>dev . new ( ) plot (effect (term="RESTIME3GR" , mod=model . Im, default . levels=20) , rescale . axis=FALSE) V rm( 'model. Im' ) descriptive . table (vars = d (ATTIME3) , + strata = d (RESTIME3GR) , data= ASS2_12, + func. names =c ("Mean", "St. Deviation", "Valid N", "Minimum", "Maximum") ) Descriptive Statistics Variable: ATTIME3 St. RESTIME3GR Mean Deviation Valid N Minimum Maximum High 8.600 7.41 35 -12.00 21.00 W N - Low -0.853 8.91 34 -21.00 12.00 Moderate 2.324 7.31 34 -15.00 14.00 > model .Im F) RESTIME 3GR 1497 . 2 2 2.895 . 45 Residuals 4165 . 8 100 Signif. codes: 0 1 *#* 1 0. 001 1 ** ' 0. 01 ( #' 0. 05 ' . ' 0.1 ' 1 summaryin (model. Anova (model . Im, type= 'III' ) Anova Table (Type III tests) Response: ATTIME3 Sum Sq Df F value Pr () F) (Intercept) 1423.0 1 25. 0788 2. 465e-06 * * * RESTIME 3GR 77.8 2 0. 6856 0.506208 ANXIETY3 636.7 1 11.2210 0.001153 * * RESTIME 3GR : ANXIETY3 73.7 2 0. 6497 0.524474 Residuals 5503.8 97 Signif. codes: ( '* **1 0.001 1 * *1 0.01 ' *1 0.05 ' . ' 0.1 ' > summarylm (model. Im)\fSimultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: Im(formula = ATTIME3 - RESTIME3GR + ANXIETY3, data = ASS2_12, na action = na.omit) Linear Hypotheses: Estimate Std. Error f value sig. Moderate - Low == 0 1.990 1.853 1.074 0.2854 High - Low = = 0 5.785 2.096 2.760 0.0069 High - Moderate = = 0 3.795 1.945 1.951 0.0539 Note: *p=.001, "p<.01 .p p values reported> library (effects) > effect (term="RESTIME3GR", mod=model. Im) RESTIME3GR effect Low 0.785 Moderate 2.775 High 6.570Q2. Students were partitioned into 3 relatively equal groups. Based on their statistical resourcefulness at time 3 (RESTIME3) scores, determine whether these groups differ in their attitude towards statistics at time 3 (ATTIME3)( FIRST ANOVA). If the influence of the student's anxiety at time 3 (ANXIETY3) is removed, do the differences in attitude towards statistics (ATTIME3) still exist among the resourcefulness groups (ANCOVA)? Where it is applicable, explain the post hoc multiple comparison test taught in workshop. Interpret your findings. Was controlling for ANXIETY3 worth doing? Why or why not? > descriptive . table (vars = d (RESTIME3, ANXIETY3, ATTIME3) , data= ASS2_12, + func. names =c ("Mean", "St. Deviation", "Valid N", "Minimum", "Maximum") ) Descriptive Statistics St. Mean Deviation Valid N Minimum Maximum RESTIME3 130.20 22.01 103 74.00 182.00 ANXIETY3 11.74 5.64 103 0.00 24.00 ATTIME3 3,41 8,77 103 -21.00 21.00 > frequencies (ASS2 12 [c ( "RESTIME3") ] Exdigits Frequencies Frequencies Frequencies ( RESTIME3 ) Value # of Cases % Cumulative % 74 1.00 1.00 76 1.00 1.90 87 1.00 2.90 4 93 1.00 3.90 94 1.00 4.90 98 1.00 5.80 99 1.00 6.80 100 1.00 7.80 101 1.00 8.70100 1.00 7.80 9 101 1.00 8.70 10 102 1.00 9.70 11 104 1.90 11.70 12 105 1.00 12.60 13 107 1.90 14.60 14 108 1.00 15.50 15 111 1.90 17.50 16 112 1.90 19.40 17 113 1.90 21.40 18 114 1.00 22.30 19 114.48 1.00 23.30 20 115 1.00 24.30 21 115.48 1.00 25.20 22 116 1.90 27.20 23 117 1.90 29.10 24 118 2.90 32.00 25 118.67 1.00 33.00 26 119 1.90 35.00 27 120 1.00 35.90 28 121 1.00 36.90 29 122 2.90 39.80 30 123 1.90 41.70 31 124 1.90 43.70 32 126 1.00 44.70 33 127 1.90 46.60 34 128 1.90 48.50 35 129 1.90 50.50 36 129.51 1.00 51.50 37 130 1.00 52.40 38 131 1.00 53.40 39 132 3.90 57.30 40 133 2.90 60.20 41 134 1.00 61.20 42 136 1.90 63.10 43 137 1.00 64.10 44 138 1.00 65.00 45 139 1.00 66.00 46 140 1.90 68.00 47 141 2.90 70.90 48 142 1.00 71.80143 1.90 73.80 No 145 1.00 74.80 147 2.90 77.70 148 1.00 78.60 150 1.00 79.60 151 1.00 80.60 152 1.00 81.60 56 153 1.90 83.50 57 153.04 1.00 84.50 58 155 1.00 85.40 59 156 1.00 86.40 60 157 1.00 87.40 61 158 1.00 88.30 159.04 1.00 89.30 63 160 1.00 90.30 64 161 1.00 91.30 65 162 1.00 92.20 66 164.5 1.00 93.20 67 166 1.90 95.10 68 169 1.00 96.10 69 172 1.00 97.10 70 173 1.00 98.10 71 174 1.00 99.00 72 182 1.00 100.00 Case Summary ( RESTIME3 ) Valid Missing Total % Missing 1 103.00 0.00 103.00 0.00 > ASS2_12 [c ("RESTIMESGR"] ] 'Low':119:139 -> 'Moderate':140:183 -> 'High';") frequencies (A552_12 [c ("RESTIME3GR"] ] , r.digits = 1) Frequencies Frequencies ( RESTIME3GR ) # of Value Cases Cumwative % High 35 34.00 34.00 Low 34 33.00 67.00 W N Moderate 33.00 100.00 Case Summary ( RESTIME3GR ) Vald Missing Total Missing 103.00 14.00 117.00 12.00\f\f\f