ANOVA dalam blok karyawan

Melanjutkan analisis sebelumnya dengan perusahaan manufaktur, di mana produktivitas pekerja ditelaah lintas berbagai blok dan program insentif diperkenalkan, kini Anda menggali data lebih dalam. Perusahaan kini memiliki himpunan data yang lebih lengkap dalam DataFrame productivity, termasuk 1200 karyawan tambahan beserta productivity_score mereka, dan telah membagi tenaga kerja menjadi tiga blok berdasarkan tingkat produktivitas. Setiap karyawan secara acak ditugaskan salah satu dari tiga opsi insentif: 'Bonus', 'Profit Sharing', atau 'Work from Home'.

Sebelum menilai dampak penuh dari perlakuan insentif terhadap produktivitas, penting untuk memverifikasi bahwa penugasan perlakuan awal benar-benar acak dan seimbang di seluruh blok produktivitas. Langkah ini memastikan bahwa setiap perbedaan produktivitas yang diamati setelah perlakuan dapat dengan yakin diatribusikan pada program insentif itu sendiri, bukan pada kesenjangan awal antarblok.

Fungsi f_oneway() dari scipy.stats telah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Petunjuk latihan

Kelompokkan prod_df berdasarkan kolom yang sesuai yang merepresentasikan blok berbeda dalam data Anda.
Gunakan fungsi lambda untuk menerapkan uji ANOVA di dalam setiap blok, dengan menentukan argumen fungsi lambda.
Untuk setiap kelompok perlakuan dalam blok, saring prod_df berdasarkan nilai kolom 'Treatment' dan pilih kolom 'productivity_score'.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Perform the within blocks ANOVA, first grouping by block
within_block_anova = prod_df.groupby('____').apply(
  # Set function
  lambda x: ____(
    # Filter Treatment values based on outcome
    x[x['____'] == '____']['____'], 
    x[x['____'] == '____']['____'],
    x[x['____'] == '____']['____'])
)
print(within_block_anova)

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 Exercise 7: Memvisualisasikan produktivitas dalam blok berdasarkan insentif Exercise 8: ANOVA dalam blok karyawan

Latihan Saat Ini

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!