Pemblokiran data eksperimen

Anda bekerja dengan sebuah perusahaan manufaktur yang ingin melakukan eksperimen terkait produktivitas pekerja. Himpunan data mereka hanya berisi 100 baris, sehingga penting untuk menjaga keseimbangan kelompok eksperimen.

Ini merupakan kesempatan yang baik untuk menggunakan pengetahuan Anda tentang pemblokiran guna membantu mereka. Mereka telah menyediakan DataFrame productivity_subjects. Bagi himpunan data yang diberikan menjadi dua kelompok yang seimbang, masing-masing berisi 50 entri.

Pustaka numpy dan pandas telah diimpor sebagai np dan pd.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Petunjuk latihan

Secara acak pilih 50 subjek dari DataFrame productivity_subjects ke dalam DataFrame baru block_1 tanpa pengembalian.
Atur kolom baru block menjadi 1 untuk DataFrame block_1.
Tempatkan sisa subjek ke dalam DataFrame bernama block_2 dan atur kolom block menjadi 2 untuk DataFrame ini.
Gabungkan kedua blok menjadi satu DataFrame, lalu cetak jumlah tiap nilai pada kolom block untuk memastikan pemblokiran berhasil.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Randomly assign half
block_1 = productivity_subjects.____(____, random_state=42, ____)

# Set the block column
block_1['block'] = ____

# Create second assignment and label
block_2 = ____
block_2['block'] = ____

# Concatenate and print
productivity_combined = pd.____([block_1, block_2], axis=0)
print(productivity_combined['block'].value_counts())

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: Menyiapkan eksperimen Exercise 2: Penugasan subjek non-acak Exercise 3: Penugasan acak subjek Exercise 4: Penyiapan data eksperimen Exercise 5: Pemblokiran data eksperimen

Latihan Saat Ini

Exercise 6: Melakukan stratifikasi pada sebuah eksperimen Exercise 7: Manakah yang distratifikasi?Exercise 8: Data normal Exercise 9: Kenormalan visual dalam sebuah eksperimen pertanian Exercise 10: Normalitas analitik dalam eksperimen pertanian

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: 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!