{"width":"100%","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.crosshyou.info%2Fentry%2F2022%2F02%2F20%2F104749\" title=\"OECD Material productivity data analysis 3 - Using R for multiple linear regression. OLS(ordinary least squares) and  WLS(weighted least squares) - R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","description":"Photo by Wolfgang Hasselmann on Unsplash www.crosshyou.info This post is following of above post. From the previous post, NONNRGMAT has correlated to r_capi: squared rooted per capita gdp. Let's do regression analysys using R. p-value for r_capi is almost 0. For TIME variables are not significant. L\u2026","url":"https://www.crosshyou.info/entry/2022/02/20/104749","provider_url":"https://hatena.blog","author_name":"cross_hyou","title":"OECD Material productivity data analysis 3 - Using R for multiple linear regression. OLS(ordinary least squares) and  WLS(weighted least squares)","height":"190","published":"2022-02-20 10:47:49","type":"rich","author_url":"https://blog.hatena.ne.jp/cross_hyou/","blog_title":"R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/cross_hyou/20220220/20220220094614.jpg","provider_name":"Hatena Blog","blog_url":"https://www.crosshyou.info/","version":"1.0","categories":["Data_Analysis"]}