Pengujian Asumsi Klasik Analisis Verifikatif

þÿ ✁✂ ✄ ÿ ☎✆✝ ✞✟ ✠✟✝✆ ✡ ✆ÿ ✠ ☛ÿ ✠ ✞✟ ☞ ÿ ✌ÿ☎ÿ ✠ ✍ ✎ ✏

4.2.2 Analisis Verifikatif

✑✒✓✒✔✕✖ ✗✘✙ ✚✕ ✘ ✛ ✕ ✜ ✢✕✣✤ ✕✚✕ ✜ ✗ ✕✓✕ ✥ ✕✚ ✘ ✕ ✤ ✒✔ ✦ ✒ ✜ ✒ ✔ ✘✓ ✘ ✕ ✜ ✧ ★ ✒✔✕ ✜ ✩✙ ✓ ✜ ✪✕ ✙✜ ✓ ✙ ✛ ✣ ✒ ✜ ✢ ✙ ✩✘ ✦✒ ✜ ✢✕✚ ✙ ✖ ✫✬ ✭ ✮ ✯ ✬ ✰✱✲ ✗ ✕ ✜ ✦✒✣✤✘ ✕✪✕ ✕ ✜ ✳✴ ✯ ✬ ✵✬ ✮ ✬ ✮ ✓✒✚✖✕✗ ✕✦ ✦ ✒ ✜ ✗ ✕✦✕✓✕ ✜ ✳✬ ✯ ✶ ✱ ✷ ✳✴ ✯ ✬ ✵✬ ✮ ✬ ✮ ✤ ✕ ✘ ✛ ★ ✒ ✸ ✕✚✕ ★ ✘ ✣✙ ✔✓✕ ✜ ✣ ✕ ✙ ✦✙✜ ✦ ✕✚ ★ ✘ ✕✔ ✧ ✗ ✘ ✢ ✙ ✜ ✕✛ ✕ ✜ ✕ ✜ ✕✔ ✘★ ✘★ ✚ ✒✢✚✒ ★ ✘ ✤ ✒✚ ✢✕ ✜ ✗✕ ✹ ✺ ✒ ✜ ✢ ✙ ✩✘ ✕ ✜ ✕✛ ✕ ✜ ✗✘✔✕✛✙ ✛ ✕ ✜ ✣ ✒✔✕✔ ✙✘ ✓✕✖ ✕✦✕ ✜ ★ ✒ ✤ ✕ ✢✕ ✘ ✤ ✒✚✘ ✛ ✙ ✓ ✻ ✺ ✒ ✜ ✢ ✙✩✘ ✕ ✜ ✙ ✩✘ ✕ ★ ✙ ✣★ ✘ ✛ ✔✕ ★ ✘ ✛ ✧ ✕ ✜ ✕✔ ✘★ ✘★ ✚✒✢✚ ✒ ★ ✘ ✔ ✘✜✘ ✒✚✧ ✛✼ ✒✽ ✘★ ✘ ✒ ✜ ✛ ✼ ✚✒✔✕ ★ ✘ ✦ ✕✚ ★ ✘ ✕✔ ✧ ✛✼ ✒✽✘ ★ ✘ ✒ ✜ ✗✒✓✒ ✚✣ ✘✜ ✕ ★ ✘ ★ ✒✚ ✓✕ ✦✒ ✜ ✢ ✙ ✩✘ ✕ ✜ ✖ ✘ ✦✼ ✓✒ ★ ✘★ ✹ ✺ ✒ ✜ ✢ ✙✩✘ ✕ ✜ ✓✒✚★ ✒ ✤ ✙ ✓ ✗ ✘ ✔✕✛✙ ✛ ✕ ✜ ✗ ✒ ✜ ✢ ✕ ✜ ✤ ✕ ✜ ✓ ✙ ✕ ✜ ✭ ✲ ✾ ✰ ✿ ✬ ✯ ❀ ✑ ✺ ✑✑ ❁❂ ✗ ✕ ✜ ✙✜ ✓ ✙ ✛ ✔✒ ✤ ✘ ✖ ✩ ✒✔ ✕ ★ ✜ ✪✕ ✕✛ ✕ ✜ ✗ ✘✤ ✕✖✕ ★ ✤ ✒✚ ✘ ✛ ✙ ✓ ✘✜ ✘ ✹

4.2.2.1 Pengujian Asumsi Klasik

✑✒ ✤ ✒✔ ✙ ✣ ✗ ✘ ✔✕✛✙ ✛ ✕ ✜ ✦✒ ✜ ✢ ✙ ✩✘ ✕ ✜ ✖✘ ✦ ✼ ✓✒ ★ ✘★ ✣ ✒ ✜ ✢✢✙✜ ✕✛✕ ✜ ✕ ✜ ✕✔ ✘★ ✘★ ✚✒✢ ✚✒ ★ ★ ✘ ✔ ✘✜ ✘ ✒✚ ✤ ✒✚ ✢✕ ✜ ✗ ✕ ✧ ✕✗ ✕ ✤ ✒ ✤ ✒✚✕✦ ✕ ✕ ★ ✙ ✣ ★ ✘ ✪✕ ✜ ✢ ✖✕ ✚ ✙★ ✓✒✚✦ ✒ ✜✙ ✖ ✘ ✕✢ ✕✚ ✛✒ ★ ✘ ✣✦✙ ✔✕ ✜ ✗✕✚ ✘ ✚ ✒✢✚✒ ★ ★ ✘ ✓ ✒✚ ★ ✒ ✤✙ ✓ ✓ ✘ ✗✕✛ ✤ ✘ ✕ ★ ✧ ✗ ✘ ✕ ✜ ✓✕✚✕ ✜ ✪✕ ✕ ✗✕✔✕✖ ✙ ✩✘ ✜✼ ✚✣ ✕✔ ✘ ✓✕ ★ ✧ ✙ ✩✘ ✣✙ ✔✓ ✘ ✛✼ ✔ ✘✜ ✘ ✒✚ ✘ ✓✕ ★ ❃ ✙✜ ✓ ✙ ✛ ✚✒✢✚ ✒ ★ ★ ✘ ✔ ✘✜ ✒✕✚ ✤ ✒✚ ✢✕ ✜ ✗ ✕ ❄ ✧ ✙✩✘ ✖✒✓✒✚✼★ ✛ ✒✗✕ ★ ✓ ✘★ ✘ ✓✕ ★ ✗✕ ✜ ✙✩✘ ✕ ✙ ✓ ✼ ✛ ✼ ✚✒✔✕ ★ ✘ ❃ ✙ ✜ ✓ ✙ ✛ ✗ ✕✓✕ ✪✕✜ ✢ ✤ ✒✚ ✤ ✒ ✜ ✓ ✙ ✛ ✗✒✚ ✒✓ ❅✕✛ ✓ ✙ ❄✹ ✺ ✕✗ ✕ ✦ ✒ ✜ ✒✔ ✘ ✓ ✘ ✕ ✜ ✘✜ ✘ ✛ ✒✒✣✦ ✕✓ ✕ ★ ✙ ✣ ★ ✘ ✪✕ ✜ ✢ ✗✘★ ✒ ✤ ✙ ✓✛ ✕ ✜ ✗ ✘ ✕✓✕ ★ ✓✒✚ ★ ✒ ✤✙ ✓ ✗✘✙✩✘ ✛✕✚ ✒ ✜ ✕ ✥✕✚✘ ✕ ✤ ✒ ✔ ✤ ✒ ✤ ✕ ★ ✪✕ ✜ ✢ ✗ ✘ ✢ ✙✜ ✕✛ ✕ ✜ ✦✕✗ ✕ ✦ ✒ ✜ ✒✔ ✘ ✓ ✘ ✕ ✜ ✘✜ ✘ ✔✒ ✤✘ ✖ ✗ ✕✚ ✘ ★ ✕✓ ✙ ❃ ✤ ✒✚ ✢✕ ✜ ✗✕ ❄ ✗ ✕ ✜ ✗ ✕✓✕ ✪✕ ✜ ✢ ✗ ✘ ✛✙ ✣ ✦ ✙ ✔✛✕ ✜ ✣✒ ✜ ✢✕ ✜ ✗ ✙ ✜ ✢ ✙ ✜★ ✙ ✚ ✗✒✚ ✒✓ ❅✕✛ ✓ ✙ ❃❆ ✓ ✕✖✙ ✜ ✦✒ ✜ ✢✕✣✕✓✕ ✜ ❄✹ 1 Uji Asumsi Normalitas ❇ ★ ✙ ✣★ ✘ ✜ ✼ ✚✣ ✕✔ ✘ ✓✕ ★ ✣✒✚✙ ✦ ✕✛✕ ✜ ✦ ✒✚ ★ ✪✕✚✕✓✕ ✜ ✪✕ ✜ ✢ ★ ✕ ✜ ✢✕✓ ✦✒ ✜ ✓ ✘✜ ✢ ✦ ✕✗✕ ✦ ✒ ✜ ✢✙✩✘ ✕ ✜ ✛ ✒ ✤ ✒✚ ✣✕✛✜ ✕✕ ✜ ❃ ★ ✘ ✢ ✜ ✘ ✽ ✘ ✛✕ ✜ ★ ✘ ❄ ✛ ✼ ✒✽ ✘★ ✘ ✒ ✜ ✚ ✒✢✚✒ ★ ★ ✘✧ ✕✦✕ ✤ ✘ ✔✕ ✣ ✼ ✗✒✔ ✚✒✢ ✚✒ ★ ★ ✘ ✓ ✘ ✗ ✕✛ ✤ ✒✚✗✘★ ✓✚✘✤✙ ★ ✘ ✜✼ ✚✣✕✔ ✣ ✕✛✕ ✛✒ ★ ✘ ✣✦✙ ✔✕ ✜ ✗ ✕✚ ✘ ✙ ✩✘ ❈ ✗✕ ✜ ✙✩✘ ✓ ✣✕ ★ ✘ ✖ ✣✒✚✕✢ ✙ ✛ ✕ ✜✧ ✛ ✕✚✒ ✜ ✕ ★ ✓✕✓ ✘★ ✓ ✘ ✛ ✙✩✘ ❈ ✗✕ ✜ ✙✩✘ ✓ ✦ ✕✗✕ ✕ ✜ ✕✔ ✘★ ✘★ ✚ ✒✢✚ ✒ ★ ★ ✘ ✗✘ ✓ ✙ ✚ ✙ ✜ ✛ ✕ ✜ ✗ ✕✚ ✘ ✗✘★ ✓✚✘✤✙ ★ ✘ ❉❊ ❋ ●❍ ■ ❊ ❏❑▲ ▼◆ ❖◆▲❑ P ❑❊ ❖ ◗❊ ❖ ▼◆ ❘ ❋ ❊ ❙❊❏❊ ❖ ❚❚ ❯ ❱❲ ❳❨❩❬❭ ❪ ❩ ❫❩ ❴ ❵❱ ❵ ❬❛❜❛❩ ❱ ❛ ❱❛ ❫ ❛ ❝❞ ❱ ❩ ❡❩ ❱ ❞❢ ❛ ❣ ❩❜ ❞ ❣ ❩❨❴ ❵ ❬ ❤❲❬❨❲ ❝ ❲ ❳ ❲ ✐ ❥❦❨❛ ❳ ❱ ❲ ✐ ❞ ❱ ❜ ❞ ❡ ❨ ❵❱❝ ❞ ❢❛ ❱❲ ❳❨❩❬❛❜❩ ❣ ❨ ❲ ❫ ❵ ❬ ❳❵❝ ❳❵❣ ❣ ❛❭ Tabel 4.4 Hasil Pengujian Asumsi Normalitas One-Sample Kolmogorov-Smirnov Test 7 .0000000 43453.44152 .179 .179 -.115 .472 .979 N Mean Std. Deviation Normal Parameters a,b Absolute Positive Negative Most Extreme Differences Kolmogorov-Smirnov Z Asymp. Sig. 2-tailed Unstandardiz ed Residual Test distribution is Normal. a. Calculated from data. b. ❪ ❩ ❫❩ ❜❩ ❧ ❵ ❬ ♠❭ ♠ ❫ ❩❴ ❩❜ ❫ ❛❬❛ ♥❩❜ ❱❛ ❬❩❛ ❴ ❳❲ ❧❩ ❧ ❛❬❛❜❩ ❣ ♦ ❣ ❛ ❝❭♣ q❩ ❱ ❝ ❫ ❛❴ ❵❳ ❲❬ ❵♥ ❫ ❩ ❳ ❛ ❞ ❢❛ ❤❲ ❬❨❲ ❝ ❲ ❳❲✐ ❥❦❨❛ ❳ ❱ ❲ ✐ ❣ ❵❧ ❵❣ ❩ ❳ r st ✉t ❭ ❤❩ ❳ ❵❱ ❩ ❱❛❬❩❛ ❴ ❳❲ ❧❩ ❧ ❛❬❛❜❩ ❣ ❴❩ ❫❩ ❞ ❢❛ ❤❲ ❬❨❲ ❝ ❲ ❳❲✐ ❥❦❨❛ ❳ ❱ ❲ ✐ ❨❩ ❣ ❛ ♥ ❬ ❵❧ ❛ ♥ ❧ ❵❣ ❩ ❳ ❫❩ ❳ ❛ ❜❛ ❱❝ ❡ ❩❜ ❡❵❡ ❵ ❬❛ ❳❞❩ ❱ ✈ ✇ ♦ r ❭ r ✈ ♣ s ❨❩ ❡❩ ❫❛ ❣ ❛❨ ❴ ❞❬ ❡ ❩ ❱ ❧ ❩ ♥ ① ❩ ❨ ❲ ❫❵ ❬ ❳❵❝ ❳❵❣ ❣ ❛ ❧ ❵❳❫ ❛ ❣ ❜ ❳ ❛ ❧ ❞❣ ❛ ❱ ❲ ❳ ❨ ❩❬❭ ❦ ❵ ② ❩ ❳❩ ✐ ❛ ❣ ❞ ❩❬ ❝❩❨ ❧❩ ❳ ❝ ❳ ❩③❛ ❡ ❱ ❲ ❳ ❨ ❩❬ ❴ ❳❲ ❧❩ ❧ ❛❬❛❜ q ❴ ❬ ❲ ❜ ❫❩❴❩❜ ❫❛❬❛ ♥ ❩❜ ❴❩ ❫❩ ❝ ❩❨ ❧ ❩ ❳ ♠ ❭ ♠ ❧ ❵❳ ❛ ❡ ❞ ❜ ❭ ④⑤ ⑥ ⑦⑧ ⑨ ⑤ ⑩❶❷ ❸❹ ❺❹❷❶ ❻ ❶⑤ ❺ ❼⑤ ❺ ❸❹ ❽ ⑥ ⑤ ❾⑤⑩⑤ ❺ ❿❿❿ Observed Cum Prob 1.0 0.8 0.6 0.4 0.2 0.0 E x p e c te d C u m P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Normal P-P Plot of Regression Standardized Residual Dependent Variable: Y Gambar 4.4 Grafik Normalitas ➀➁➂➃➄➅ ➆➄ ➂ ➇ ➂ ➈ ➉➊ ➉➋➊ ➁ ➇➊ ➌ ➂ ➈ ➍ ➂➎➏ ➂ ➉ ➐ ➆➊➑ ➁ ➊ ➌➁ ➊➈ ➈ ➄ ➒➂➓ ➌ ➆➄ ➋ ➊ ➁ ➐ ➑➊ ➎ ➍ ➊ ➁➆➄ ➈ ➄➇ ➁ ➄➍ ➔➈ ➄ ➓➐ ➁ ➉➂ ➑→ ➆➄ ➉➂➓ ➂ ➈ ➊➍ ➂ ➁➂➓ ➆➂ ➇ ➂ ➍➊ ➁ ➂➆➂ ➆ ➄➈ ➊➅➄➇ ➂➁ ➌➂➁ ➄➈ ➆➄ ➂➌ ➐ ➓ ➂ ➑➣ 2 Uji Asumsi Multikolinieritas ↔ ➔ ➑➇➄➅➐ ➑➄ ➓ ➄➊ ➁➄➇➂ ➈ ➍➊ ➁ ➂➁ ➇➄ ➂➆➂➓ ➒➂ ➎➔➍ ➔ ➓ ➌➂➓ ➒➂➓ ➌ ➅ ➔ ➂ ➇ ➆➄ ➂➓➇ ➂➁➂ ➍ ➊➍➊ ➁ ➂➋➂ ➂ ➇ ➂ ➔ ➈ ➊ ➉ ➔ ➂ ↕ ➂➁ ➄ ➂ ➍ ➊➑ ➍ ➊➍ ➂ ➈ ➋ ➂➆➂ ➉ ➐ ➆ ➊➑ ➁ ➊ ➌➁➊➈ ➄➣ ➙ ➄➅ ➂ ➇➊ ➁➆➂➋ ➂ ➇ ↔ ➔ ➑➇➄➅➐ ➑➄ ➓ ➄➊ ➁➄ ➇ ➂ ➈ ➉ ➂ ➅ ➂ ➅➐ ➊ ➃ ➄➈ ➄➊ ➓ ➁ ➊ ➌➁ ➊➈ ➄ ➉ ➊ ➓➛ ➂➆ ➄ ➇➄ ➆➂ ➅ ➇➊ ➓ ➇➔ → ➇ ➄ ➓ ➌ ➅ ➂ ➇ ➅ ➊➈ ➂ ➑ ➂➎ ➂➓➓ ➒➂ ➉➊ ➓ ➛ ➂➆➄ ➈ ➂➓ ➌➂ ➇ ➍ ➊➈ ➂➁ ➆➂➓ ➍➄ ➂ ➈ ➂➓ ➒➂ ➆➄➇ ➂➓➆ ➂ ➄ ➆ ➊ ➓ ➌➂➓ ➓ ➄➑ ➂ ➄ ➅ ➐➊ ➃ ➄➈ ➄➊ ➓ ➆ ➊➇➊ ➁ ➉ ➄ ➓ ➂ ➈ ➄ ➒➂➓➌ ➈ ➂➓ ➌ ➂ ➇ ➍➊➈ ➂➁ ➇➊➇ ➂➋➄ ➋➂➆ ➂ ➋ ➊ ➓ ➌ ➔ ➛➄ ➂➓ ➋ ➂➁ ➈ ➄ ➂ ➑ ➅➐ ➊ ➃ ➄➈ ➄➊ ➓ ➁ ➊ ➌➁ ➊➈ ➄→ ➇➄ ➆ ➂ ➅ ➂➆ ➂ ➂ ➇ ➂ ➔ ➋➔ ➓ ➅ ➂ ➑ ➂ ➔ ➂➆ ➂ ➈ ➂➓ ➌➂ ➇ ➈ ➊ ➆ ➄➅ ➄➇ ➈ ➊➅ ➂ ➑➄ ➅ ➐➊ ➃ ➄➈ ➄➊ ➓ ➁ ➊ ➌➁ ➊➈ ➄ ➒➂➓ ➌ ➈ ➄ ➌➓ ➄ ➃ ➄➅ ➂➓ ➣ ➜ ➂➆ ➂ ➋➊ ➓ ➊➑➄➇➄ ➂➓ ➄ ➓➄ ➆➄ ➌ ➔ ➓ ➂ ➅ ➂➓ ➓ ➄➑ ➂ ➄ ➝➞➟ ➠➞➡ ➢➤ ➠ ➡ ➥➦ ➞➧➠➨➡ ➥ ➞➢➧➨➟ ➩ ➫ ➭➯➲➳ ➈ ➊➍ ➂ ➌➂ ➄ ➄ ➓➆➄➅ ➂ ➇➐ ➁ ➂➆➂ ➇➄ ➆ ➂ ➅ ➓ ➒➂ ➉ ➔➑➇➄➅ ➐➑➄ ➓➄➊ ➁ ➄➇ ➂ ➈ ➆➄ ➂➓ ➇ ➂➁➂ ↕ ➂➁➄ ➂ ➍➊➑ ➍ ➊➍ ➂ ➈ ➣ ➵➸ ➺ ➻➼ ➽ ➸ ➾➚➪ ➶➹ ➘➹➪➚ ➴ ➚➸ ➘ ➷➸ ➘ ➶➹ ➬ ➺ ➸ ➮➸➾➸ ➘ ➱➱ ✃ Tabel 4.5 Hasil Pengujian Asumsi Multikolinieritas Coefficients a .260 3.852 .260 3.852 X1 X2 Model 1 Tolerance VIF Collinearity Statistics Dependent Variable: Y a. ❐❒❮❰ ÏÐ Ï❮ ÑÏ Ò ÒÓÔ Ï Ó Õ Ö× ØÏ ÒÙ ❰ ÓÚ❒❮Û Ô❒Ü Ð ❒ Ú ❒❮ ÝÓ Ý ❒❮ ÔÓ Ü Ï Ý Ú Ï❰ Ï Ý Ï Þ❒ Ô ß àá ❰ Ó Ï Ý ÏÐ â ❒ Ò ã Ò äã ÑÑ Ï Ò ÝÓ ❰ÏÑ Ï❰Ï Ñ Û❮ ❒ Ô ÏÐ Ó ØÏ ÒÙ åã Ñ ã Ú Ñ ã Ï Ý Ï Ò Ý Ï❮Ï Ð ❒Ð Ï â Ï æ Ï❮ Ó Ï Þ❒ Ô Þ ❒ ÞÏÐ ç ❰ Ó â Ï ÒÏ ÒÓÔ Ï Ó Õ Ö× ❰ Ï❮ Ó Ñ ❒❰ ã Ï æ Ï❮ Ó Ï Þ ❒ Ô Þ❒ Þ ÏÐ Ô ❒ Þ Ó Ü Ñ❒ å ÓÔ ❰ Ï❮ Ó è é ❰Ï Ò ❰Ï ÚÏ Ý ❰ Ó Ð Ó â Ú ã Ô ÑÏ Ò ÝÓ ❰ ÏÑ Ý ❒❮ ❰Ï ÚÏ Ý âã ÔÝÓ ÑÛ ÔÓÒÓ ❒❮ ÓÝ ÏÐ ❰ Ó Ï ÒÝ Ï❮Ï Ñ❒❰ ã Ï æ Ï❮ Ó Ï Þ ❒ Ô Þ❒ ÞÏÐ à 3 Uji Asumsi Heteroskedastisitas ê ❒ Ý ❒❮Û Ð Ñ❒❰ ÏÐ ÝÓ Ð ÓÝ ÏÐ â ❒❮ ã ÚÏÑÏ Ò ÓÒ❰ Ó Ñ ÏÐ Ó æ Ï❮ Ó Ï Ò Ï Ò Ý Ï❮ ❮ ❒Ð Ó ❰ ã Ï Ô ÝÓ ❰ ÏÑ Ü Û â ÛÙ❒ Ò ØÏ ÒÙ â ❒ ÒÙÏÑ ÓÞ Ï Ý Ñ Ï Ò Ò ÓÔ Ï Ó Ý ÏÑÐ Ó ❮Ï Ò ØÏ ÒÙ ❰ ÓÚ ❒❮ Û Ô ❒Ü ÝÓ ❰ÏÑ ❒ë Ó Ð Ó ❒ Ò à ì ÒÝ ã Ñ â ❒ Ò Ù ã ä Ó Ü Û â Û Ù❒ Ò Ó Ý ÏÐ æ Ï❮ Ó Ï Ò ❰Ï❮ Ó ❮ ❒Ð Ó ❰ ã Ï Ô ❰ ÓÙ ã Ò ÏÑ Ï Ò ãä Ó ❮ Ï Ò Ñ í îïð ñ òð óç ØÏ ÓÝ ã ❰ ❒ Ò Ù Ï Ò â ❒ ÒÙÑ Û❮ ❒ Ô ÏÐ Ó ÑÏ Ò æ Ï❮ Ó Ï Þ❒ Ô Þ ❒ ÞÏÐ Ý ❒❮ ÜÏ❰ Ï Ú Ò ÓÔ Ï Ó Ï Þ Ð Û Ô ã Ý ❰Ï❮ Ó ❮❒Ð Ó ❰ ã Ï Ôô ❒❮ ❮Û ❮õ à ö ÚÏ ÞÓÔ Ï Ñ Û❒ë Ó Ð Ó ❒ Ò Ñ Û❮ ❒ Ô ÏÐ Ó ❰Ï❮ Ó â ÏÐ ÓÒÙ ÷ â ÏÐ ÓÒ Ù æ Ï❮ Ó Ï Þ ❒ Ô ÓÒ ❰ ❒ Ú ❒ Ò❰❒ Ò Ï ❰Ï ØÏ ÒÙ Ð Ó Ù Ò Ó ë Ó ÑÏ Ò ÚÏ❰Ï ÝÓÒ ÙÑ Ï Ý Ñ ❒Ñ❒ ÔÓ ❮ ã Ï Ò á ç â ❒ ÒÙ ÓÒ❰ Ó Ñ ÏÐ Ó Ñ Ï Ò Ï❰Ï ÒØÏ Ü ❒ Ý ❒❮ÛÐ Ñ ❒❰ÏÐ ÝÓ Ð ÓÝ ÏÐ à ø Ï❰Ï Ý Ï Þ ❒ Ô ß à ù Þ ❒❮ Ó Ñ ã Ý ❰Ï ÚÏ Ý ❰ ÓÔÓ ÜÏ Ý Ò ÓÔ Ï Ó Ð Ó Ù Ò Ó ë Ó ÑÏ ÒÐ Ó â ÏÐ ÓÒ Ù ÷ â ÏÐ ÓÒÙ Ñ Û❒ë Ó Ð Ó ❒ Ò ÑÛ ❮❒ Ô ÏÐ Ó æ Ï❮ Ó Ï Þ ❒ Ô Þ❒ Þ ÏÐ Ý ❒❮Ü Ï❰ Ï Ú Ò ÓÔ Ï Ó Ï ÞÐ Û Ô ã Ý ❰Ï❮ Ó ❮ ❒Ð Ó ❰ ã Ï Ô ô❒❮❮Û ❮õ à úû ü ýþ ÿ û ✁✂ ✄☎ ✆☎✂✁ ✝ ✁û ✆ ✞û ✆ ✄☎ ✟ ü û ✠û û ✆ ✡✡ ☛ Tabel 4.6 Hasil Pengujian Asumsi Heteroskedastisitas Correlations .286 .535 7 .357 .432 7 Correlation Coefficient Sig. 2-tailed N Correlation Coefficient Sig. 2-tailed N X1 X2 Spearmans rho absolut_error ☞✌✍✎ ✏✑ ✏✍ ✒✏ ✓ ✔✏✑ ✕✖ ✒ ✗✍ ✌✖✏✑ ✕ ✘ ✏ ✓ ✙ ✎ ✕✚✌✍✗ ✖✌ ✔ ✑ ✌ ✚ ✌✍ ✛✕ ✎ ✏ ✚ ✏✛ ✎✕✖✕ ✔ ✏✛ ✚ ✏✎ ✏ ✛✏✜✌✖ ✢✣✤ ✎ ✕✏✛✏✑ ✥ ✌ ✥ ✜ ✌✍✕✒ ✏ ✓ ✑ ✦ ✏✛ ✦ ✕ ✓✎✕✒ ✏✑ ✕ ✜ ✏ ✔ ✧ ✏ ✍ ✌✑ ✕✎ ✦ ✏✖ ★ ✌✍✍✗ ✍ ✩ ✘ ✏ ✓ ✙ ✥ ✦ ✓ ✪ ✦ ✖ ✎✏✍✕ ✚✌✍✑ ✏ ✥ ✏✏ ✓ ✍✌ ✙ ✍✌✑ ✕ ✥ ✌ ✥ ✚ ✦ ✓✘ ✏✕ ✫✏✍✕✏ ✓✑ ✘ ✏ ✓ ✙ ✑ ✏ ✥ ✏ ★ ✛✕✎✏✒ ✛✌✍ ✬ ✏✎ ✕ ✔✌✛✌✍✗ ✑ ✒ ✌✎✏✑ ✛✕✑ ✕✛✏✑ ✩ ✭ ✎✕ ✥ ✏ ✓✏ ✓ ✕✖✏✕ ✑ ✕ ✙ ✓ ✕ ✮ ✕✒ ✏ ✓ ✑ ✕ ★ ✑ ✕✙ ✩ ✎✏✍ ✕ ✥ ✏✑ ✕ ✓✙ ✯ ✥ ✏✑ ✕ ✓ ✙ ✒ ✗✌ ✮ ✕✑ ✕✌ ✓ ✒ ✗✍ ✌✖ ✏✑ ✕ ✒ ✌✎ ✦ ✏ ✫ ✏✍✕✏✜ ✌✖ ✜ ✌✜✏✑ ✎✌ ✓ ✙ ✏ ✓ ✓ ✕✖✏✕ ✏✜ ✑ ✗ ✖ ✦ ✛ ✌✍ ✍✗✍ ★ ✘ ✏✕✛ ✦ ✰ ✭ ✱ ✲✱ ✎✏ ✓ ✰ ✭ ✢ ✲ ✳ ✩ ✥ ✏✑ ✕ ✔ ✖✌✜ ✕ ✔ ✜ ✌✑ ✏✍ ✎ ✏✍✕ ✰ ✭ ✰✱ ✣ 4 Uji Asumsi Autokorelasi ✴ ✦ ✛✗✒ ✗✍✌✖✏✑ ✕ ✎✕✎ ✌ ✮ ✕ ✓ ✕✑ ✕✒✏ ✓ ✑ ✌✜ ✏ ✙ ✏✕ ✒✗ ✍✌✖✏✑ ✕ ✏ ✓✛✏✍ ✗✜ ✑ ✌✍ ✫ ✏✑ ✕ ✘ ✏ ✓ ✙ ✎✕ ✦ ✒ ✦ ✍ ✜ ✌✍✎ ✏ ✑ ✏✍ ✒✏ ✓ ✎✌ ✍✌✛ ✧ ✏✒ ✛ ✦ ✎✏✖✏ ✥ ✥ ✗ ✎✌✖ ✍ ✌ ✙ ✍✌✑ ✕ ✏✛ ✏ ✦ ✎ ✌ ✓ ✙ ✏ ✓ ✒✏✛✏ ✖✏✕ ✓ ✌✍ ✍✗ ✍ ✎ ✏✍✕ ✗ ✜✑ ✌✍ ✫✏✑ ✕ ✛✏ ✔ ✦ ✓ ✜✌✍ ✬ ✏✖✏ ✓ ✎ ✕ ✚ ✌ ✓ ✙ ✏✍ ✦ ✔ ✕ ✗ ✖✌ ✔ ✌✍✍ ✗✍ ✎ ✏✍✕ ✗ ✜✑ ✌✍ ✫✏✑ ✕ ✛✏ ✔ ✦ ✓ ✑ ✌✜✌✖ ✦ ✥ ✓ ✘ ✏ ✣ ✵ ✏✎ ✏ ✚ ✌ ✓ ✙ ✦ ✬ ✕✏ ✓ ✏ ✦ ✛✗ ✒✗ ✍✌ ✖✏✑ ✕ ✎ ✕ ✙ ✦ ✓✏✒✏ ✓ ✦ ✬ ✕ ✶ ✦ ✍✜ ✕ ✓ ✯ ✷✏✛✑ ✗ ✓ ✦ ✓ ✛ ✦ ✒ ✥ ✌ ✓✙✌✛ ✏ ✔ ✦ ✕ ✏✎✏ ✛✕✎ ✏✒ ✓ ✘ ✏ ✏ ✦ ✛✗✒ ✗ ✍✌✖✏✑ ✕ ✚✏✎✏ ✥ ✗ ✎✌✖ ✍✌ ✙ ✍ ✌✑ ✑ ✕ ✎✏ ✓ ✜ ✌✍✕✒ ✦ ✛ ✓✕✖✏✕ ✶ ✦ ✍✜ ✕✓ ✯ ✷✏✛✑ ✗ ✓ ✘ ✏ ✓✙ ✎ ✕ ✚ ✌✍✗ ✖✌ ✔ ✥ ✌✖✏✖ ✦ ✕ ✔ ✏✑ ✕✖ ✌✑ ✛✕ ✥ ✏✑ ✕ ✥ ✗ ✎✌✖ ✍✌ ✙✍✌✑ ✑ ✕ ✣ ✸✹ ✺ ✻✼ ✽ ✹ ✾✿❀ ❁❂ ❃❂❀✿ ❄ ✿✹ ❃ ❅✹ ❃ ❁❂ ❆ ✺ ✹ ❇✹✾✹ ❃ ❈❈ ❉ Tabel 4.7 Nilai Durbin-Watson Untuk Uji Autokorelasi Model Summary b .969 a .938 .907 53219.37965 2.446 Model 1 R R Square Adjusted R Square Std. Error of the Estimate Durbin- Watson Predictors: Constant, X2, X1 a. Dependent Variable: Y b. ❊❋●❍ ■❏ ■● ❑■ ▲ ▼■❏ ◆❖ P❋ ▲◗ ❘ ❖ ■ ▼ ■ ▲ ❍ ◆ P❋● ❘ ❖ ❋ ▼ ▲◆❖ ■ ◆ ❏ ❙■❙ ◆ ❏ ❙ ◆ ❑ ❚❯● ❱ ◆▲❲❳ ■❙❏ ❘ ▲ ❨❚❲ ❳ ❩ ❬ ❭❪ ❫ ❫❴❪ ❏ ❋ ❵ ❋ ▲❙■●■ ❍ ■● ◆ ❙■ ❱❋ ❖ ❍ ❯ ▲ ❙❯ ❑ ❛ ❯ ❵ ❖ ■ ▼ ❜ ■● ◆ ■ ❱ ❋ ❖ ❱❋ ❱ ■❏ ❬ ❭ ❍ ■ ▲ ❛ ❯ ❵ ❖ ■ ▼ P ❋ ▲ ◗ ■ ❵ ■❙■ ▲ ▲ ❬ ❝ ❍ ◆ P ❋● ❘ ❖ ❋ ▼ ❱■❙■❏ ❱■ ❞ ■ ▼ ▲◆❖ ■ ◆ ❙■ ❱ ❋ ❖ ❨❍ ❡ ❩ ❬ ❢ ❪ ❫ ❴ ❝ ❍■ ▲ ❱■❙■❏ ■❙■❏ ▲ ❣ ■ ❨ ❍ ❤ ❩ ❬ ✐❪ ❥❦ ❴ ❧ ♠ ■● ❋ ▲ ■ ▲◆❖ ■ ◆ ❚❯● ❱◆▲❲❳ ■❙❏ ❘ ▲ ❵ ❘❍❋ ❖ ●❋◗ ●❋❏ ❏ ◆ ❨ ❭❪ ❫ ❫❴❩ ❱ ❋●■❍ ■ ❍ ◆ ■ ▲ ❙■●■ ❫ ❲ ❍ ❤ ❨ ❭ ❪ ✐ ❢ ❫ ❩ ❍■ ▲ ❫ ❲❍ ❡ ❨♥ ❪ ♦♥ ♥ ❩ ❪ ❣ ■ ◆ ❙❯ ❍ ■❋●■ ▼ ❙ ◆ ❍■❑ ■❍ ■ ❑ ❋P❯ ❙❯❏ ■ ▲ ❵ ■❑ ■ ❱❋ ❖ ❯ ❵ ❍ ■P■❙ ❍ ◆ ❏ ◆ ❵ P❯ ❖ ❑■ ▲ ■P ■❑■ ▼ ❙❋● ❛ ■❍ ◆ ■❯❙ ❘❑ ❘●❋ ❖ ■❏ ◆ P ■❍■ ❵ ❘❍❋ ❖ ● ❋◗●❋❏ ❏ ◆ ❧ 4 Terdapat Autokorelasi Positif Terdapat Autokorelasi Negatif Tidak Terdapat Autokorelasi Tidak Ada Keputusan Tidak Ada Keputusan d L =0,467 d U =1,896 4-d U =2,104 4-d L =3,533 D-W =2,446 Gambar 4.5 Daerah Kriteria Pengujian Autokorelasi ♣▲❙❯❑ ❵ ❋ ❵ ■❏ ❙ ◆ ❑ ■ ▲ ■❍■ ❙ ◆ ❍■❑ ▲ ❣ ■ ■❯ ❙ ❘ ❑ ❘●❋ ❖ ■❏ ◆ ❵ ■❑■ P ❋ ▲ ◗❯ ❛ ◆ ■ ▲ ❍ ◆❖ ■ ▲ ❛ ❯ ❙❑■ ▲ ❵ ❋ ▲ ◗ ◗❯ ▲■❑ ■ ▲ q rs t ✉✈ t ✉ ❨ ✇ ❯ ❛ ■●■❙ ◆ ❪ ❭ ❢❢♥ ①❫ ❴ ♦ ❩❧ ② ■❏ ◆❖ P ❋ ▲ ◗❯ ❛ ◆ ■ ▲ ❵ ❋ ▲◗◗❯ ▲■❑■ ▲ q rs t ✉✈ t ✉ ❍ ■P■❙ ❍ ◆❖◆▼ ■❙ P■❍ ■ ❙■ ❱❋ ❖ ❫ ❧ ❥ ❱ ❋● ◆ ❑ ❯❙ ◆ ▲ ◆ ❧ ③④ ⑤ ⑥⑦ ⑧ ④ ⑨⑩❶ ❷❸ ❹❸❶⑩ ❺ ⑩④ ❹ ❻④ ❹ ❷❸ ❼ ⑤ ④ ❽④⑨④ ❹ ❾❾ ❿ Tabel 4.8 Hasil Runs Test Untuk Memastikan Ada Tidaknya Autokorelasi Runs Test -11815.17994 3 4 7 5 .061 .952 Test Value a Cases Test Value Cases = Test Value Total Cases Number of Runs Z Asymp. Sig. 2-tailed Unstandardiz ed Residual Median a. ➀➁➂➃➂➄➅ ➆ ➃➇ ➅ ➂ ➈ ➉ ➊➋ ➌➍➋ ➌ ➎ ➃ ➏ ➃ ➐➃➑ ➁➂ ➒ ➓➔ ➏ ➃ ➎ ➃➐ ➏➅ ➂ ➅➆ ➃➐ ➑ ➃ ➆→ ➃ ➣➅ ➂➃ ➅ ➇ ➅ ↔ ➣ ➅↕➅ ➙ ➃ ➣ ➇ ➅ ➄ ➛ ➅ ➜ ➝ ➞ ➃ ➅➐➄ ➟ ➠➡➢ ➤➥ ➦ ➃➇ ➅➆ ➂➁➑ ➅➆ ➑ ➁➇ ➃ ➧ ➏ ➃ ➧ ➅ ➟➠➟➢ ➞ ➃ ➣ ↔ ➦ ➁ ➣ ↔ ➅➣ ➏➅ ➙ ➃➇ ➅ ➙ ➃ ➣ ➐ ➅➏ ➃➙ ➐➁ ➧ ➏ ➃ ➎ ➃➐ ➃➄➐➨ ➙➨ ➧ ➁➂➃➇ ➅ ➎ ➃ ➏ ➃ ➦ ➨➏ ➁➂ ➧ ➁↔ ➧ ➁➇ ➇ ➅ ➓ ➩ ➁➐➁➂➃ ➆ ➙ ➁➁ ➦ ➎ ➃➐ ➃➇ ➄ ➦ ➇ ➅ ➧ ➁↔ ➧ ➁➇ ➇ ➅ ➏➅ ➄ ➛ ➅ ➏ ➃ ➣ ➐➁ ➧ ➎ ➁ ➣ ➄➆➅ ➠ ➇ ➁➂➃ ➣ ➛ ➄ ➐ ➣ ➞ ➃ ➏ ➅ ➂➃➙➄ ➙➃ ➣ ➎ ➁ ➣ ↔ ➄ ➛ ➅ ➃ ➣ ➆➅➎ ➨➐➁➇ ➅ ➇ ➠ ➞ ➃ ➅ ➐➄ ➎ ➁ ➣ ↔➃ ➧ ➄ ➆ ➫➭➋ ➯ ➈ ➭➌ ➲➳ ➏ ➃ ➣ ➎ ➁ ➦ ➑ ➅ ➃ ➞ ➃ ➃ ➣ ➵➉ ➈ ➭ ➸➭ ➯➭ ➯ ➐➁ ➧ ➆ ➃ ➏ ➃ ➎ ➎ ➁ ➣ ➏ ➃ ➎ ➃ ➐ ➃ ➣ ➵ ➭ ➈ ➺ ➲➊ ➵➉ ➈ ➭ ➸➭ ➯➭ ➯ ➓

4.2.2.2 Analisis Regresi Linier Berganda