\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\u6709\u4e24\u4e2a\u7528\u9014\uff1a<\/span><\/p>\n (1)<\/strong>\u91cf\u5316\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u4e0e\u54cd\u5e94\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/span><\/p>\n (2)<\/b>\u4f7f\u7528\u6a21\u578b\u9884\u6d4b\u672a\u6765\u503c\u3002<\/span><\/p>\n\u5173\u4e8e\uff082\uff09<\/strong> \uff0c\u5f53\u6211\u4eec\u4f7f\u7528\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u672a\u6765\u503c\u65f6\uff0c\u6211\u4eec\u901a\u5e38\u5e0c\u671b\u9884\u6d4b\u7cbe\u786e\u503c<\/em>\u4ee5\u53ca\u5305\u542b\u4e00\u7cfb\u5217\u53ef\u80fd\u503c\u7684\u533a\u95f4<\/em>\u3002\u8fd9\u4e2a\u533a\u95f4\u79f0\u4e3a\u9884\u6d4b\u533a\u95f4<\/strong>\u3002<\/span><\/p>\n\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u4f7f\u7528\u5b66\u4e60\u65f6\u95f4<\/em>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\u3001\u8003\u8bd5\u6210\u7ee9<\/em>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\u6765\u62df\u5408\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002\u4f7f\u7528\u8fd9\u4e2a\u6a21\u578b\uff0c\u6211\u4eec\u53ef\u4ee5\u9884\u6d4b\u5b66\u4e60 6 \u5c0f\u65f6\u7684\u5b66\u751f\u5c06\u5728\u8003\u8bd5\u4e2d\u83b7\u5f9791 \u5206<\/strong>\u3002<\/span><\/p>\n\u7136\u800c\uff0c\u7531\u4e8e\u6b64\u9884\u6d4b\u5b58\u5728\u4e0d\u786e\u5b9a\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u9884\u6d4b\u533a\u95f4\uff0c\u8868\u660e\u5b66\u4e60 6 \u5c0f\u65f6\u7684\u5b66\u751f\u6709 95% \u7684\u673a\u4f1a\u83b7\u5f9785<\/strong>\u523097<\/strong>\u4e4b\u95f4\u7684\u8003\u8bd5\u6210\u7ee9\u3002\u8fd9\u4e2a\u503c\u8303\u56f4\u88ab\u79f0\u4e3a 95% \u9884\u6d4b\u533a\u95f4\uff0c\u5bf9\u6211\u4eec\u6765\u8bf4\u901a\u5e38\u6bd4\u4ec5\u4ec5\u77e5\u9053\u786e\u5207\u7684\u9884\u6d4b\u503c\u66f4\u6709\u7528\u3002<\/span><\/p>\n\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u9884\u6d4b\u533a\u95f4<\/strong><\/span><\/h2>\n\u4e3a\u4e86\u8bf4\u660e\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u9884\u6d4b\u533a\u95f4\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5185\u7f6e\u7684mtcars<\/em>\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u51e0\u79cd\u4e0d\u540c\u6c7d\u8f66\u7684\u7279\u5f81\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n #view first six rows of<\/span> mtcars<\/em><\/span>\nhead(mtcars)\n\n# mpg cyl disp hp drat wt qsec vs am gear carb\n#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1\n<\/strong><\/pre>\n\u9996\u5148\uff0c\u6211\u4eec\u5c06\u4f7f\u7528disp<\/em>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\u3001 mpg<\/em>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\u6765\u62df\u5408\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002<\/span><\/p>\n #fit simple linear regression model<\/span>\nmodel <- lm(mpg ~ disp, data = mtcars)\n\n#view summary of fitted model<\/span>\nsummary(model)\n\n#Call:\n#lm(formula = mpg ~ availability, data = mtcars)\n#\n#Residuals:\n# Min 1Q Median 3Q Max \n#-4.8922 -2.2022 -0.9631 1.6272 7.2305 \n#\n#Coefficients:\n#Estimate Std. Error t value Pr(>|t|) \n#(Intercept) 29.599855 1.229720 24.070 < 2e-16 ***\n#disp -0.041215 0.004712 -8.747 9.38e-10 ***\n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#Residual standard error: 3.251 on 30 degrees of freedom\n#Multiple R-squared: 0.7183, Adjusted R-squared: 0.709 \n#F-statistic: 76.51 on 1 and 30 DF, p-value: 9.38e-10\n<\/strong><\/pre>\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u62df\u5408\u56de\u5f52\u6a21\u578b\u6839\u636edisp<\/em>\u7684\u4e09\u4e2a\u65b0\u503c\u6765\u9884\u6d4bmpg<\/em>\u7684\u503c\u3002<\/span><\/p>\n #create data frame with three new values for<\/span> avail\n<\/span><\/em>new_disp <- data.frame(disp= c(150, 200, 250))\n\n#use the fitted model to predict the value for mpg<\/em><\/span> based on the three new values<\/span>\n#for<\/span> avail<\/span>\n<\/em>predict(model, newdata = new_disp)\n\n#1 2 3 \n#23.41759 21.35683 19.29607 \n<\/strong><\/pre>\n\u8fd9\u4e9b\u503c\u7684\u89e3\u91ca\u65b9\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n\n- \u5bf9\u4e8eEPA<\/em>\u4e3a 150 \u7684\u65b0\u8f66\uff0c\u6211\u4eec\u9884\u8ba1\u5176 mpg \u4e3a23.41759<\/strong> mpg<\/em> \u3002<\/span><\/li>\n
- \u5bf9\u4e8eEPA<\/em>\u4e3a 200 \u7684\u65b0\u8f66\uff0c\u6211\u4eec\u9884\u8ba1\u5b83\u7684mpg<\/em>\u4e3a21.35683 \u82f1\u91cc\/\u52a0\u4ed1<\/strong>\u3002<\/span><\/li>\n
- \u5bf9\u4e8eEPA<\/em>\u4e3a 250 \u7684\u65b0\u8f66\uff0c\u6211\u4eec\u9884\u8ba1\u5176mpg<\/em>\u4e3a19.29607 \u82f1\u91cc<\/strong>\u3002<\/span><\/li>\n<\/ul>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u62df\u5408\u56de\u5f52\u6a21\u578b\u56f4\u7ed5\u8fd9\u4e9b\u9884\u6d4b\u503c\u521b\u5efa\u9884\u6d4b\u533a\u95f4\uff1a<\/span><\/p>\n #create prediction intervals around the predicted values<\/span>\npredict(model, newdata = new_disp, interval = \" predict<\/span> \")<\/span>\n\n# fit lwr upr\n#1 23.41759 16.62968 30.20549\n#2 21.35683 14.60704 28.10662\n#3 19.29607 12.55021 26.04194\n<\/strong><\/pre>\n\u8fd9\u4e9b\u503c\u7684\u89e3\u91ca\u65b9\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n\n- EPA<\/em>\u4e3a 150 \u7684\u6c7d\u8f66\u7684 95% mpg<\/em>\u9884\u6d4b\u533a\u95f4\u572816.62968<\/strong>\u548c30.20549<\/strong>\u4e4b\u95f4\u3002<\/span><\/li>\n
- EPA<\/em>\u4e3a 200 \u7684\u6c7d\u8f66\u7684 95% mpg<\/em>\u9884\u6d4b\u533a\u95f4\u572814.60704<\/b>\u548c28.10662<\/strong>\u4e4b\u95f4\u3002<\/span><\/li>\n
- EPA<\/em>\u4e3a 250 \u7684\u6c7d\u8f66\u7684 95% mpg<\/em>\u9884\u6d4b\u533a\u95f4\u572812.55021<\/b>\u548c26.04194<\/strong>\u4e4b\u95f4\u3002<\/span><\/li>\n<\/ul>\n
\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cR \u4f7f\u7528 95% \u7684\u9884\u6d4b\u533a\u95f4\u3002\u4f46\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528level<\/strong>\u547d\u4ee4\u6839\u636e\u9700\u8981\u66f4\u6539\u6b64\u8bbe\u7f6e\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u4e86\u5982\u4f55\u521b\u5efa 99% \u9884\u6d4b\u533a\u95f4\uff1a<\/span><\/p>\n #create 99% prediction intervals around the predicted values\n<\/span>predict(model, newdata = new_disp, interval = \" predict<\/span> \", level = 0.99<\/span><\/span><\/span> )<\/span>\n\n# fit lwr upr\n#1 23.41759 14.27742 32.55775\n#2 21.35683 12.26799 30.44567\n#3 19.29607 10.21252 28.37963\n<\/strong><\/pre>\n\u8bf7\u6ce8\u610f\uff0c99% \u9884\u6d4b\u533a\u95f4\u6bd4 95% \u9884\u6d4b\u533a\u95f4\u66f4\u5bbd\u3002\u8fd9\u662f\u6709\u9053\u7406\u7684\uff0c\u56e0\u4e3a\u95f4\u9694\u8d8a\u5bbd\uff0c\u5305\u542b\u9884\u6d4b\u503c\u7684\u53ef\u80fd\u6027\u5c31\u8d8a\u5927\u3002<\/span><\/p>\n\u5982\u4f55\u5728 R \u4e2d\u53ef\u89c6\u5316\u9884\u6d4b\u533a\u95f4<\/strong><\/span><\/h2>\n\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u4e86\u5982\u4f55\u521b\u5efa\u5177\u6709\u4ee5\u4e0b\u529f\u80fd\u7684\u56fe\u8868\uff1a<\/span><\/p>\n\n- \u53ef\u7528\u6027<\/em>\u548c\u82f1\u91cc\/\u52a0\u4ed1<\/em>\u6570\u636e\u70b9\u7684\u6563\u70b9\u56fe<\/span><\/li>\n
- \u84dd\u7ebf\u8868\u793a\u62df\u5408\u56de\u5f52\u7ebf<\/span><\/li>\n
- \u7070\u8272\u7f6e\u4fe1\u5e26<\/span><\/li>\n
- \u7ea2\u8272\u9884\u6d4b\u5e26<\/span><\/li>\n<\/ul>\n
#define dataset<\/span>\ndata <- mtcars[, c(\"mpg\", \"disp\")]\n\n#create simple linear regression model\n<\/span>model <- lm(mpg ~ disp, data = mtcars)\n\n#use model to create prediction intervals\n<\/span>predictions <- predict(model, interval = \" predict<\/span> \")\n\n#create dataset that contains original data along with prediction intervals\n<\/span>all_data <- cbind(data, predictions)\n\n#load ggplot2<\/em> library\n<\/span>library(ggplot2)\n\n#createplot\n<\/span>ggplot(all_data, aes(x = disp, y = mpg)) + #define x and y axis variables<\/span>\n geom_point() + #add scatterplot points<\/span>\n stat_smooth(method = lm) + #confidence bands<\/span>\n geom_line(aes(y = lwr), col = \"coral2\", linetype = \"dashed\") + #lwr pred interval<\/span>\n geom_line(aes(y = upr), col = \"coral2\", linetype = \"dashed\") #upr pred interval<\/span><\/strong><\/pre>\n\u4f55\u65f6\u4f7f\u7528\u7f6e\u4fe1\u533a\u95f4\u4e0e\u9884\u6d4b\u533a\u95f4<\/span><\/strong><\/h2>\n\u9884\u6d4b\u533a\u95f4<\/strong>\u6355\u83b7\u5355\u4e2a\u503c\u7684\u4e0d\u786e\u5b9a\u6027\u3002\u7f6e\u4fe1\u533a\u95f4<\/strong>\u6355\u83b7\u9884\u6d4b\u5e73\u5747\u503c\u7684\u4e0d\u786e\u5b9a\u6027\u3002\u56e0\u6b64\uff0c\u5bf9\u4e8e\u76f8\u540c\u503c\uff0c\u9884\u6d4b\u533a\u95f4\u5c06\u59cb\u7ec8\u6bd4\u7f6e\u4fe1\u533a\u95f4\u66f4\u5bbd\u3002<\/span><\/p>\n\u5f53\u60a8\u5bf9\u7279\u5b9a\u7684\u4e2a\u4f53\u9884\u6d4b\u611f\u5174\u8da3\u65f6\uff0c\u5e94\u8be5\u4f7f\u7528\u9884\u6d4b\u533a\u95f4\uff0c\u56e0\u4e3a\u7f6e\u4fe1\u533a\u95f4\u4f1a\u4ea7\u751f\u592a\u7a84\u7684\u503c\u8303\u56f4\uff0c\u4ece\u800c\u5bfc\u81f4\u8be5\u533a\u95f4\u4e0d\u5305\u542b\u771f\u5b9e\u503c\u7684\u53ef\u80fd\u6027\u66f4\u5927\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u6709\u4e24\u4e2a\u7528\u9014\uff1a (1)\u91cf\u5316\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u4e0e\u54cd\u5e94\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002 (2)\u4f7f\u7528\u6a21\u578b\u9884\u6d4b\u672a\u6765\u503c\u3002 \u5173\u4e8e […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-529","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n
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