A Logistic Regression Model with a Hierarchical Random Error Term for Analyzing the Utilization of Public Transport

<div>Mean of the samples of <svg height="12.2532pt" id="M276" style="vertical-align:-3.29108pt" version="1.1" viewbox="-0.0498162 -8.96212 18.7225 12.2532" width="18.7225pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M600 0V24L339 665L311 657L43 24V0H600ZM497 50H107L303 539L497 50Z" id="g113-133"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="641" vert-adv-y="641"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,8.327,0)"><path d="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z" id="g113-85"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="637" vert-adv-y="637"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,15.503,3.132)"><path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z" id="g50-106"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="280" vert-adv-y="280"></glyph.data></g></svg> grouped by the value of CARS.</div>

Mathematical Problems in Engineering

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Figure 4

Figure 4: A Logistic Regression Model with a Hierarchical Random Error Term for Analyzing the Utilization of Public Transport 