Menerapkan Tukey's HSD

Setelah analisis ANOVA yang menunjukkan adanya perbedaan signifikan pada efektivitas tiga jenis terapi, para psikolog ingin menggali lebih dalam. Mereka meminta Anda menjelaskan jenis terapi mana saja yang berbeda satu sama lain dalam menurunkan tingkat kecemasan. Di sinilah uji Tukey's Honest Significant Difference (HSD) digunakan. Ini adalah uji post-hoc untuk melakukan perbandingan berpasangan antar-mean kelompok setelah ANOVA menunjukkan adanya perbedaan yang signifikan. Uji Tukey's HSD membantu mengidentifikasi pasangan kelompok tertentu yang memiliki perbedaan signifikan pada mean-nya.

DataFrame therapy_outcomes yang memuat data eksperimen ini telah dimuat kembali bersama dengan pandas as pd dan from statsmodels.stats.multicomp import pairwise_tukeyhsd.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Petunjuk latihan

Pada taraf signifikansi 0.05, lakukan uji Tukey's HSD untuk membandingkan rata-rata penurunan kecemasan di antara tiga kelompok terapi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Perform Tukey's HSD test
tukey_results = ____(
    ____, 
    ____, 
    ____
)

print(tukey_results)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Factorial designs: principles and applications Exercise 2: Understanding marketing campaign effectiveness Exercise 3: Heatmap of campaign interactions Exercise 4: Factorial designs and randomized block designs Exercise 5: Randomized block design: controlling variance Exercise 6: Implementing a randomized block design Exercise 7: Visualizing productivity within blocks by incentive Exercise 8: ANOVA within blocks of employees Exercise 9: Covariate adjustment in experimental design Exercise 10: Importance of covariates Exercise 11: Covariate adjustment with chick growth

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Memilih uji statistik yang tepat Exercise 2: Memilih uji yang tepat: petrokimia Exercise 3: Memilih uji yang tepat: sumber daya manusia Exercise 4: Memilih uji yang tepat: keuangan Exercise 5: Analisis post-hoc setelah ANOVA Exercise 6: ANOVA untuk pengobatan kecemasan Exercise 7: Menerapkan Tukey's HSD

Latihan Saat Ini

Exercise 8: Menerapkan koreksi Bonferroni Exercise 9: P-value, alpha, dan galat Exercise 10: Menganalisis daya tahan mainan Exercise 11: Memvisualisasikan perbedaan ketahanan Exercise 12: Peran tingkat signifikansi Exercise 13: Analisis daya: ukuran sampel dan efek Exercise 14: Tujuan ukuran efek Exercise 15: Mengestimasi ukuran sampel yang diperlukan untuk studi energi

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Synthesizing insights from complex experiments Exercise 2: Visualizing loan approval yield Exercise 3: Exploring customer satisfaction Exercise 4: Effectively communicating experimental data Exercise 5: Addressing complexities in experimental data Exercise 6: Check for heteroscedasticity in shelf life Exercise 7: Exploring and transforming shelf life data Exercise 8: Applying nonparametric tests in experimental analysis Exercise 9: Visualizing and testing preservation methods Exercise 10: Further analyzing food preservation techniques Exercise 11: Congratulations!