Melakukan stratifikasi pada sebuah eksperimen

Anda bekerja dengan sebuah organisasi pemerintah yang ingin melakukan eksperimen tentang bagaimana kebijakan pemerintah tertentu memengaruhi kekayaan bersih individu di sejumlah wilayah.

Mereka meminta Anda membantu menyiapkan desain eksperimen. Mereka mengingatkan bahwa kemungkinan ada sekelompok kecil pengguna yang sudah memiliki kekayaan bersih tinggi dan khawatir kelompok ini dapat mendominasi hasil eksperimen yang diamati. Anda tahu harus melakukan apa!

Gunakan pengetahuan Anda tentang desain eksperimen untuk melakukan pengacakan berblok (block randomization), dengan melakukan stratifikasi berdasarkan kolom high_wealth pada DataFrame wealth_data yang disediakan. Terdapat 2000 baris dalam DataFrame dengan 200 subjek berkekayaan bersih tinggi (high_wealth bernilai 1).

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create the first block
strata_1 = wealth_data[wealth_data[____] == ____]
strata_1['Block'] = ____

# Create two groups assigning to Treatment or Control
strata_1_g1 = strata_1.____(____, replace=False)
strata_1_g1['T_C'] = ____
strata_1_g2 = strata_1.drop(strata_1_g1.index)
strata_1_g2[____] = 'C'

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 Exercise 6: Melakukan stratifikasi pada sebuah eksperimen

Latihan Saat Ini

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!