LAMPIRAN UJI VALIDITAS
|
|
|
- Candace Hood
- 5 years ago
- Views:
Transcription
1 LAMPIRAN UJI VALIDITAS 1
2 e1 e2,00 Q1,04 Q2,01 -,04 e3 Q3,00 -,21 -,09 e4 Q4,49 -,01 e5 Q5,70,50,71 e6 Q6,47,69,74 e7 Q7,54,41,08 e8 Q8,17 e9 Q9,01 e10 Q10 Kompetensi Guru Chi-Square = 78,430 Probabilitas =,000 DF = 35 Gambar 1. Uji Validitas Kompetisi Guru,50 e5 Q5,53,70,73 e6 Q6,46,68 e7 Q7,73,53 e8 Q8 Kompetensi Guru Chi-Square = 7,718 Probabilitas =,005 DF = 1 Gambar 2. Uji Validitas Kompetisi Guru Tahap 2 e11 e12,39,22 e13 Q13,23,15,62 e14 Q14,31,38,56 e15 Q15,54,74 e16 Q16 -,23,05,71 e17 Q17,13,51 e18 e19 Q11 Q12 Q18,05,05,02 Q19 Gaji Chi-Square = 51,696 Probabilitas =,003 DF = 27 Gambar 3. Uji Validitas Variabel Gaji e13 Q13 e15 Q15,56,64,80 e16 Q16 e18 Q18,40,31,64,42,63 Gaji Chi-Square = 3,939 Probabilitas =,140 DF = 2 Gambar 4. Uji Validitas Variabel Gaji tahap 2 2
3 e20 e21 e22 e23 e24 Q24,68,53,73 e25 Q25,12,35,46 e26 Q26,21,38,19 e27 Q27,15 e28 e29 Q20,08 Q21,43 Q22,39 Q23,47 Q28,04 Q29,00,04,28,66,63 Kepemimpinan Chi-Square = 64,717 Proababilitas =,002 DF = 35 Gambar 5. Uji Validitas Kepemimpinan Tahap 1 e22,70 Q22,40,84,63 e23 Q23,41,64 e24 Q24,61,38 e25 Q25 Kepemimpinan Kepala Sekolah Chi-Square =-20,678 Probabilitas = \p DF = \df Gambar 6. Uji Validitas Kepemimpinan Tahap 2 e30,49 Q30 e31,42 Q31,53,70,65 e32 Q32,56,73 e33 Q33,75,44,67 e34 Q34,02,15,43 e35 Q35,19,43 e36 Q36,19 e37 Q37 Kinerja Guru Chi-square = 113,467 Probabilitas =,000 DF = 20 Gambar 7. Uji Validitas Kinerja guru Tahap 1 3
4 e30 Q30,50,50,71 e31 Q31,56,71,75 e32 Q32,54,74 e33 Q33,62,38 e34 Q34 Kinerja Guru Chi-Sguare = 16,718 Probabilitas =,005 DF = 5 Gambar 7. Uji Validitas Kinerja guru Tahap 2 e38,41,65 e39 Q39,55,64,74 e40 e41 Q38 Q40 Q41 e42 Q42,43,38,61,70,49 Profesionalisme guru Chi-Square = 24,959 Proabilitas =,000 DF = 5 Gambar 9. Uji Validitas Profesionalisme 4
5 LAMPIRAN UJI RELIABILITAS 5
6 Estimate ME ej Q1 <--- Kompetensi guru 0,694 0,481 0,519 Q4 <--- Kompetensi guru 0,766 0,587 0,413 Q5 <--- Kompetensi guru 0,653 0,427 0,573 Q6 <--- Kompetensi guru 0,589 0,347 0,653 2,702 7,301 2,158 Q8 <--- Gaji 0,566 0,321 0,679 Q10 <--- Gaji 0,692 0,479 0,521 Q11 <--- Gaji 0,644 0,415 0,585 Q12 <--- Gaji 0,675 0,455 0,545 2,577 6,641 2,33 Q15 <--- Kepemimpinan 0,835 0,698 0,302 Q16 <--- Kepemimpinan 0,675 0,455 0,545 Q17 <--- Kepemimpinan 0,666 0,444 0,556 Q18 <--- Kepemimpinan 0,659 0,434 0,566 2,835 8,037 1,969 Q20 <--- Kinerja guru 0,605 0,366 0,634 Q21 <--- Kinerja guru 0,63 0,397 0,603 Q22 <--- Kinerja guru 0,651 0,424 0,576 Q23 <--- Kinerja guru 0,649 0,421 0,579 Q24 <--- Kinerja guru 0,524 0,275 0,725 3,059 9,357 3,117 Q26 <--- Profesionalisme 0,638 0,408 0,592 Q27 <--- Profesionalisme 0,793 0,628 0,372 Q28 <--- Profesionalisme 0,648 0,42 0,580 Q29 <--- Profesionalisme 0,639 0,408 0,592 Q30 <--- Profesionalisme 0,551 0,304 0,696 Q33 <--- Profesionalisme 0,530 0,281 0,719 3,799 14,432 3,551 Kompetensi guru 0,7719 Gaji 0,7403 Kepemimpinan 0,8032 Kinerja guru 0,7501 Profesionalisme 0,8025 6
7 LAMPIRAN KONSTRUK AWAL 7
8 MODEL KONSTRUK AWAL e5,47 Q5,56,69 e6 Q6,75,47,69 e7 Q7,52,72 e8 Q8 e13 e15 e16 e18 e22 e23 e24 e25,42 Q13,30 Q15,63 Q16,42 Q18,80,65,65,55 kompetensi gaji,65 Q22,42,81 Q23,65,43,65 kepemimpinan Q24,40,63 Q25 e30,42 Q30 e31 e32,39,47,51,34 Q31 Q32 Q33 Q34 Kinerja Guru,42 e33,65,63,69,72,58,29,35,38,43,22 z1,33,31 e34 z2,58 Profesio nalisme,58 Q38,69,76,83 Q39,42,65 Q40,63,40,54 Q41,29 Q42 Chi-Square = 278,753 Probabilitas =,000 DF = 202 AGFI =,756 GFI =,805 RMSEA =,062 TLI =,900 CFI =,912 CMIN/DF = 1,380 e38 e39 e40 e41 e42 MODEL KONSTRUK REVISI e5,47 Q5,50,69 e6 Q6,71,50,71 e7 Q7,54,73 e8 Q8 e13 e15 e16 e18 e22 e23 e24 e25,44 Q13,30 Q15,57 Q16,45 Q18,76,67,66,55 kompetensi gaji,61,70,72 Q22,39,84 Q23,62,40,63 kepemimpinan Q24,40,63 Q25 e30,49 Q30 e31 e32,46,54,58,41 Q31 Q32 Q33 Q34 Kinerja Guru,55 e33,70,68,74,76,64,20,27,32,59,32,14 z1,25,32 e34 z2,69 Profesio nalisme,66 Q38,76,81,87 Q39,51,71 Q40,70,49,60 Q41,36 Q42 Chi-Square = 206,833 Probabilitas =,337 DF = 199 AGFI =,806 GFI =,848 RMSEA =,020 TLI =,990 CFI =,991 CMIN/DF = 1,039 e38 e39 e40 e41 e42 8
9 Modul Konstruk Awal Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Default model , ,000 1,380 Saturated model 253,000 0 Independence model , ,000 4,784 RMR, GFI Model RMR GFI AGFI PGFI Default model,100,805,756,643 Saturated model,000 1,000 Independence model,180,255,184,233 Baseline Comparisons Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Default model,748,712,915,900,912 Saturated model 1,000 1,000 1,000 Independence model,000,000,000,000,000 Parsimony-Adjusted Measures Model PRATIO PNFI PCFI Default model,874,654,798 Saturated model,000,000,000 Independence model 1,000,000,000 NCP Model NCP LO 90 HI 90 Default model 76,753 36, ,680 Saturated model,000,000,000 Independence model 874, , ,481 9
10 FMIN Model FMIN F0 LO 90 HI 90 Default model 2,816,775,372 1,259 Saturated model,000,000,000,000 Independence model 11,164 8,830 7,822 9,914 RMSEA Model RMSEA LO 90 HI 90 PCLOSE Default model,062,043,079,139 Independence model,196,184,207,000 AIC Model AIC BCC BIC CAIC Default model 380, , , ,616 Saturated model 506, , , ,108 Independence model 1149, , , ,508 ECVI Model ECVI LO 90 HI 90 MECVI Default model 3,846 3,443 4,330 4,158 Saturated model 5,111 5,111 5,111 6,658 Independence model 11,608 10,600 12,692 11,743 HOELTER Model HOELTER HOELTER Default model Independence model
11 Covariances: (Group number 1 - Default model) M.I. Par Change gaji <--> kepemimpinan 30,005,153 kompetensi <--> kepemimpinan 21,458,125 kompetensi <--> gaji 20,675,139 e41 <--> e42 9,206,105 e38 <--> e40 7,160 -,071 e38 <--> e39 5,152,049 e33 <--> e34 9,795,078 e22 <--> gaji 9,239,093 e22 <--> kompetensi 5,510,070 e24 <--> e38 4,795 -,062 e24 <--> e31 4,237,060 e13 <--> kepemimpinan 4,617,057 e16 <--> e22 4,122,061 e18 <--> kompetensi 6,545,089 e7 <--> e33 8,541,073 11
12 MODEL REVISI Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Default model , ,337 1,039 Saturated model 253,000 0 Independence model , ,000 4,784 RMR, GFI Model RMR GFI AGFI PGFI Default model,028,848,806,667 Saturated model,000 1,000 Independence model,180,255,184,233 Baseline Comparisons Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Default model,813,783,991,990,991 Saturated model 1,000 1,000 1,000 Independence model,000,000,000,000,000 Parsimony-Adjusted Measures Model PRATIO PNFI PCFI Default model,861,700,854 Saturated model,000,000,000 Independence model 1,000,000,000 NCP Model NCP LO 90 HI 90 Default model 7,833,000 46,179 Saturated model,000,000,000 Independence model 874, , ,481 FMIN Model FMIN F0 LO 90 HI 90 Default model 2,089,079,000,466 12
13 Model FMIN F0 LO 90 HI 90 Saturated model,000,000,000,000 Independence model 11,164 8,830 7,822 9,914 RMSEA Model RMSEA LO 90 HI 90 PCLOSE Default model,020,000,048,961 Independence model,196,184,207,000 AIC Model AIC BCC BIC CAIC Default model 314, , , ,512 Saturated model 506, , , ,108 Independence model 1149, , , ,508 ECVI Model ECVI LO 90 HI 90 MECVI Default model 3,180 3,101 3,567 3,510 Saturated model 5,111 5,111 5,111 6,658 Independence model 11,608 10,600 12,692 11,743 HOELTER Model HOELTER HOELTER Default model Independence model
14 Modification Indices (Group number 1 - Default model) Covariances: (Group number 1 - Default model) M.I. Par Change gaji <--> kepemimpinan 30,005,153 kompetensi <--> kepemimpinan 21,458,125 kompetensi <--> gaji 20,675,139 e41 <--> e42 9,206,105 e38 <--> e40 7,160 -,071 e38 <--> e39 5,152,049 e33 <--> e34 9,795,078 e22 <--> gaji 9,239,093 e22 <--> kompetensi 5,510,070 e24 <--> e38 4,795 -,062 e24 <--> e31 4,237,060 e13 <--> kepemimpinan 4,617,057 e16 <--> e22 4,122,061 e18 <--> kompetensi 6,545,089 e7 <--> e33 8,541,073 Variances: (Group number 1 - Default model) M.I. Par Change Regression Weights: (Group number 1 - Default model) M.I. Par Change Q42 <--- Q41 5,033,171 Q41 <--- Q42 6,208,232 Q41 <--- Q5 4,560 -,183 Q33 <--- Q34 5,995,171 Q33 <--- Q7 6,980,175 Q22 <--- gaji 9,239,373 Q22 <--- kompetensi 5,510,290 Q22 <--- Q38 4,701,175 Q22 <--- Q15 7,418,160 Q22 <--- Q16 9,864,236 Q22 <--- Q5 5,924,187 Q22 <--- Q8 6,615,206 Q25 <--- Q18 4,028,153 Q13 <--- kepemimpinan 4,617,288 Q18 <--- kompetensi 6,545,372 14
15 M.I. Par Change Q18 <--- Profesio_nalisme 6,433,340 Q18 <--- Q39 5,747,221 Q18 <--- Q25 4,307,189 Q18 <--- Q5 6,951,238 Q18 <--- Q7 4,986,209 Q5 <--- Q18 4,549,157 Q7 <--- Kinerja_Guru 4,302,324 Q7 <--- Q33 10,123,300 15
16 Estimates (Group number 1 - Default model) Scalar Estimates (Group number 1 - Default model) Maximum Likelihood Estimates Regression Weights: (Group number 1 - Default model) Estimate S.E. C.R. P Label Kinerja_Guru <--- kompetensi,227,106 2,142,032 par_18 Kinerja_Guru <--- gaji,297,122 2,434,015 par_20 Kinerja_Guru <--- kepemimpinan,377,134 2,819,005 par_21 Profesio_nalisme <--- kompetensi,365,136 2,682,007 par_19 Profesio_nalisme <--- kepemimpinan,386,167 2,309,021 par_22 Profesio_nalisme <--- Kinerja_Guru,407,194 2,095,036 par_23 Profesio_nalisme <--- gaji,227,140 1,629,103 par_24 Q8 <--- kompetensi 1,000 Q7 <--- kompetensi,962,159 6,040 *** par_1 Q6 <--- kompetensi 1,061,187 5,675 *** par_2 Q5 <--- kompetensi,996,177 5,642 *** par_3 Q18 <--- gaji 1,000 Q16 <--- gaji 1,155,202 5,711 *** par_4 Q15 <--- gaji 1,015,238 4,261 *** par_5 Q13 <--- gaji,838,176 4,751 *** par_6 Q25 <--- kepemimpinan 1,000 Q24 <--- kepemimpinan 1,029,207 4,971 *** par_7 Q23 <--- kepemimpinan,815,162 5,033 *** par_8 Q22 <--- kepemimpinan 1,344,234 5,744 *** par_9 Q32 <--- Kinerja_Guru,994,151 6,597 *** par_10 Q33 <--- Kinerja_Guru 1,079,168 6,439 *** par_11 Q38 <--- Profesio_nalisme 1,000 Q39 <--- Profesio_nalisme 1,127,112 10,073 *** par_12 Q40 <--- Profesio_nalisme,863,117 7,351 *** par_13 Q41 <--- Profesio_nalisme,922,128 7,185 *** par_14 Q31 <--- Kinerja_Guru,993,161 6,155 *** par_15 Q30 <--- Kinerja_Guru 1,000 Q34 <--- Kinerja_Guru,980,179 5,484 *** par_16 Q42 <--- Profesio_nalisme,686,112 6,101 *** par_17 Standardized Regression Weights: (Group number 1 - Default model) Estimate Kinerja_Guru <--- kompetensi,287 16
17 Estimate Kinerja_Guru <--- gaji,383 Kinerja_Guru <--- kepemimpinan,433 Profesio_nalisme <--- kompetensi,348 Profesio_nalisme <--- kepemimpinan,334 Profesio_nalisme <--- Kinerja_Guru,307 Profesio_nalisme <--- gaji,221 Q8 <--- kompetensi,718 Q7 <--- kompetensi,686 Q6 <--- kompetensi,746 Q5 <--- kompetensi,686 Q18 <--- gaji,647 Q16 <--- gaji,796 Q15 <--- gaji,545 Q13 <--- gaji,646 Q25 <--- kepemimpinan,631 Q24 <--- kepemimpinan,654 Q23 <--- kepemimpinan,649 Q22 <--- kepemimpinan,806 Q32 <--- Kinerja_Guru,689 Q33 <--- Kinerja_Guru,717 Q38 <--- Profesio_nalisme,759 Q39 <--- Profesio_nalisme,833 Q40 <--- Profesio_nalisme,651 Q41 <--- Profesio_nalisme,634 Q31 <--- Kinerja_Guru,626 Q30 <--- Kinerja_Guru,648 Q34 <--- Kinerja_Guru,584 Q42 <--- Profesio_nalisme,539 Variances: (Group number 1 - Default model) Estimate S.E. C.R. P Label kompetensi,240,066 3,639 *** par_25 gaji,250,079 3,181,001 par_26 kepemimpinan,199,063 3,151,002 par_27 z1,088,028 3,178,001 par_28 z2,112,032 3,548 *** par_29 e8,226,045 5,003 *** par_30 e7,250,047 5,369 *** par_31 e6,215,046 4,652 *** par_32 e5,269,049 5,437 *** par_33 17
18 Estimate S.E. C.R. P Label e18,348,062 5,611 *** par_34 e16,194,053 3,656 *** par_35 e15,610,100 6,106 *** par_36 e13,245,045 5,467 *** par_37 e25,301,051 5,907 *** par_38 e24,282,049 5,777 *** par_39 e23,182,031 5,776 *** par_40 e22,194,050 3,907 *** par_41 e30,209,036 5,857 *** par_42 e31,230,038 5,971 *** par_43 e32,165,030 5,564 *** par_44 e33,165,032 5,241 *** par_45 e38,196,036 5,464 *** par_46 e39,149,034 4,377 *** par_47 e40,269,043 6,187 *** par_48 e41,335,054 6,204 *** par_49 e34,279,046 6,129 *** par_50 e42,305,047 6,524 *** par_51 Squared Multiple Correlations: (Group number 1 - Default model) Estimate Kinerja_Guru,417 Profesio_nalisme,578 Q42,290 Q34,341 Q41,402 Q40,424 Q39,693 Q38,575 Q33,515 Q32,474 Q31,392 Q30,419 Q22,649 Q23,421 Q24,428 Q25,398 Q13,418 Q15,297 Q16,633 18
19 Estimate Q18,418 Q5,470 Q6,557 Q7,471 Q8,516 Matrices (Group number 1 - Default model) Total Effects (Group number 1 - Default model) kepemimpin kompete Kinerja_Gur Profesio_nalis gaji an nsi u me Kinerja_Guru,377,297,227,000,000 Profesio_nalis me,540,348,458,407,000 Q42,370,239,314,279,686 Q34,369,291,222,980,000 Q41,498,321,422,376,922 Q40,466,301,395,352,863 Q39,608,393,516,459 1,127 Q38,540,348,458,407 1,000 Q33,407,321,245 1,079,000 Q32,374,295,226,994,000 Q31,374,295,225,993,000 Q30,377,297,227 1,000,000 Q22 1,344,000,000,000,000 Q23,815,000,000,000,000 Q24 1,029,000,000,000,000 Q25 1,000,000,000,000,000 Q13,000,838,000,000,000 Q15,000 1,015,000,000,000 Q16,000 1,155,000,000,000 Q18,000 1,000,000,000,000 Q5,000,000,996,000,000 Q6,000,000 1,061,000,000 Q7,000,000,962,000,000 Q8,000,000 1,000,000,000 Standardized Total Effects (Group number 1 - Default model) kepemimpina n gaji kompete nsi Kinerja_Gur u Profesio_nalis me 19
20 kepemimpina kompete Kinerja_Gur Profesio_nalis gaji n nsi u me Kinerja_Guru,433,383,287,000,000 Profesio_nalis me,467,339,436,307,000 Q42,252,182,235,165,539 Q34,253,224,168,584,000 Q41,296,215,277,195,634 Q40,304,220,284,200,651 Q39,389,282,363,256,833 Q38,355,257,331,233,759 Q33,311,275,206,717,000 Q32,298,264,198,689,000 Q31,271,240,180,626,000 Q30,280,248,186,648,000 Q22,806,000,000,000,000 Q23,649,000,000,000,000 Q24,654,000,000,000,000 Q25,631,000,000,000,000 Q13,000,646,000,000,000 Q15,000,545,000,000,000 Q16,000,796,000,000,000 Q18,000,647,000,000,000 Q5,000,000,686,000,000 Q6,000,000,746,000,000 Q7,000,000,686,000,000 Q8,000,000,718,000,000 Direct Effects (Group number 1 - Default model) kepemimpin kompet Kinerja_Gur Profesio_nalis gaji an ensi u me Kinerja_Guru,377,297,227,000,000 Profesio_nalis me,386,227,365,407,000 Q42,000,000,000,000,686 Q34,000,000,000,980,000 20
21 kepemimpin kompet Kinerja_Gur Profesio_nalis gaji an ensi u me Q41,000,000,000,000,922 Q40,000,000,000,000,863 Q39,000,000,000,000 1,127 Q38,000,000,000,000 1,000 Q33,000,000,000 1,079,000 Q32,000,000,000,994,000 Q31,000,000,000,993,000 Q30,000,000,000 1,000,000 Q22 1,344,000,000,000,000 Q23,815,000,000,000,000 Q24 1,029,000,000,000,000 Q25 1,000,000,000,000,000 Q13,000,838,000,000,000 Q15,000 1,015,000,000,000 Q16,000 1,155,000,000,000 Q18,000 1,000,000,000,000 Q5,000,000,996,000,000 Q6,000,000 1,061,000,000 Q7,000,000,962,000,000 Q8,000,000 1,000,000,000 Standardized Direct Effects (Group number 1 - Default model) kepemimpina kompete Kinerja_Gur Profesio_nalis gaji n nsi u me Kinerja_Guru,433,383,287,000,000 Profesio_nalis me,334,221,348,307,000 Q42,000,000,000,000,539 Q34,000,000,000,584,000 Q41,000,000,000,000,634 Q40,000,000,000,000,651 Q39,000,000,000,000,833 Q38,000,000,000,000,759 Q33,000,000,000,717,000 Q32,000,000,000,689,000 Q31,000,000,000,626,000 Q30,000,000,000,648,000 Q22,806,000,000,000,000 Q23,649,000,000,000,000 21
22 kepemimpina kompete Kinerja_Gur Profesio_nalis gaji n nsi u me Q24,654,000,000,000,000 Q25,631,000,000,000,000 Q13,000,646,000,000,000 Q15,000,545,000,000,000 Q16,000,796,000,000,000 Q18,000,647,000,000,000 Q5,000,000,686,000,000 Q6,000,000,746,000,000 Q7,000,000,686,000,000 Q8,000,000,718,000,000 Indirect Effects (Group number 1 - Default model) kepemimpina kompete Kinerja_Gur Profesio_nalis gaji n nsi u me Kinerja_Guru,000,000,000,000,000 Profesio_nalis me,153,121,092,000,000 Q42,370,239,314,279,000 Q34,369,291,222,000,000 Q41,498,321,422,376,000 Q40,466,301,395,352,000 Q39,608,393,516,459,000 Q38,540,348,458,407,000 Q33,407,321,245,000,000 Q32,374,295,226,000,000 Q31,374,295,225,000,000 Q30,377,297,227,000,000 Q22,000,000,000,000,000 Q23,000,000,000,000,000 Q24,000,000,000,000,000 Q25,000,000,000,000,000 Q13,000,000,000,000,000 Q15,000,000,000,000,000 Q16,000,000,000,000,000 Q18,000,000,000,000,000 Q5,000,000,000,000,000 Q6,000,000,000,000,000 Q7,000,000,000,000,000 Q8,000,000,000,000,000 22
23 Standardized Indirect Effects (Group number 1 - Default model) kepemimpina kompete Kinerja_Gur Profesio_nalis gaji n nsi u me Kinerja_Guru,000,000,000,000,000 Profesio_nalis me,133,118,088,000,000 Q42,252,182,235,165,000 Q34,253,224,168,000,000 Q41,296,215,277,195,000 Q40,304,220,284,200,000 Q39,389,282,363,256,000 Q38,355,257,331,233,000 Q33,311,275,206,000,000 Q32,298,264,198,000,000 Q31,271,240,180,000,000 Q30,280,248,186,000,000 Q22,000,000,000,000,000 Q23,000,000,000,000,000 Q24,000,000,000,000,000 Q25,000,000,000,000,000 Q13,000,000,000,000,000 Q15,000,000,000,000,000 Q16,000,000,000,000,000 Q18,000,000,000,000,000 Q5,000,000,000,000,000 Q6,000,000,000,000,000 Q7,000,000,000,000,000 Q8,000,000,000,000,000 23
24 LAMPIRAN FREKUENSI DATA 24
25 Frekuensi Identitas Responden Statistics Jenis_kelamin Umur Pendidikan_Terak hir N Valid Missing Frequency Table Jenis_kelamin Valid Laki-Laki Perempuan Umur Valid
26 Pendidikan_Terakhir Valid D S S-2 (Pascasarjana)
27 Frekuensi Variabel Kompetensi guru N Statistics Valid Missing Mean Median Mode Sum Q Q Q Q Q Q Q Q Q Q Kompetensi Frequency Table Q1 Valid
28 Q2 Valid Q3 Valid Q4 Valid
29 Q5 Valid Q6 Valid Q7 Valid
30 Q8 Valid Q9 Valid Q10 Valid
31 Kompetensi Valid
32 Frekuensi Variabel Gaji Statistics N Valid Missing Mean Median Mode Sum Q Q Q Q Q a 365 Q Q Q Q Gaji a 3444 a. Multiple modes exist. The smallest value is shown Frequency Table Q11 Valid
33 Q12 Valid Q13 Valid Q14 Valid
34 Q15 Valid Q16 Valid Q17 Valid
35 Q18 Valid Q19 Valid
36 Gaji Valid
37 Frekuensi Variabel Kepemimpinan Statistics N Valid Missing Mean Median Mode Sum Q Q Q Q Q Q Q Q Q Q Kepemimpinan Frequency Table Q20 Valid
38 Q21 Valid Q22 Valid Q23 Valid
39 Q24 Valid Q25 Valid Q26 Valid
40 Q27 Valid Q28 Valid Q29 Valid
41 Kepemimpinan Valid
42 Frekuensi Variabel Kinerja guru Statistics N Valid Missing Mean Median Mode Sum Q Q Q Q Q Q Q Q Kinerja Frequency Table Q30 Valid
43 Q31 Valid Q32 Valid Q33 Valid
44 Q34 Valid Q35 Valid Q36 Valid
45 Q37 Valid Kinerja Valid
46 Frekuensi Variabel Profesionalisme N Statistics Valid Missing Mean Median Mode Sum Q Q Q Q Q Profesionalisme Frequency Table Q38 Valid Q39 Valid
47 Q40 Valid Q41 Valid Q42 Valid
48 Profesionalisme Valid
MODUL PELATIHAN SEM ANANDA SABIL HUSSEIN, PHD
MODUL PELATIHAN SEM ANANDA SABIL HUSSEIN, PHD PUSAT KAJIAN DAN PENGABDIAN MASYARAKAT JURUSAN MANAJEMEN UNIVERSITAS BRAWIJAYA 2018 1) 2) ANALISA JALUR 1) LMK = 27,209 3,599IPK + 1,749 x 10-7 US + 0,019JR
TRY OUT 30 Responden Variabel Kompetensi/ x1
1 TRY OUT 30 Responden Variabel Kompetensi/ x1 Case Processing Summary N % 30 100.0 Cases Excluded a 0.0 Total 30 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
UJI VALIDITAS DAN RELIABILIAS VARIABEL KOMPENSASI
1 UJI VALIDITAS DAN RELIABILIAS VARIABEL KOMPENSASI Case Processing Summary N % 20 100.0 Cases Excluded a 0.0 Total 20 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
Lampiran IV. Hasil Output SPSS Versi 16.0 untuk Analisis Deskriptif
182 Lampiran IV. Hasil Output SPSS Versi 16.0 untuk Analisis Deskriptif Frequencies Statistics Kinerja Guru Sikap Guru Thdp Kepsek Motivasi Kerja Guru Kompetensi Pedagogik Guru N Valid 64 64 64 64 Missing
TRY OUT 25 Responden Variabel Kepuasan / x1
1 TRY OUT 25 Responden Variabel Kepuasan / x1 Case Processing Summary N % 25 100.0 Cases Excluded a 0.0 Total 25 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
. Enter. Model Summary b. Std. Error. of the. Estimate. Change. a. Predictors: (Constant), Emphaty, reliability, Assurance, responsive, Tangible
LAMPIRAN Variables Entered/Removed b Variables Model Variables Entered Removed Method 1 Emphaty, reliability, Assurance, responsive, Tangible a. Enter a. All requested variables entered. b. Dependent Variable:
HASIL OUTPUT SPSS. Reliability Scale: ALL VARIABLES
139 HASIL OUTPUT SPSS Reliability Scale: ALL VARIABLES Case Processing Summary N % 100 100.0 Cases Excluded a 0.0 Total 100 100.0 a. Listwise deletion based on all variables in the procedure. Reliability
LAMPIRAN. Lampiran 1. Data deviden untuk menghitung economic performance tahun
LAMPIRAN Lampiran 1. Data deviden untuk menghitung economic performance tahun 2011-2013 No Kode 2011 2012 2013 Div Div Div 1 SMCB 23 32 48 2 UNVR 250 300 300 3 AMFG 80 80 80 4 INTP 263 293 450 5 ICBP 116
SEM over time. Changes in Structure, changes in Means
SEM over time Changes in Structure, changes in Means Measuring at two time points Is the structure the same Do the means change (is there growth) Create the data x.model
LAMPIRAN 1. Lampiran Nama dan Kondisi Perusahaan Textile No Kode Nama Perusahaan Hasil z-score FD Non-FD
87 LAMPIRAN 1. Lampiran Nama dan Kondisi Perusahaan Textile 2010-2014 No Kode Nama Perusahaan Hasil z-score FD Non-FD 1 ADMG PT Polychem Indonesia Tbk 1,39 1 2 ARGO PT Argo Pantes Tbk 0,93 1 3 CTNX PT
Lampiran 1. Penjualan PT Honda Mandiri Bogor
LAMPIRAN 64 Lampiran 1. Penjualan PT Honda Mandiri Bogor 29-211 PENJUALAN 29 TYPE JAN FEB MAR APR MEI JUNI JULI AGT SEP OKT NOV DES TOTA JAZZ 16 14 22 15 23 19 13 28 15 28 3 25 248 FREED 23 25 14 4 13
Lampiran 1. Data Perusahaan
Lampiran. Data Perusahaan NO PERUSH MV EARN DIV CFO LB.USAHA TOT.ASS ACAP 3 9 8 5 369 9678 376 ADES 75-35 - 6 3559-5977 7358 3 AQUA 5 368 65 335 797 678 53597 BATA 88 5 9 863 958 93 5 BKSL 5.3 -. 9-9 5
The influence of age and gender of student motorcycle riders on traffic violations and accidents using a structural equation model
The influence of age and gender of student motorcycle riders on traffic violations and accidents using a structural equation model I Wayan Suteja 1,*, Mifta Holman 1, D.M. Priyantha Wedagama 2, and P.
Lampiran i Jadwal Penelitian
Lampiran i Jadwal Penelitian Tahap penelitian Juni Juli Agust Sept Oktb Pengajuan Judul Penyetujuan proposal Penyelesain proposal Bimbingan skripisi 81 Lampiran i (lanjutan) Daftar Sampel Perusahaan Manufaktur
DATA PENELITIAN 1. CAR CAR (%)
DATA PENELITIAN. CAR No. Tahun Nama Bank CAR (%) Arta Niaga Kencana 2,8 2 Artha Graha 0,58 3 Asiatic -9,9 4 Danpac 25,74 5 Global International 42, 6 Harmoni 7,47 7 IFI 22,62 8 Bukopin 20,37 9 International
LAMPIRAN 1. Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari Februari 2012
LAMPIRAN 1 Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari 2011 29 Februari 2012 No Tanggal Indeks Harga Saham No Tanggal Indeks Harga Saham 1 20-Jan-011 2.35 138 05-Agst-011 1.95 2
Bayes Factors. Structural Equation Models (SEMs): Schwarz BIC and Other Approximations
Bayes Factors in Structural Equation Models (SEMs): Schwarz BIC and Other Approximations Kenneth A. Bollen University of North Carolina, Chapel Hill Surajit Ray SAMSI and University of North Carolina,
Lampiran 1. Uji Validitas dan Reliability Variabel Kualitas Pelayanan
Lampira 1. Uji itas da Reliability Variabel Kualitas Pelayaa 1 Frequecy Percet Percet Percet Kurag setuju 14 14.1 14.1 14.1 Ragu-ragu 57 57.6 57.6 71.7 Setuju 24 24.2 24.2 96.0 Sagat Setuju 4 4.0 4.0 100.0
Universitas Sumatera Utara
LAMPIRAN I LAMPRIAN PDRB Harga Berlaku NO KAB/KOTA 2005 2006 2007 2008 2009 2010 1 Asahan 15527794210 6429147880 8174125380 9505603030 10435935630 11931676610 2 Dairi 2303591460 2552751860 2860204810 3116742540
Lampiran 1. Tabel Sampel Penelitian
Lampiran 1 Tabel Sampel Penelitian No Kode Emiten Nama Perusahaan Tanggal IPO 1 APLN Agung Podomoro Land Tbk 11 Nov 2010 2 ASRI Alam Sutera Reality Tbk 18 Dec 2007 3 BAPA Bekasi Asri Pemula Tbk 14 Jan
Stat 301 Lecture 30. Model Selection. Explanatory Variables. A Good Model. Response: Highway MPG Explanatory: 13 explanatory variables
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)
LAMPIRAN DAFTAR SAMPEL PENELITIAN. Kriteria No. Nama Perusahaan. Sampel Emiten
LAMPIRAN DAFTAR SAMPEL PENELITIAN Kode Kriteria No. Nama Perusahaan Sampel Emiten 1 2 3 1. AGRO PT. Bank Agroniaga, Tbk 1 2. BABP PT. Bank ICB Bumiputera Indonesia, Tbk X - 3. BBCA PT. Bank Central Asia,
Daftar Sampel Perusahaan Pertambangan. 4 BORN Borneo Lumbung Energy & Metal, Tbk
Lampiran i Daftar Sampel Perusahaan Daftar Sampel Perusahaan Pertambangan No Kode Sampel 1 ADRO Adaro Energy, Tbk 2 ANTM Aneka Tambang (Persero), Tbk 3 ATPK ATPK Resources, Tbk 4 BORN Borneo Lumbung Energy
Review of Upstate Load Forecast Uncertainty Model
Review of Upstate Load Forecast Uncertainty Model Arthur Maniaci Supervisor, Load Forecasting & Energy Efficiency New York Independent System Operator Load Forecasting Task Force June 17, 2011 Draft for
DATA SAMPEL TAHUN 2006
DATA SAMPEL TAHUN 2006 No Nama Emiten CGPI Kode Saham Harga Saham EPS PER Laba Bersih 1 Bank Niaga 89.27 BNGA 920 54 17.02 647,732 2 Bank Mandiri 83.66 BMRI 2,900 118 24.65 2,422,472 3 Astra International
KUESIONER PENELITIAN BAGIAN 1
LAMPIRAN 48 49 Lampiran 1. Kuesioner Penelitian KUESIONER PENELITIAN Kuesioner ini digunakan sebagai bahan untuk menyusun skripsi mengenai Pengaruh Gaya Kepemimpinan Kepala Cabang terhadap Partisipasi
Daftar Sampel Perusahaan
Lampiran i Daftar Sampel Perusahaan NAMA PERUSAHAAN PT. Bank Bukopin Tbk PT. Bank Bumi Arta Tbk PT. Bank Central Asia Tbk PT. Bank CIMB Niaga Tbk PT. Bank Danamon Indonesia Tbk PT. Bank Ekonomi Raharja
Basic SAS and R for HLM
Basic SAS and R for HLM Edps/Psych/Soc 589 Carolyn J. Anderson Department of Educational Psychology c Board of Trustees, University of Illinois Spring 2019 Overview The following will be demonstrated in
Performance of the Mean- and Variance-Adjusted ML χ 2 Test Statistic with and without Satterthwaite df Correction
FORDHAM UNIVERSITY THE JESUIT UNIVERSITY OF NEW YORK Performance of the Mean- and Variance-Adjusted ML χ 2 Test Statistic with and without Satterthwaite df Correction Jonathan M. Lehrfeld Heining Cham
Perusahaan Consumer Goods yang Terdaftar di BEI ( ) Nama Perusahaan Perusahaan 1
LAMPIRAN 1 Perusahaan Consumer Goods yang Terdaftar di BEI (2010-2012) No. Sub Sektor Kode Nama Perusahaan Perusahaan 1 ADES Akasha Wira Internasional Tbk 2 AISA Tiga Pilar Sejahtera Food Tbk 3 ALTO Tri
R-Sq criterion Data : Surgical room data Chap 9
Chap 9 - For controlled experiments model reduction is not very important. P 347 - For exploratory observational studies, model reduction is important. Criteria for model selection p353 R-Sq criterion
Rata-Rata Nilai Debt to Equity Ratio (DER) Perusahaan Otomotif yang 0, ,97 0, ,44 1,9 1,6 1,4 1,7 1,65
Lampiran I Rata-Rata Nilai Debt to Equity Ratio (DER) Perusahaan Otomotif yang Terdaftar di Bursa Efek Indonesia Periode 2010-2013 DER No Kode Nama perusahaan 2010 2011 2012 2013 Rata-rata 1. ASII PT Astra
LAMPIRAN I Data Perusahaan Sampel kode DPS EPS Ekuitas akpi ,97 51,04 40,
LAMPIRAN I Data Perusahaan Sampel kode DPS EPS Ekuitas 2013 2014 2015 2013 2014 2015 2013 2014 2015 akpi 34 8 9 50,97 51,04 40,67 1.029.336.000.000 1.035.846.000.000 1.107.566.000.000 asii 216 216 177
Lampiran 1 DAFTAR PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN (Menyajikan Laporan Keuangan Secara Berturut-turut)
Lampiran 1 DAFTAR PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2014-2016 (Menyajikan Laporan Keuangan Secara Berturut-turut) NO KODE NAMA PERUSAHAAN 1 ALKA PT ALASKA INDONESIA TBK
LAMPIRAN. Lampiran 1 Data Sampel Penelitian
LAMPIRAN Lampiran Data Sampel Penelitian Variabel Karakteristik Auditor pada Perusahaan Sampel Ukuran KAP No Kode 2 2 22 23 (Aryanto, Amir AGRO Jusuf, Mawar & Saptoto) 2 BABP 3 BACA 4 BAEK 5 BBCA 6 BBKP
5. CONSTRUCTION OF THE WEIGHT-FOR-LENGTH AND WEIGHT-FOR- HEIGHT STANDARDS
5. CONSTRUCTION OF THE WEIGHT-FOR-LENGTH AND WEIGHT-FOR- HEIGHT STANDARDS 5.1 Indicator-specific methodology The construction of the weight-for-length (45 to 110 cm) and weight-for-height (65 to 120 cm)
Daftar Nama Perusaahan. yang ada di Industri Consumer Goods periode
99 Lampiran 1 Daftar Nama Perusaahan yang ada di Industri Consumer Goods periode 2013-2016 KODE AISA ALTO CEKA CLEO DLTA ICBP INDF HOKI MLBI MYOR PSDN ROTI SKBM SKLT STTP ULTJ GGRM HMSP RMBA WIIM CINT
fruitfly fecundity example summary Tuesday, July 17, :13:19 PM 1
fruitfly fecundity example summary Tuesday, July 17, 2018 02:13:19 PM 1 The UNIVARIATE Procedure Variable: fecund line = NS Basic Statistical Measures Location Variability Mean 33.37200 Std Deviation 8.94201
Author s Accepted Manuscript
Author s Accepted Manuscript Dataset on statistical analysis of Jet A-1 fuel laboratory properties for on-spec into-plane operations Aderibigbe Israel Adekitan, Tobi Shomefun, Temitope M. John, Bukola
LAMPIRAN A. Tabulasi Data Perusahaan Sample
LAMPIRAN A Tabulasi Data Perusahaan Sample Current Ratio (%) NO Kode Emiten Nama Perusahaan ASII Astra International Tbk. 2 AUTO Astra Otoparts Tbk. 3 BATA Sepatu Bata Tbk. 4 BRAM Indo Kordsa Tbk 5 BRNA
Stat 301 Lecture 26. Model Selection. Indicator Variables. Explanatory Variables
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan There is an indicator for Pickup but there are no pickups
THERMOELECTRIC SAMPLE CONDITIONER SYSTEM (TESC)
THERMOELECTRIC SAMPLE CONDITIONER SYSTEM (TESC) FULLY AUTOMATED ASTM D2983 CONDITIONING AND TESTING ON THE CANNON TESC SYSTEM WHITE PAPER A critical performance parameter for transmission, gear, and hydraulic
PDF hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is a publisher's version. For additional information about this publication click this link. http://hdl.handle.net/2066/112075
Team project 2017 Dony Pratidana S. Hum Bima Agus Setyawan S. IIP
Hak cipta dan penggunaan kembali: Lisensi ini mengizinkan setiap orang untuk menggubah, memperbaiki, dan membuat ciptaan turunan bukan untuk kepentingan komersial, selama anda mencantumkan nama penulis
Lampiran 1. Daftar Sampel Perusahaan
Lampiran 1. Daftar Sampel Perusahaan NO. KODE NAMA PERUSAHAAN 1 ARNA Arwana Citramulia Tbk. 2 ASII Astra Internastional Tbk. 3 AUTO Astra otoparts Tbk. 4 BTON Betonjaya Manunggal Tbk 5 DVLA Darya-Varia
Professor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh
Statistic Methods in in Data Mining Business Understanding Data Understanding Data Preparation Deployment Modelling Evaluation Data Mining Process (Part 2) 2) Professor Dr. Gholamreza Nakhaeizadeh Professor
ACCIDENT MODIFICATION FACTORS FOR MEDIAN WIDTH
APPENDIX G ACCIDENT MODIFICATION FACTORS FOR MEDIAN WIDTH INTRODUCTION Studies on the effect of median width have shown that increasing width reduces crossmedian crashes, but the amount of reduction varies
Guatemalan cholesterol example summary
Guatemalan cholesterol example summary Wednesday, July 11, 2018 02:04:06 PM 1 The UNIVARIATE Procedure Variable: level = rural Basic Statistical Measures Location Variability Mean 157.0204 Std Deviation
Regression Models Course Project, 2016
Regression Models Course Project, 2016 Venkat Batchu July 13, 2016 Executive Summary In this report, mtcars data set is explored/analyzed for relationship between outcome variable mpg (miles for gallon)
Multivariate Twin Analysis. OpenMx 2012 Hermine Maes & Elizabeth Prom-Wormley
Multivariate Twin Analysis OpenMx 2012 Hermine Maes & Elizabeth Prom-Wormley Copy Files l.dat winmulaceconnl_yours.r winmulaceconnl.r Multivariate Saturated Model equality of means/variances Genetic Models
Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR
Tutorial 1 Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR Dataset for running Correlated Component Regression This tutorial 1 is based on data provided by Michel Tenenhaus and
LAMPIRAN I FORMULIR SURVEI
LAMPIRAN I FORMULIR SURVEI 56 Universitas Kristen Maranatha L.1.1 FORMULIR SURVEI KEBISINGAN LALULINTAS Lokasi : Cuaca : Hari/Tanggal : Surveyor : Periode / menit 5 10 15 20 25 30 35 40 45 50 55 60 65
Antonio Olmos Priyalatha Govindasamy Research Methods & Statistics University of Denver
Antonio Olmos Priyalatha Govindasamy Research Methods & Statistics University of Denver American Evaluation Association Conference, Chicago, Ill, November 2015 AEA 2015, Chicago Ill 1 Paper overview Propensity
Structural Equation Modeling On the Calculation of Motorcycle Ownership Index Using Amos Software
IOSR Journal of Business and Management (IOSR-JBM) e-issn: 2278-487X, p-issn: 2319-7668. Volume 20, Issue 4. Ver. IV (April. 2018), PP 35-43 www.iosrjournals.org Structural Equation Modeling On the Calculation
Model Information Data Set. Response Variable (Events) Summe Response Variable (Trials) N Response Distribution Binomial Link Function
02:32 Donnerstag, November 03, 2016 1 Model Information Data Set WORK.EXP Response Variable (Events) Summe Response Variable (Trials) N Response Distribution inomial Link Function Logit Variance Function
Data Nama Perusahaan Perbankan Yang Terdaftar Di BEI.
LAMPIRAN Lampiran 1 Data Nama Perusahaan Perbankan Yang Terdaftar Di BEI. No Kode Perusahaan Nama Perusahaan 1 AGRO Bank Rakyat Indonesia Agroniaga Tbk 2 BMRI Bank Mandiri (Persero) Tbk 3 BMAS PT Bank
Lampiran 1 Tabel 3.1 Sampel No. Kode Nama Perusahaan Sumber : didownload tanggal 13 November 2016 (data diolah)
72 Lampiran 1 Tabel 3.1 Sampel No. Kode Nama Perusahaan 1 ALKA PT. Alakasa Industrindo Tbk 2 BIMA PT. Primarindo Asia Infrastructure Tbk 3 BRNA PT. Berlina Tbk 4 ETWA PT. Eterindo Wahanatama Tbk 5 FASW
The Session.. Rosaria Silipo Phil Winters KNIME KNIME.com AG. All Right Reserved.
The Session.. Rosaria Silipo Phil Winters KNIME 2016 KNIME.com AG. All Right Reserved. Past KNIME Summits: Merging Techniques, Data and MUSIC! 2016 KNIME.com AG. All Rights Reserved. 2 Analytics, Machine
CHAPTER V CONCLUSION, SUGGESTION AND LIMITATION. 1. Independent commissioner boards proportion does not negatively affect
CHAPTER V CONCLUSION, SUGGESTION AND LIMITATION 5.. Conclusion Based on data analysis that has been done, researcher may draw following conclusions:. Independent commissioner boards proportion does not
From Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT. Full book available for purchase here.
From Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT. Full book available for purchase here. About this Book... ix About the Author... xiii Acknowledgments...xv Chapter 1 Introduction...
tool<-read.csv(file="d:/chilo/regression 7/tool.csv", header=t) tool
Regression nalysis lab 7 1 Indicator variables 1.1 Import data tool
Quarterly Market Detail - Q Townhouses and Condos Miami-Fort Lauderdale-West Palm Beach MSA
ly Market Detail - Q3 218 Summary Statistics Q3 218 Q3 217 Paid in Cash 11,55 9,91 11.7% 5,712 5,554 2.8% $19, $18, 5.6% Average Sale Price Dollar Volume $281,57 $264,562 6.2% $3.1 Billion $2.6 Billion
Stat 401 B Lecture 31
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)
Appendix B STATISTICAL TABLES OVERVIEW
Appendix B STATISTICAL TABLES OVERVIEW Table B.1: Proportions of the Area Under the Normal Curve Table B.2: 1200 Two-Digit Random Numbers Table B.3: Critical Values for Student s t-test Table B.4: Power
Technical Papers supporting SAP 2009
Technical Papers supporting SAP 29 A meta-analysis of boiler test efficiencies to compare independent and manufacturers results Reference no. STP9/B5 Date last amended 25 March 29 Date originated 6 October
Lampiran 1: Nama Perusahaan yang menjadi Sampel Penelitian
LAMPIRAN Lampiran 1: Nama Perusahaan yang menjadi Sampel Penelitian NO. KODE NAMA PERUSAHAAN 1. INTA PT Intraco Penta Tbk 2. JKON PT Jaya Konstruksi Manggala Pratama Tbk 3. KONI PT Perdana Bangun Pusaka
Industry Classification/Stock Name
Lampiran 1 : Data Perusahaan Sampel Industry Classification/Stock Name 1. AGRICULTURE Plantation 1 AALI Astra Argo Lestari Tbk (S) 2 LSIP PP London Sumatera Tbk (S) 3 SGRO Sampoerna Agro Tbk (S) 4 SMAR
Master of Applied Statistics Applied Statistics Comprehensive Exam Computer Output January 2018
Question 1a output Master of Applied Statistics Applied Statistics Comprehensive Exam Computer Output January 2018 Question 1f output Basic Statistical Measures Location Variability Mean -2.32429 Std Deviation
Lecture 2. Review of Linear Regression I Statistics Statistical Methods II. Presented January 9, 2018
Review of Linear Regression I Statistics 211 - Statistical Methods II Presented January 9, 2018 Estimation of The OLS under normality the OLS Dan Gillen Department of Statistics University of California,
Drilling Example: Diagnostic Plots
Math 3080 1. Treibergs Drilling Example: Diagnostic Plots Name: Example March 1, 2014 This data is taken from Penner & Watts, Mining Information, American Statistician 1991, as quoted by Levine, Ramsey
Booklet of Code and Output for STAD29/STA 1007 Final Exam
Booklet of Code and Output for STAD29/STA 1007 Final Exam List of Figures in this document by page: List of Figures 1 Raisins data.............................. 2 2 Boxplot of raisin data........................
A REPORT ON THE STATISTICAL CHARACTERISTICS of the Highlands Ability Battery CD
A REPORT ON THE STATISTICAL CHARACTERISTICS of the Highlands Ability Battery CD Prepared by F. Jay Breyer Jonathan Katz Michael Duran November 21, 2002 TABLE OF CONTENTS Introduction... 1 Data Determination
Important Formulas. Discrete Probability Distributions. Probability and Counting Rules. The Normal Distribution. Confidence Intervals and Sample Size
blu38582_if_1-8.qxd 9/27/10 9:19 PM Page 1 Important Formulas Chapter 3 Data Description Mean for individual data: Mean for grouped data: Standard deviation for a sample: X2 s X n 1 or Standard deviation
DAFTAR LAMPIRAN. Daftar Perusahaan yang dijadikan sampel
83 DAFTAR LAMPIRAN Daftar Perusahaan yang dijadikan sampel No KODE Nama perusahaan 01 ALKA Alakasa Industrindo Tbk 02 ALMI Alumindo Light Metal Industri Tbk 03 AMFG Asahimas Flat Glass Tbk 04 ASTRA Astra
EXST7034 Multiple Regression Geaghan Chapter 11 Bootstrapping (Toluca example) Page 1
Chapter 11 Bootstrapping (Toluca example) Page 1 Toluca Company Example (Problem from Neter, Kutner, Nachtsheim & Wasserman 1996,1.21) A particular part needed for refigeration equipment replacement parts
In the Slow Lane: ZEV Markets in California, June 2014 to June 2017
In the Slow Lane: ZEV Markets in California, June 2014 to June 2017 STEPs Symposium Ken Kurani Plug-in Hybrid & Electric Vehicle Center Institute of Transportation Studies University of California, Davis
LAMPIRAN 1 : DAFTAR PERUSAHAAN SAMPEL PERIODE
69 LAMPIRAN 1 : DAFTAR PERUSAHAAN SAMPEL PERIODE 2008-2010 No Kode Nama Perusahaan 1 AISA PT Tiga Pilar Sejahtera Food Tbk 2 ARNA PT Arwana Citramulia Tbk 3 ASII PT Astra International Tbk 4 AUTO PT Astra
The Degrees of Freedom of Partial Least Squares Regression
The Degrees of Freedom of Partial Least Squares Regression Dr. Nicole Krämer TU München 5th ESSEC-SUPELEC Research Workshop May 20, 2011 My talk is about...... the statistical analysis of Partial Least
Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran
Statistics and Quantitative Analysis U4320 Segment 8 Prof. Sharyn O Halloran I. Introduction A. Overview 1. Ways to describe, summarize and display data. 2.Summary statements: Mean Standard deviation Variance
Investigation of Relationship between Fuel Economy and Owner Satisfaction
Investigation of Relationship between Fuel Economy and Owner Satisfaction June 2016 Malcolm Hazel, Consultant Michael S. Saccucci, Keith Newsom-Stewart, Martin Romm, Consumer Reports Introduction This
a. Uji kenormalan data model sebaran suhu pada band 7 citra tahun 2001 b. Uji kenormalan data model sebaran suhu pada band 4 citra tahun 2006
Dependent Variable: Suhu Regression Standardized Residual of Plot P-P Normal Dependent Variable: Suhu Regression Standardized Residual of Plot P-P Normal Lampiran 1. Hasil Uji Normalitas a. Uji kenormalan
Road Surface characteristics and traffic accident rates on New Zealand s state highway network
Road Surface characteristics and traffic accident rates on New Zealand s state highway network Robert Davies Statistics Research Associates http://www.statsresearch.co.nz Joint work with Marian Loader,
Table 2: Tests for No-Cointegration Empirical Rejection Frequency of 5% Tests
Table 2: Tests for No-Cointegration Empirical Rejection Frequency of 5% Tests EQ-TAR BAND-TAR c T ADF HW EG BVD ADF HW EG BVD 3 100 0.434 0.939 0.950 0.990 0.133 0.253 0.264 0.459 3 250 0.990 1 1 1 0.638
Volvo City Safety loss experience a long-term update
Highway Loss Data Institute Bulletin Vol. 32, No. 1 : April 2015 Volvo City Safety loss experience a long-term update This Highway Loss Data Institute (HLDI) report updates two prior bulletins on the Volvo
Climatography of the United States No
Climate Division: CA 5 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 45.9 26.0 36.0 67
Climatography of the United States No
Climate Division: MA 3 NWS Call Sign: BOS Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 36.5 22.1 29.3 72
Data Hasil Olahan Populasi
LAMPIRAN 1 Data Hasil Olahan Populasi No. Kode Bank Nama Bank Kriteria 1 Kriteria 2 Sampel terpilih 1. AGRO Bank Rakyat Indonesia Agro Niaga Tbk 2. AGRS Bank Agris Tbk X X 3. BABP Bank MNC Internasional
female male help("predict") yhat age
30 40 50 60 70 female male 1.0 help("predict") 0.5 yhat 0.0 0.5 1.0 30 40 50 60 70 age 30 40 50 60 70 1.5 1.0 female male help("predict") 0.5 yhat 0.0 0.5 1.0 1.5 30 40 50 60 70 age 2 Wald Statistics Response:
Climatography of the United States No
Climate Division: WY 9 NWS Call Sign: LND Temperature ( F) Month (1) Min (2) Month(1) Extremes Lowest (2) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 31.9 8.7 20.3
DEPARTMENT OF STATISTICS AND DEMOGRAPHY MAIN EXAMINATION, 2011/12 STATISTICAL INFERENCE II ST232 TWO (2) HOURS. ANSWER ANY mree QUESTIONS
I.. UNIVERSITY OF SWAZILAND Page 1 of3 DEPARTMENT OF STATISTICS AND DEMOGRAPHY, MAIN EXAMINATION, 2011/12 COURSE TITLE: STATISTICAL INFERENCE II COURSE CODE: ST232 TIME ALLOWED: TWO (2) HOURS INSTRUCTION:
The PRINCOMP Procedure
Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, 2010 1 Food production variables The PRINCOMP Procedure Observations 16 Variables 4 Simple Statistics PRECIP ndvi aet temp Mean 260.8102476
Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data
Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data A Research Report Submitted to the Maryland State Department of Education (MSDE)
Summary Statistics. Closed Sales. Paid in Cash. Median Sale Price. Average Sale Price. Dollar Volume. Median Percent of Original List Price Received
ly Market Detail - May 218 Summary Statistics May 218 May 217 Paid in Cash 1,667 1,647 1.2% 888 943-5.8% $168, $159, 5.7% Average Sale Price Dollar Volume $231,288 $21,944 9.6% $385.6 Million $347.4 Million
Monthly Market Detail - June 2018 Single Family Homes Miami-Dade County
ly Market Detail - June 218 Summary Statistics June 218 June 217 Paid in Cash 1,335 1,346 -.8% 286 33-5.6% $355, $335, 6.% Average Sale Price Dollar Volume $598,494 $57,82 18.% $799. Million $682.5 Million
Monthly Market Detail - June 2018 Townhouses and Condos Miami-Dade County
ly Market Detail - June 218 Summary Statistics June 218 June 217 Paid in Cash 1,257 1,323-5.% 657 682-3.7% $24, $235, 2.1% Average Sale Price Dollar Volume $439,546 $384,319 14.4% $552.5 Million $58.5
Model Selection in Information Systems Research Using Partial Least Squares Based Structural Equation Modeling
Model Selection in Information Systems Research Using Partial Least Squares Based Structural Equation Modeling Journal: International Conference on Information Systems 2012 Manuscript ID: ICIS-0250-2012.R1
The Incubation Period of Cholera: A Systematic Review Supplement. A. S. Azman, K. E. Rudolph, D.A.T. Cummings, J. Lessler
The Incubation Period of Cholera: A Systematic Review Supplement A. S. Azman, K. E. Rudolph, D.A.T. Cummings, J. Lessler 1 Basic Model Our models follow the approach for analysis of coarse data from Reich
Preface... xi. A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content...
Contents Preface... xi A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content... xii Chapter 1 Introducing Partial Least Squares...
Applying Categorical Data Analysis to Multi-way Contingency Table Location, Accident Type, and Related Factors With Severity
Applying Categorical Data Analysis to Multi-way Contingency Table Location, Accident Type, and Related Factors With Severity Li wan Chen, LENDIS Corporation, McLean, VA David L. Harkey, Highway Safety
Motor Trend MPG Analysis
Motor Trend MPG Analysis SJ May 15, 2016 Executive Summary For this project, we were asked to look at a data set of a collection of cars in the automobile industry. We are going to explore the relationship