C$,$E$,$F$,],[1,$Lifestyle,New Users,$\backslash$table$[[$Users Who$],[$Viewed Only $1],[$Page$]],$Bounce Rate,$\backslash$table$[[$Users Who Were$],[$Engaged in a$],[$Transaction$]],\backslash$table$[[$Ecommerce$],[$Conversion Rate$]]],[2,$Avid Shoppers,,,,,$],[3,$Kitchen Strangers,,,,,$],[4,$Constrained Shoppers,,,,,$],[5,$Hurried Shoppers,,,,,$],[6,$Unfettered Shoppers,,,,,$]]$