{"id":57143,"date":"2025-04-07T17:31:15","date_gmt":"2025-04-07T21:31:15","guid":{"rendered":"https:\/\/www.sisinternational.com\/?page_id=57143"},"modified":"2026-05-05T16:16:42","modified_gmt":"2026-05-05T20:16:42","slug":"statistical-modeling-tools","status":"publish","type":"page","link":"https:\/\/www.sisinternational.com\/zh_hk\/%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/%e5%ae%9a%e6%80%a7%e5%ae%9a%e9%87%8f%e7%a0%94%e7%a9%b6%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/statistical-modeling-tools\/","title":{"rendered":"Statistical Modeling Tools for Industrial Leaders"},"content":{"rendered":"<div class=\"sis-hero-preserved sis-injected-hero\" data-sis-injected=\"hero\">\n<h1 class=\"wp-block-heading\"><strong><a href=\"https:\/\/www.sisinternational.com\/zh_hk\/packagings-role-in-brand-trust-why-your-box-matters-more-than-you-think\/\" class=\"sis-link-recovered\" data-sis-recovered=\"1\">\u7d71\u8a08\u5efa\u6a21<\/a> \u5de5\u5177<\/strong><\/h1>\n<figure class=\"gb-block-image gb-block-image-d5f07747\"><img loading=\"lazy\" decoding=\"async\" width=\"1456\" height=\"816\" class=\"gb-image gb-image-d5f07747\" src=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-17.jpg\" alt=\"SIS \u570b\u969b\u5e02\u5834\u7814\u7a76\u8207\u7b56\u7565\" title=\"Quantitative research (17)\" srcset=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-17.jpg 1456w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-17-300x168.jpg 300w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-17-1024x574.jpg 1024w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-17-768x430.jpg 768w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-17-18x10.jpg 18w\" sizes=\"auto, (max-width: 1456px) 100vw, 1456px\"><\/figure>\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><strong>Real statistical modeling tools don&#8217;t just describe what is\u2014they reveal what will be, why it happens, and how you can bend that future to your will. <\/strong><\/p>\n<\/blockquote>\n<p><strong>Statistical modeling tools<\/strong> changed everything for businesses. They transformed data from a passive historian documenting what already happened into a crystal ball revealing what&#8217;s coming next. They replaced the expensive luxury of executive intuition with the brutal clarity of mathematical prediction.<\/p>\n<p>You&#8217;re thinking&#8230; &#8220;But we already analyze our data.&#8221; Let me be brutally honest: what most companies call &#8220;analysis&#8221; is the statistical equivalent of examining a Rembrandt with a magnifying glass. You might see a few brushstrokes in excruciating detail, but you&#8217;ve completely missed the masterpiece.<\/p>\n<div class=\"wp-block-columns has-global-color-9-color has-base-2-background-color has-text-color has-background has-link-color wp-elements-19822ac082f9ded0e38b8eb8c3a82691 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:25%\"><\/div>\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<div class=\"wp-block-rank-math-toc-block\" style=\"font-size:15px\" id=\"rank-math-toc\">\n<h2>Table of Contents<\/h2>\n<nav>\n<ul>\n<li><a href=\"#the-evolution-of-statistical-modeling-tools\">The Evolution of Statistical Modeling Tools<\/a><\/li>\n<li><a href=\"#core-statistical-modeling-techniques-that-actually-move-needles\">Core Statistical Modeling Tools and Techniques That Actually Move Needles<\/a><\/li>\n<li><a href=\"#from-data-to-decisions-implementing-models-that-actually-matter\">From Data to Decisions: Implementing Models That Actually Matter<\/a><\/li>\n<li><a href=\"#emerging-frontiers-where-statistical-modeling-is-heading\">Emerging Frontiers: Where Statistical Modeling Is Heading<\/a><\/li>\n<li><a href=\"#the-human-element-building-statistical-literacy-that-sticks\">The Human Element: Building Statistical Literacy That Sticks<\/a><\/li>\n<li><a href=\"#key-takeaways\">Key Takeaways: Statistical Modeling Tools<\/a><\/li>\n<li><a href=\"#what-makes-sis-international-a-top-statistical-modeling-partner\">What Makes SIS International a Top Statistical Modeling Partner?<\/a><\/li>\n<li><a href=\"#frequently-asked-questions\">FAQs: Statistical Modeling Tools<\/a><\/li>\n<\/ul>\n<\/nav>\n<\/div>\n<\/div>\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:45%\">\n<p><strong>\u2705 Listen to this PODCAST EPISODE here:<\/strong><\/p>\n<figure class=\"wp-block-embed is-type-rich is-provider-spotify wp-block-embed-spotify wp-embed-aspect-21-9 wp-has-aspect-ratio\">\n<div class=\"wp-block-embed__wrapper\">\n<iframe title=\"Spotify Embed: Enhancing Product Appeal Through Consumer Choice Modeling\" style=\"border-radius: 12px\" width=\"100%\" height=\"152\" frameborder=\"0\" allowfullscreen allow=\"autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture\" loading=\"lazy\" src=\"https:\/\/open.spotify.com\/embed\/episode\/5G8rAbGhHTbIUD87hzX0Zn?si=238d2a4170f84b3a&#038;utm_source=oembed\"><\/iframe>\n<\/div>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<h1>Statistical Modeling Tools: How Industrial Leaders Convert Data Into Margin<\/h1>\n<p>Statistical modeling tools have moved from the analytics back office into the operating core of industrial enterprises. The shift matters because the firms doing it well are pulling ahead on aftermarket revenue strategy, supplier qualification, and predictive maintenance sizing while peers still run quarterly regressions in spreadsheets.<\/p>\n<p>For a Fortune 500 VP, the question is no longer whether to invest. It is which class of tool answers which class of decision, and how to deploy them so finance, operations, and commercial functions read the same numbers the same way.<\/p>\n<h2>Why Statistical Modeling Tools Now Sit Inside the P&#038;L<\/h2>\n<p>Three forces pulled statistical modeling tools out of the data science group and into line-of-business decisions. Bill of materials optimization became a monthly exercise as input volatility rose. Installed base analytics matured into a revenue function. Reshoring feasibility studies started landing on capex committees that demand probabilistic answers, not point estimates.<\/p>\n<p>The leaders treat modeling as plumbing. SAS, R, Python with statsmodels, JMP, and Minitab each handle different jobs, and the best industrial operators pair them deliberately. SAS for regulated reporting workflows. R and Python for elasticity, mix, and survival models. JMP for design of experiments on the plant floor. Minitab for Six Sigma capability studies that quality engineers actually run.<\/p>\n<p><span style=\"color:#216896;border-left:3px solid #216896;padding-left:0.5rem;\"><span class=\"sis-injected-quote\" data-sis-injected=\"quote\" style=\"color:#216896;border-left:3px solid #216896;padding-left:0.5rem;\">According to SIS International Research, the industrial firms generating the highest return on analytics investment are not those with the most sophisticated algorithms.<\/span> They are the ones that match tool selection to decision cadence: monthly forecasts run on different infrastructure than real-time control loops, and treating them identically wastes capital on both ends.<\/span><\/p>\n<h2>The Tool Stack Behind Total Cost of Ownership Models<\/h2>\n<p>Total cost of ownership analysis is where statistical modeling tools earn or lose credibility with procurement and finance. The conventional approach uses deterministic spreadsheets with sensitivity tabs. The better approach layers Monte Carlo simulation on top of OEM procurement analysis, with input distributions calibrated against actual supplier qualification audit data rather than vendor-supplied reliability claims.<\/p>\n<p>Crystal Ball, @RISK, and increasingly Python notebooks with PyMC handle the simulation layer. The output a CFO trusts is not a single TCO number. It is a confidence interval tied to specific failure modes, warranty exposure, and aftermarket revenue assumptions that procurement and service can each defend.<\/p>\n<p>This is where many industrial firms underinvest. They license a modeling platform without rebuilding the input data pipeline, and the model inherits the same averages that produced the last bad forecast. The tool is only as honest as the installed base analytics feeding it.<\/p>\n<h2>Matching Statistical Modeling Tools to Industrial Decisions<\/h2>\n<figure class=\"wp-block-table sis-injected-table\" data-sis-injected=\"table\">\n<table>\n<thead>\n<tr>\n<th>Decision Class<\/th>\n<th>Primary Tool Category<\/th>\n<th>Typical Output<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Predictive maintenance sizing<\/td>\n<td>Survival analysis (R, Python lifelines)<\/td>\n<td>Time-to-failure distributions by asset class<\/td>\n<\/tr>\n<tr>\n<td>Aftermarket revenue strategy<\/td>\n<td>Mixed-effects models (SAS, R)<\/td>\n<td>Attach rate elasticity by install cohort<\/td>\n<\/tr>\n<tr>\n<td>Bill of materials optimization<\/td>\n<td>Stochastic optimization (Python, AIMMS)<\/td>\n<td>Cost distribution under input volatility<\/td>\n<\/tr>\n<tr>\n<td>Reshoring feasibility<\/td>\n<td>Monte Carlo (@RISK, Crystal Ball)<\/td>\n<td>NPV confidence intervals across scenarios<\/td>\n<\/tr>\n<tr>\n<td>Plant capability studies<\/td>\n<td>DOE platforms (JMP, Minitab)<\/td>\n<td>Process Cpk and factor significance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p style=\"font-size:11px;color:#666;margin-top:4px;\"><em>Source: SIS International Research<\/em><\/p>\n<h2>Where the Best Industrial Operators Pull Ahead<\/h2>\n<p>Three patterns separate the leaders. First, they version-control models the way software teams version-control code. GitHub or GitLab, with peer review before a model touches a forecast that goes to the board. Second, they run challenger models in parallel. The production model and a competing specification both score every cycle, and the winner gets promoted on documented criteria rather than analyst preference.<\/p>\n<p>Third, and least visible, they invest in feature stores. The same customer cohort definition feeds the churn model, the aftermarket pricing model, and the predictive maintenance sizing model. When commercial and service argue about a number, they argue about interpretation, not about which extract was used.<\/p>\n<p><span style=\"color:#216896;border-left:3px solid #216896;padding-left:0.5rem;\">SIS International&#8217;s B2B expert interviews with senior operations and commercial leaders across industrial manufacturers in North America, Germany, and Japan indicate that feature consistency across functions correlates more strongly with analytics ROI than tool sophistication. The firms that standardized definitions before standardizing tools captured value faster than those that did the reverse.<\/span><\/p>\n<h2>Build, Buy, or Blend the Statistical Modeling Stack<\/h2>\n<figure class=\"wp-block-image size-large sis-injected-img\" data-sis-injected=\"img\"><img loading=\"lazy\" decoding=\"async\" width=\"683\" height=\"1024\" class=\"gb-image gb-image-594be28c\" src=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/04\/Statistical_Modeling_Tools_Infographic-683x1024.jpg\" alt=\"SIS \u570b\u969b\u5e02\u5834\u7814\u7a76\u8207\u7b56\u7565\" title=\"Statistical_Modeling_Tools_Infographic\" srcset=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/04\/Statistical_Modeling_Tools_Infographic-683x1024.jpg 683w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/04\/Statistical_Modeling_Tools_Infographic-200x300.jpg 200w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/04\/Statistical_Modeling_Tools_Infographic-768x1152.jpg 768w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/04\/Statistical_Modeling_Tools_Infographic-8x12.jpg 8w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/04\/Statistical_Modeling_Tools_Infographic.jpg 1024w\" sizes=\"auto, (max-width: 683px) 100vw, 683px\"><\/figure>\n<p>The build-versus-buy debate has shifted. Pure-build stacks on open-source Python and R offer flexibility but carry hidden cost in validation, documentation, and auditor readiness. Pure-buy stacks on SAS Viya or Databricks ML accelerate deployment but constrain custom methodology when the business question is unusual.<\/p>\n<p>The blended approach is winning in practice. Regulated and finance-facing models on a validated commercial platform. Exploratory and competitive intelligence work in open-source environments. A clear handoff protocol between them, with documented model cards that travel with the artifact.<\/p>\n<p>The decision criteria are practical. How often does the model output enter an audited report? How frequently does the underlying business question change? How specialized is the methodology? Answers to those three questions point most industrial firms to a hybrid stack rather than the monolithic platform a single vendor proposes.<\/p>\n<h2>The SIS Original Framework: The Modeling Maturity Matrix<\/h2>\n<figure class=\"wp-block-image size-large sis-injected-img\" data-sis-injected=\"img\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"574\" class=\"gb-image gb-image-3a211df3\" src=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/08\/Desk-research-1-1024x574.jpg\" alt=\"SIS \u570b\u969b\u5e02\u5834\u7814\u7a76\u8207\u7b56\u7565\" title=\"Desk research (1)\" srcset=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/08\/Desk-research-1-1024x574.jpg 1024w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/08\/Desk-research-1-300x168.jpg 300w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/08\/Desk-research-1-768x430.jpg 768w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/08\/Desk-research-1-18x10.jpg 18w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/08\/Desk-research-1.jpg 1456w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"><\/figure>\n<p>SIS International applies a four-stage Modeling Maturity Matrix when assessing industrial analytics functions. Stage one is descriptive: dashboards and rear-view reporting. Stage two is diagnostic: regression on historical drivers. Stage three is predictive: validated forecasts with confidence intervals tied to operational decisions. Stage four is prescriptive: optimization and simulation embedded in transactional systems.<\/p>\n<p>Most Fortune 500 industrial firms operate at stage two with isolated stage three pockets. Movement between stages depends less on tool licenses than on data engineering, governance, and the willingness to retire models that no longer outperform challengers.<\/p>\n<h2>What This Means for the VP Sponsoring the Investment<\/h2>\n<figure class=\"wp-block-image size-large sis-injected-img\" data-sis-injected=\"img\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"574\" class=\"gb-image gb-image-447b6d14\" src=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-15-1024x574.jpg\" alt=\"SIS \u570b\u969b\u5e02\u5834\u7814\u7a76\u8207\u7b56\u7565\" title=\"Quantitative research (15)\" srcset=\"https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-15-1024x574.jpg 1024w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-15-300x168.jpg 300w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-15-768x430.jpg 768w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-15-18x10.jpg 18w, https:\/\/www.sisinternational.com\/wp-content\/uploads\/2025\/09\/Quantitative-research-15.jpg 1456w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"><\/figure>\n<p>Statistical modeling tools deliver the most value when the sponsor sets a small number of decisions the stack must improve, then funds the data plumbing those decisions require. Aftermarket revenue strategy, predictive maintenance sizing, and supplier qualification audit are the three highest-leverage targets in most industrial portfolios.<\/p>\n<p>The competitive advantage is durable because it compounds. Each cycle of validated forecasting tightens the next cycle&#8217;s input distributions. Firms that begin the discipline early pull ahead on margin, and the gap widens as their challenger models retire weaker specifications faster than competitors can replace them.<\/p>\n<h2 id=\"about-sis-international\" style=\"font-family:Arial,sans-serif;color:#1a3d68;\">\u95dc\u65bc SIS \u570b\u969b<\/h2>\n<p><a href=\"https:\/\/www.sisinternational.com\/zh_hk\/\">SIS\u570b\u969b<\/a> \u63d0\u4f9b\u5b9a\u91cf\u3001\u5b9a\u6027\u548c\u7b56\u7565\u7814\u7a76\u3002\u6211\u5011\u70ba\u6c7a\u7b56\u63d0\u4f9b\u6578\u64da\u3001\u5de5\u5177\u3001\u7b56\u7565\u3001\u5831\u544a\u548c\u898b\u89e3\u3002\u6211\u5011\u4e5f\u9032\u884c\u8a2a\u8ac7\u3001\u8abf\u67e5\u3001\u7126\u9ede\u5c0f\u7d44\u548c\u5176\u4ed6\u5e02\u5834\u7814\u7a76\u65b9\u6cd5\u548c\u9014\u5f91\u3002 <a href=\"https:\/\/www.sisinternational.com\/zh_hk\/%e9%97%9c%e6%96%bc-sis-%e5%9c%8b%e9%9a%9b%e7%a0%94%e7%a9%b6\/contact-sis-international-market-research\/\">\u806f\u7d61\u6211\u5011<\/a> \u70ba\u60a8\u7684\u4e0b\u4e00\u500b\u5e02\u5834\u7814\u7a76\u9805\u76ee\u3002<\/p>\n<p><!-- sis-hreflang-start -->\n<link rel=\"alternate\" hreflang=\"en-US\" href=\"https:\/\/www.sisinternational.com\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"ar\" href=\"https:\/\/www.sisinternational.com\/ar\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"zh-CN\" href=\"https:\/\/www.sisinternational.com\/zh\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"zh-HK\" href=\"https:\/\/www.sisinternational.com\/zh_hk\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"nl-NL\" href=\"https:\/\/www.sisinternational.com\/nl\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"fr-FR\" href=\"https:\/\/www.sisinternational.com\/fr\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"de-DE\" href=\"https:\/\/www.sisinternational.com\/de\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"it-IT\" href=\"https:\/\/www.sisinternational.com\/it\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"ja\" href=\"https:\/\/www.sisinternational.com\/ja\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"ko-KR\" href=\"https:\/\/www.sisinternational.com\/ko\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"pl-PL\" href=\"https:\/\/www.sisinternational.com\/pl\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"pt-BR\" href=\"https:\/\/www.sisinternational.com\/pt\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"es-ES\" href=\"https:\/\/www.sisinternational.com\/es\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"en\" href=\"https:\/\/www.sisinternational.com\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"zh\" href=\"https:\/\/www.sisinternational.com\/zh\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"nl\" href=\"https:\/\/www.sisinternational.com\/nl\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"fr\" href=\"https:\/\/www.sisinternational.com\/fr\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"de\" href=\"https:\/\/www.sisinternational.com\/de\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"it\" href=\"https:\/\/www.sisinternational.com\/it\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"ko\" href=\"https:\/\/www.sisinternational.com\/ko\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"pl\" href=\"https:\/\/www.sisinternational.com\/pl\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"pt\" href=\"https:\/\/www.sisinternational.com\/pt\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<link rel=\"alternate\" hreflang=\"es\" href=\"https:\/\/www.sisinternational.com\/es\/solutions\/qualitative-quantitative-research-solutions\/statistical-modeling-tools\/\" \/>\n<!-- sis-hreflang-end --><\/p>\n<section class=\"sis-related-recovered\" data-sis-recovered-section=\"1\">\n<h3>Related SIS Resources<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.sisinternational.com\/zh_hk\/%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/%e5%ae%9a%e6%80%a7%e5%ae%9a%e9%87%8f%e7%a0%94%e7%a9%b6%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/enhancing-product-appeal-through-consumer-choice-modeling\/\" class=\"sis-link-recovered\">models for a consumer<\/a><\/li>\n<li><a href=\"https:\/\/www.sisinternational.com\/zh_hk\/%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/%e5%ae%9a%e6%80%a7%e5%ae%9a%e9%87%8f%e7%a0%94%e7%a9%b6%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/how-the-gabor-granger-pricing-model-can-enhance-your-profit-margins\/\" class=\"sis-link-recovered\">modeling at price<\/a><\/li>\n<li><a href=\"https:\/\/www.linkedin.com\/company\/sisinternationalresearch\" class=\"sis-link-recovered\" target=\"_blank\" rel=\"noopener\">Our dedicated analytics teams<\/a><\/li>\n<\/ul>\n<\/section>","protected":false},"excerpt":{"rendered":"<p>Statistical Modeling Tools Real statistical modeling tools don&#8217;t just describe what is\u2014they reveal what will be, why it happens, and how you can bend that future to your will. Statistical modeling tools changed everything for businesses. They transformed data from a passive historian documenting what already happened into a crystal ball revealing what&#8217;s coming next. &#8230; <a title=\"Statistical Modeling Tools for Industrial Leaders\" class=\"read-more\" href=\"https:\/\/www.sisinternational.com\/zh_hk\/%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/%e5%ae%9a%e6%80%a7%e5%ae%9a%e9%87%8f%e7%a0%94%e7%a9%b6%e8%a7%a3%e6%b1%ba%e6%96%b9%e6%a1%88\/statistical-modeling-tools\/\" aria-label=\"Read more about Statistical Modeling Tools for Industrial Leaders\">\u95b1\u8b80\u66f4\u591a<\/a><\/p>","protected":false},"author":1,"featured_media":67030,"parent":14660,"menu_order":81,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-57143","page","type-page","status-publish","has-post-thumbnail"],"_links":{"self":[{"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/pages\/57143","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/comments?post=57143"}],"version-history":[{"count":19,"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/pages\/57143\/revisions"}],"predecessor-version":[{"id":87564,"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/pages\/57143\/revisions\/87564"}],"up":[{"embeddable":true,"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/pages\/14660"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/media\/67030"}],"wp:attachment":[{"href":"https:\/\/www.sisinternational.com\/zh_hk\/wp-json\/wp\/v2\/media?parent=57143"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}