Penugasan subjek non-acak

Sebuah perusahaan agrikultur sedang melakukan eksperimen untuk mengukur bagaimana pemberian berbagai jenis rumput kepada domba memengaruhi berat badan mereka. Mereka meminta bantuan Anda untuk menyiapkan eksperimen dengan benar. Salah satu manajer mengatakan Anda dapat melakukan penugasan subjek dengan mengambil 250 baris teratas dari DataFrame dan itu sudah cukup.

Tugas Anda adalah menggunakan kemampuan analitis untuk menunjukkan mengapa hal ini mungkin bukan ide yang baik. Tempatkan subjek ke dalam dua kelompok menggunakan penugasan non-acak (250 baris pertama) dan amati perbedaan dalam statistik deskriptifnya.

Anda telah menerima DataFrame, weights yang memiliki kolom berisi weight domba dan kolom id yang unik.

numpy dan pandas telah diimpor masing-masing sebagai np dan pd.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Petunjuk latihan

Gunakan slicing DataFrame untuk memasukkan 250 baris pertama weights ke group1_non_rand dan sisanya ke group2_non_rand.
Hasilkan statistik deskriptif dari kedua kelompok dan gabungkan menjadi satu DataFrame.
Cetak untuk mengamati perbedaannya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Non-random assignment
group1_non_rand = ____
group2_non_rand = ____

# Compare descriptive statistics of groups
compare_df_non_rand = ____([group1_non_rand['weight'].____, group2_non_rand['weight'].____], axis=1)
compare_df_non_rand.columns = ['group1', 'group2']

# Print to assess
print(____)

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

Latihan Saat Ini

Exercise 3: Penugasan acak subjek Exercise 4: Penyiapan data eksperimen Exercise 5: Pemblokiran data eksperimen 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!