Bir deneyi katmanlara ayırma

Belirli bazı devlet politikalarının, çeşitli bölgelerdeki bireylerin net varlığı üzerindeki etkisini incelemek için bir deney yapmak isteyen bir devlet kurumu ile çalışıyorsun.

Deney tasarımını kurmanda senden yardım istediler. Sana, halihazırda yüksek net varlığa sahip küçük bir kullanıcı grubunun olabileceğini ve bu grubun gözlemlenen herhangi bir deney sonucunu gölgede bırakmasından endişe ettiklerini söylediler. Ne yapacağını çok iyi biliyorsun!

Deney tasarımı bilgini kullanarak, verilen wealth_data DataFrame’inde high_wealth sütununa göre katmanlandırma yaparak blok rastgeleleştirmesi uygula. DataFrame’de 2000 satır var ve bunların 200’ü yüksek net varlıklı denek (high_wealth değeri 1).

Bu egzersiz

Python ile Deney Tasarımı

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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'

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Deney Tasarımı

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Deneyleri kurma Exercise 2: Deneklerin rastgele olmayan atanması Exercise 3: Deneklerin rastgele atanması Exercise 4: Deneysel veri kurulumu Exercise 5: Deneysel verilerde bloklama Exercise 6: Bir deneyi katmanlara ayırma

Geçerli Egzersiz

Exercise 7: Hangisi tabakalandırılmıştı?Exercise 8: Normal veri Exercise 9: Bir tarım deneyinde görsel normallik Exercise 10: Bir tarım deneyinde analitik normallik

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!