Identifying　multiple　change　points　in　the　mean　and/or　variance　is　crucial　across　various　fields,　including　finance　and　quality　control.　We　introduce　a　novel　technique　that　detects　change　points　for　the　mean　and/or　variance　of　a　noisy　sequence　and　constructs　confidence　intervals　for　both　the　mean　and　variance　of　the　sequence.　This　method　integrates　the　weighted　bootstrap　with　the　Sequential　Binary　Segmentation　(SBS)　algorithm.　Not　only　does　our　technique　pinpoint　the　location　and　number　of　change　points,　but　it　also　determines　the　type　of　change　for　each　estimated　point,　specifying　whether　the　change　occurred　in　the　mean,　variance,　or　both.　Our　simulations　show　that　our　method　outperforms　other　approaches　in　most　scenarios,　clearly　demonstrating　its　superiority.　Finally,　we　apply　our　technique　to　three　datasets,　including　DNA　copy　number　variation,　stock　volume,　and　traffic　flow　data,　further　validating　its　practical　utility　and　wide-ranging　applicability.