[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"sanity-TMNNqbBmIfVtr8ON1F2YpQ_Br4YE3N_UnZphadytvG4":3,"sanity-rSGCtOcCUN1HJeI4-RkMRd-O2Ttf9f8rUVD4-IuNYlA":298},{"data":4,"sourceMap":-1},{"latestPodcast":5,"latestReleases":14,"post":39,"recent":273},[6],{"_id":7,"publishedAt":8,"slug":9,"sponsored":12,"title":13},"d53f9358-3bb2-4f69-aebe-d31d19522cd4","2026-07-10T07:40:00.000Z",{"_type":10,"current":11},"slug","building-more-than-just-an-agent-harness",null,"Building more than just an agent harness",[15,21,27,33],{"_id":16,"publishedAt":17,"slug":18,"title":20},"eb5b66eb-9410-4329-83bb-22bbff39402a","2026-04-28T13:00:00.000Z",{"_type":10,"current":19},"turn-scattered-knowledge-into-trusted-intelligence","Turning scattered knowledge into trusted intelligence: Stack Internal 2026.3",{"_id":22,"publishedAt":23,"slug":24,"title":26},"369c2401-b62e-4a37-8ff8-bf603023ecad","2026-03-02T15:03:00.988Z",{"_type":10,"current":25},"what-s-new-at-stack-overflow-march-2026","What’s new at Stack Overflow: March 2026",{"_id":28,"publishedAt":29,"slug":30,"title":32},"5e9053a4-07ea-447c-91ea-29e0b6228537","2026-02-02T15:00:00.000Z",{"_type":10,"current":31},"what-s-new-at-stack-overflow-february-2026","What’s new at Stack Overflow: February 2026",{"_id":34,"publishedAt":35,"slug":36,"title":38},"a1b538eb-a8a6-46d0-80a1-ac70ec9bb935","2026-01-05T10:00:00.000-05:00",{"_type":10,"current":37},"what-s-new-at-stack-overflow-january-2026","What’s new at Stack Overflow: January 2026",{"_createdAt":40,"_id":41,"_rev":42,"_type":43,"_updatedAt":44,"author":45,"body":62,"comments":232,"dateUrl":233,"excerpt":234,"image":235,"legacyBody":238,"product":12,"publishedAt":241,"slug":242,"sponsored":12,"tags":244,"title":272,"visible":232},"2023-05-25T09:39:18Z","wp-post-18104","dgl3SCUzppW3U2LvCoSsMu","blogPost","2023-07-13T14:55:59Z",[46],{"_createdAt":47,"_id":48,"_rev":49,"_type":50,"_updatedAt":51,"avatar":52,"bio":57,"employee":58,"name":59,"slug":60},"2023-05-23T16:27:18Z","wp-author-cap-17508","07ZbrKPSUrjrV4wQ6fDpaa","blogAuthor","2023-06-20T15:05:10Z",{"_type":53,"asset":54},"image",{"_ref":55,"_type":56},"image-8c28cb2ef9d5c7ef909c7685c3808e5c66f17aeb-400x400-jpg","reference","Curriculum Developer, Codecademy","none","Sophie Sommer",{"current":61},"sophie-sommer",[63,74,115,123,142,150,154,162,173,184,214],{"_key":64,"_type":65,"children":66,"markDefs":72,"style":73},"a4a52fbb4b84","block",[67],{"_key":68,"_type":69,"marks":70,"text":71},"a4a52fbb4b840","span",[],"Linear regression is a machine learning technique for modeling continuous outcomes. It is used for both prediction and data analysis in a variety of different fields. It is also the basis for a number of other machine learning models, including logistic regression and poisson regression. For anyone who is interested in learning more about data science and statistics, or for anyone who wants to read and understand research papers more easily, linear regression is a great place to start!",[],"normal",{"_key":75,"_type":65,"children":76,"markDefs":107,"style":73},"abf117b2795d",[77,81,85,89,94,98,103],{"_key":78,"_type":69,"marks":79,"text":80},"abf117b2795d0",[],"The Codecademy Live: Linear Regression in Python series will be hosted by ",{"_key":82,"_type":69,"marks":83,"text":59},"abf117b2795d1",[84],"f3ff32c2fd85",{"_key":86,"_type":69,"marks":87,"text":88},"abf117b2795d2",[],", a Curriculum Developer at Codecademy and creator of the ",{"_key":90,"_type":69,"marks":91,"text":93},"abf117b2795d3",[92],"326d41413176","Linear Regression in Python course",{"_key":95,"_type":69,"marks":96,"text":97},"abf117b2795d4",[]," on ",{"_key":99,"_type":69,"marks":100,"text":102},"abf117b2795d5",[101],"4a31f0dabc70","Codecademy",{"_key":104,"_type":69,"marks":105,"text":106},"abf117b2795d6",[],". She has a masters degree in Applied Statistics from NYU and six years of classroom teaching experience, working with middle school through masters-level students.",[108,111,113],{"_key":84,"_type":109,"href":110,"reference":12},"link","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsophia-sophie-sommer-1a4359a7\u002F",{"_key":92,"_type":109,"href":112,"reference":12},"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1",{"_key":101,"_type":109,"href":114,"reference":12},"https:\u002F\u002Fwww.codecademy.com\u002F?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1",{"_key":116,"_type":65,"children":117,"markDefs":122,"style":73},"608412b2b347",[118],{"_key":119,"_type":69,"marks":120,"text":121},"608412b2b3470",[],"The live series will start with a simple linear regression model and slowly build toward more complex and flexible models that can handle real-world (and messy) data. We'll mostly follow the Linear Regression in Python course, but will cover some bonus topics as time permits.",[],{"_key":124,"_type":65,"children":125,"markDefs":139,"style":73},"01eb0b25b47b",[126,130,135],{"_key":127,"_type":69,"marks":128,"text":129},"01eb0b25b47b0",[],"Both the course and the stream are free for anyone! We'll also be hosting 30 minutes of office hours on Thursdays at 11am EDT through at least June 3rd. During the office hours, anyone is welcome to join and ask questions about anything from the livestream or course. If you want to join those sessions, you can find more information on our ",{"_key":131,"_type":69,"marks":132,"text":134},"01eb0b25b47b1",[133],"f02cff16a297","Events Page",{"_key":136,"_type":69,"marks":137,"text":138},"01eb0b25b47b2",[],". We look forward to meeting some of you in those sessions!",[140],{"_key":133,"_type":109,"href":141,"reference":12},"https:\u002F\u002Fwww.codecademy.com\u002Fevents\u002F",{"_key":143,"_type":65,"children":144,"markDefs":149,"style":73},"62f6fed76625",[145],{"_key":146,"_type":69,"marks":147,"text":148},"62f6fed766250",[],"In the first lesson of the series, we'll be covering the basics of simple linear regression with a quantitative predictor. We'll use a small dataset to build a linear regression model that predicts weight based on height. In the process, we'll demo how to use a Jupyter notebook and introduce some common Python packages for data analysis. We'll also discuss some of the assumptions of linear regression and teach you to fit a simple model in Python.",[],{"_key":151,"_type":152,"markDefs":12,"url":153},"2616f4ee017a","embed","https:\u002F\u002Fwww.youtube.com\u002Fembed\u002F2htO1YFkpds?start=311",{"_key":155,"_type":65,"children":156,"markDefs":161,"style":73},"8d23427463c9",[157],{"_key":158,"_type":69,"marks":159,"text":160},"8d23427463c90",[],"Here are some Stack Overflow questions related to the work we did in today's session:",[],{"_key":163,"_type":65,"children":164,"markDefs":170,"style":73},"7d5b78c2b258",[165],{"_key":166,"_type":69,"marks":167,"text":169},"7d5b78c2b2580",[168],"a6ade6b3828d","OLS Regression: Scikit vs. Statsmodels",[171],{"_key":168,"_type":109,"href":172,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22054964\u002Fols-regression-scikit-vs-statsmodels",{"_key":174,"_type":65,"children":175,"markDefs":181,"style":73},"a8a49a9d3c2f",[176],{"_key":177,"_type":69,"marks":178,"text":180},"a8a49a9d3c2f0",[179],"22e04a6af999","How to Plot Statsmodels Linear Regression (OLS) Cleanly",[182],{"_key":179,"_type":109,"href":183,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F42261976\u002Fhow-to-plot-statsmodels-linear-regression-ols-cleanly",{"_key":185,"_type":65,"children":186,"markDefs":209,"style":73},"fef020b20be3",[187,191,196,200,205],{"_key":188,"_type":69,"marks":189,"text":190},"fef020b20be30",[],"If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the ",{"_key":192,"_type":69,"marks":193,"text":195},"fef020b20be31",[194],"023f0be480c9","YouTube recording of this lesson",{"_key":197,"_type":69,"marks":198,"text":199},"fef020b20be32",[],". If you’d like to watch a session live, follow the ",{"_key":201,"_type":69,"marks":202,"text":204},"fef020b20be33",[203],"c112210c6efa","Codecademy YouTube channel",{"_key":206,"_type":69,"marks":207,"text":208},"fef020b20be34",[],".",[210,212],{"_key":194,"_type":109,"href":211,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2htO1YFkpds",{"_key":203,"_type":109,"href":213,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured",{"_key":215,"_type":65,"children":216,"markDefs":230,"style":73},"50e979b13300",[217,221,226],{"_key":218,"_type":69,"marks":219,"text":220},"50e979b133000",[],"Finally, if you want even more linear regression content, you can sign up for the ",{"_key":222,"_type":69,"marks":223,"text":225},"50e979b133001",[224],"63463fb49851","Linear Regression in Python interactive course",{"_key":227,"_type":69,"marks":228,"text":229},"50e979b133002",[]," this series was based on. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!",[231],{"_key":224,"_type":109,"href":112,"reference":12},true,"2021\u002F05\u002F22","",{"_type":53,"asset":236},{"_ref":237,"_type":56},"image-f5b272e299c874f83358613fe0855ad7f7ea164c-2400x1240-png",{"code":239,"language":240},"\u003C!-- wp:paragraph -->\n\u003Cp>Linear regression is a machine learning technique for modeling continuous outcomes. It is used for both prediction and data analysis in a variety of different fields. It is also the basis for a number of other machine learning models, including logistic regression and poisson regression. For anyone who is interested in learning more about data science and statistics, or for anyone who wants to read and understand research papers more easily, linear regression is a great place to start!\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>The Codecademy Live: Linear Regression in Python series will be hosted by \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsophia-sophie-sommer-1a4359a7\u002F\">Sophie Sommer\u003C\u002Fa>, a Curriculum Developer at Codecademy and creator of the \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1\">Linear Regression in Python course\u003C\u002Fa> on \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002F?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1\">Codecademy\u003C\u002Fa>. She has a masters degree in Applied Statistics from NYU and six years of classroom teaching experience, working with middle school through masters-level students.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>The live series will start with a simple linear regression model and slowly build toward more complex and flexible models that can handle real-world (and messy) data. We'll mostly follow the Linear Regression in Python course, but will cover some bonus topics as time permits. \u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Both the course and the stream are free for anyone! We'll also be hosting 30 minutes of office hours on Thursdays at 11am EDT through at least June 3rd. During the office hours, anyone is welcome to join and ask questions about anything from the livestream or course. If you want to join those sessions, you can find more information on our \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002Fevents\u002F\">Events Page\u003C\u002Fa>. We look forward to meeting some of you in those sessions!\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>In the first lesson of the series, we'll be covering the basics of simple linear regression with a quantitative predictor. We'll use a small dataset to build a linear regression model that predicts weight based on height. In the process, we'll demo how to use a Jupyter notebook and introduce some common Python packages for data analysis. We'll also discuss some of the assumptions of linear regression and teach you to fit a simple model in Python.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:html -->\n\u003Ciframe width=\"560\" height=\"560\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002F2htO1YFkpds?start=311\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen>\u003C\u002Fiframe>\n\u003C!-- \u002Fwp:html -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Here are some Stack Overflow questions related to the work we did in today's session:\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:jetpack\u002Fmarkdown {\"source\":\"[OLS Regression: Scikit vs. Statsmodels](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22054964\u002Fols-regression-scikit-vs-statsmodels)\"} -->\n\u003Cdiv class=\"wp-block-jetpack-markdown\">\u003Cp>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22054964\u002Fols-regression-scikit-vs-statsmodels\">OLS Regression: Scikit vs. Statsmodels\u003C\u002Fa>\u003C\u002Fp>\n\u003C\u002Fdiv>\n\u003C!-- \u002Fwp:jetpack\u002Fmarkdown -->\n\n\u003C!-- wp:jetpack\u002Fmarkdown {\"source\":\"[How to Plot Statsmodels Linear Regression (OLS) Cleanly](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F42261976\u002Fhow-to-plot-statsmodels-linear-regression-ols-cleanly)\"} -->\n\u003Cdiv class=\"wp-block-jetpack-markdown\">\u003Cp>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F42261976\u002Fhow-to-plot-statsmodels-linear-regression-ols-cleanly\">How to Plot Statsmodels Linear Regression (OLS) Cleanly\u003C\u002Fa>\u003C\u002Fp>\n\u003C\u002Fdiv>\n\u003C!-- \u002Fwp:jetpack\u002Fmarkdown -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2htO1YFkpds\">YouTube recording of this lesson\u003C\u002Fa>. If you’d like to watch a session live, follow the \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured\">Codecademy YouTube channel\u003C\u002Fa>.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Finally, if you want even more linear regression content, you can sign up for the \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1\">Linear Regression in Python interactive course\u003C\u002Fa> this series was based on. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->","html","2021-05-22T14:00:00.000Z",{"current":243},"level-up-linear-regression-in-python-part-1",[245,253,258,263,268],{"_createdAt":246,"_id":247,"_rev":248,"_type":249,"_updatedAt":246,"slug":250,"title":252},"2023-05-23T16:43:21Z","wp-tagcat-code-for-a-living","9HpbCsT2tq0xwozQfkc4ih","blogTag",{"current":251},"code-for-a-living","Code for a Living",{"_createdAt":246,"_id":254,"_rev":248,"_type":249,"_updatedAt":246,"slug":255,"title":257},"wp-tagcat-jupyter-notebooks",{"current":256},"jupyter-notebooks","jupyter notebooks",{"_createdAt":246,"_id":259,"_rev":248,"_type":249,"_updatedAt":246,"slug":260,"title":262},"wp-tagcat-level-up",{"current":261},"level-up","level up",{"_createdAt":246,"_id":264,"_rev":248,"_type":249,"_updatedAt":246,"slug":265,"title":267},"wp-tagcat-linear-regression",{"current":266},"linear-regression","linear regression",{"_createdAt":246,"_id":269,"_rev":248,"_type":249,"_updatedAt":246,"slug":270,"title":271},"wp-tagcat-python",{"current":271},"python","Level Up: Linear Regression in Python - Part 1",[274,280,286,292],{"_id":275,"publishedAt":276,"slug":277,"sponsored":12,"title":279},"76c9771b-34e6-4d98-8641-ecefc711f0ef","2026-07-06T15:23:34.559Z",{"_type":10,"current":278},"when-the-sensor-starts-thinking-snortml-agentic-ai-and-the-evolving-architecture-of-intrusion-detection","When the sensor starts thinking: SnortML, agentic AI, and the evolving architecture of intrusion detection",{"_id":281,"publishedAt":282,"slug":283,"sponsored":12,"title":285},"28e560af-f0aa-4d46-bd90-f435ad604aa7","2026-06-26T14:00:27.102Z",{"_type":10,"current":284},"paging-charity-how-can-engineering-leaders-avoid-becoming-bond-villains","Paging Charity! How can engineering leaders avoid becoming Bond villains?",{"_id":287,"publishedAt":288,"slug":289,"sponsored":12,"title":291},"4b22c2a3-3779-4966-93eb-5230391dbdce","2026-06-23T14:08:58.595Z",{"_type":10,"current":290},"your-ai-shipped-a-backend-that-boots-that-is-the-whole-problem","Your AI shipped a backend that boots. That is the whole problem.",{"_id":293,"publishedAt":294,"slug":295,"sponsored":12,"title":297},"5cf362e1-fe7b-45af-b69c-914731c6a052","2026-06-23T14:00:00.000Z",{"_type":10,"current":296},"the-2026-developer-survey-is-now-open-for-human-developers-only","The 2026 Developer Survey is now open (for human developers only)!",{"data":299,"sourceMap":-1},{"count":300,"lastTimestamp":12},0]