+ Descriptives T-Tests ANDVA Mixed Models Regression Frequentes Factor Audit Bain Distributions JAGS Machine Learning Meta-Analysis Netw Model summary Linear Mixed Models Deviance Deviance [ REMLY log Lik. di NC BIG Dependent variable ill income_reg 6242 3458 4203 1090 -3101.5546 11 0225 1030 6204. 8503 cent_income_reg More. The model was fined using restricted maximum likelihood equality_reg Fixed effects variables studentstaffRatio sch cent_academicAch Fixed Effects Estimates prestige_sch cent_Intelligence anxiety Estimate HE di P anx ely.caleg Intercept 20355 4135 514 3065 287.7628 155 0807 385350-280 anxiety.ordinal cant academicAch 1040 0509 07 3880 2RD 6476 10 7710 5 60908 -23 all cent_arkiety cant_intelligence -19 5673 15 5571 280 8136 -0 4030 0.6873 cent_academicAch * cent_intelligence 1.9089 19486 295.9734 D.4017 28002 parsonity.agr cent_personality.agr Random effects groping factors Wore. The intercept comesponds to the (unweighted) grand moon; for each factor with k levels, k - 1 parameters arc estimated Consequently, the estimates cannot be directly mapped to factor levels. personality.con region cent_personality.con personality.ext Variance/Correlation Estimates cent_personality ext Intelligence indian: Variance Estimates atd. Deviation Variance Intercept 6.9539 18. 4981 T Mode cent_academicAch 1.2790 1.8380 cent_intelligence U.5624 0.3183 Model components Fled Affects Note The Intercept corresponds to the (urweighted) cent_academicAch cent_academicAch grand mean; for each factor with k levels, k - 1 cent_ntellgence scent_intelligence parameters Are estimated Consequently, the estimates cannot be directly mapped to factor levels. cent_academicAch * cent_Intelliga region: Comelation Estimates Term Intercept cant_academieAch CAM_Intelligence Intercept 1 0000 cent_academicAch 00141 1.0000 cent_intelligence 0.4546 -0.8970 1.0000 Random effects Mom Tine Intercept corresponds to the (urwalgady grand maan; for each factor