迴歸分析[英 1]係統計模型上嘅一類技術,用嚟建立描述兩個或者以上唔同變數之間嘅關係嘅數學模型[7]:喺統計學上,研究者好多時會想用一個變數嘅數值嚟預測第啲變數嘅數值;喺最簡單嗰種情況下,個統計模型會涉及兩個連續[英 2]嘅變數,當中一個係自變數[英 3],而另一個就係應變數[英 4],而個研究者會用個 IV 嘅數值嚟預測個 DV 嘅數值;對個研究者嚟講,一個可能嘅做法係搜集啲數據返嚟,用啲數據做迴歸分析,整個模型(即係畫條線)出嚟,個模型就能夠幫佢預測「當 IV 係呢個數值嗰陣,假設第啲因素不變,個 DV 嘅數值會傾向係幾多」[8][9]。迴歸模型有以下呢啲[10]:
每個隱藏因素都有個箭咀指住反映佢嘅變數,而箭咀掕住嘅數字就係嗰個隱藏因素同嗰個指標變數之間嘅因素負荷量,例如 scale 1 呢個變數係智能呢個隱藏因素嘅指標,兩者之間嘅因素負荷量係 0.7;而 high school GPA(高中成績)係學力表現呢個隱藏因素嘅指標,兩者之間嘅因素負荷量係 0.75。如果一個指標變數嘅因素負荷量有返咁上下低,通常研究者就會覺得噉表示個變數根本反映唔到個隱藏因素,會考慮將嗰個變數由個模型嗰度攞走。
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