Menerapkan randomized block design

Perusahaan manufaktur yang pernah Anda bantu sebelumnya masih tertarik melakukan eksperimen mengenai produktivitas pekerja. Sebelumnya, dua blok ditetapkan secara acak. Meskipun ini bisa berhasil, akan lebih baik jika subjek dikelompokkan berdasarkan karakteristik yang serupa.

Karyawan yang sama kembali dimuat, namun kali ini dalam sebuah DataFrame bernama productivity bersama 1200 rekan kerja lainnya. DataFrame ini juga menyertakan kolom 'productivity_score' berdasarkan jumlah unit yang diproduksi per jam. Kolom ini dibagi ke dalam tiga kelompok untuk membentuk blok berdasarkan nilai produktivitas yang serupa. Perusahaan ingin menerapkan program insentif baru dengan tiga opsi ('Bonus', 'Profit Sharing', dan 'Work from Home') di seluruh perusahaan dengan perlakuan yang diberikan secara acak.

numpy dan pandas masing-masing sudah dimuat sebagai np dan pd.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Petunjuk latihan

Acak block untuk membuat DataFrame baru bernama prod_df.
Setel ulang indeks agar block tidak menjadi indeks sekaligus kolom.
Tetapkan secara acak tiga nilai perlakuan pada kolom 'Treatment'.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Randomly assign workers to blocks
prod_df = productivity.____('____').apply(
  lambda x: x.____(____)
)

# Reset the index
prod_df = prod_df.____(____)

# Assign treatment randomly
prod_df['Treatment'] = np.random.choice(
  ['____', '____', '____'],
  size=len(____)
)

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: Desain faktorial: prinsip dan penerapan Exercise 2: Memahami efektivitas kampanye pemasaran Exercise 3: Heatmap interaksi kampanye Exercise 4: Desain faktorial dan desain blok acak Exercise 5: Desain blok acak: mengendalikan varians Exercise 6: Menerapkan randomized block design

Latihan Saat Ini

Exercise 7: Memvisualisasikan produktivitas dalam blok berdasarkan insentif Exercise 8: ANOVA dalam blok karyawan Exercise 9: Penyesuaian kovariat dalam rancangan eksperimen Exercise 10: Pentingnya kovariat Exercise 11: Penyesuaian kovariat pada pertumbuhan anak ayam

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: Choosing the right statistical test Exercise 2: Choosing the right test: petrochemicals Exercise 3: Choosing the right test: human resources Exercise 4: Choosing the right test: finance Exercise 5: Post-hoc analysis following ANOVA Exercise 6: Anxiety treatments ANOVA Exercise 7: Applying Tukey's HSD Exercise 8: Applying Bonferoni correction Exercise 9: P-values, alpha, and errors Exercise 10: Analyzing toy durability Exercise 11: Visualizing durability differences Exercise 12: Role of significance levels Exercise 13: Power analysis: sample and effect size Exercise 14: Effect size purpose Exercise 15: Estimating required sample size for energy study

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!