Hubungan antar variabel

Pada bab ini, Anda akan bekerja dengan himpunan data world_happiness yang berisi hasil dari World Happiness Report 2019. Laporan tersebut memberi skor berbagai negara berdasarkan seberapa bahagia penduduknya. Laporan ini juga memberi peringkat setiap negara pada berbagai aspek sosial seperti dukungan sosial, kebebasan, korupsi, dan lainnya. Himpunan data ini juga mencakup PDB per kapita dan angka harapan hidup untuk setiap negara.

Dalam latihan ini, Anda akan menelaah hubungan antara angka harapan hidup suatu negara (life_exp) dan skor kebahagiaan (happiness_score) baik secara visual maupun kuantitatif. seaborn sebagai sns, matplotlib.pyplot sebagai plt, dan pandas sebagai pd sudah dimuat dan world_happiness tersedia.

Latihan ini adalah bagian dari kursus

Pengantar Statistika di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a scatterplot of happiness_score vs. life_exp and show
____

# Show plot
____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Statistika di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

Exercise 1: What is statistics?Exercise 2: Descriptive and inferential statistics Exercise 3: Data type classification Exercise 4: Measures of center Exercise 5: Calculating mean and median Exercise 6: Mean, median, and distribution shape Exercise 7: Measures of spread Exercise 8: Variance and standard deviation Exercise 9: Quartiles, quantiles, and quintiles Exercise 10: Finding outliers using IQR

In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

Exercise 1: What are the chances?Exercise 2: With or without replacement?Exercise 3: Calculating probabilities Exercise 4: Sampling deals Exercise 5: Discrete distributions Exercise 6: Creating a probability distribution Exercise 7: Identifying distributions Exercise 8: Expected value vs. sample mean Exercise 9: Continuous distributions Exercise 10: Which distribution?Exercise 11: Data back-ups Exercise 12: Simulating wait times Exercise 13: The binomial distribution Exercise 14: Simulating sales deals Exercise 15: Calculating binomial probabilities Exercise 16: How many sales will be won?

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire.

Exercise 1: The normal distribution Exercise 2: Distribution of Amir's sales Exercise 3: Probabilities from the normal distribution Exercise 4: Simulating sales under new market conditions Exercise 5: Which market is better?Exercise 6: The central limit theorem Exercise 7: Visualizing sampling distributions Exercise 8: The CLT in action Exercise 9: The mean of means Exercise 10: The Poisson distribution Exercise 11: Identifying lambda Exercise 12: Tracking lead responses Exercise 13: More probability distributions Exercise 14: Distribution dragging and dropping Exercise 15: Modeling time between leads Exercise 16: The t-distribution

In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Exercise 1: Korelasi Exercise 2: Tebak korelasi Exercise 3: Hubungan antar variabel

Latihan Saat Ini

Exercise 4: Catatan penting tentang korelasi Exercise 5: Apa yang tidak dapat diukur oleh korelasi?Exercise 6: Transformasi variabel Exercise 7: Apakah gula meningkatkan kebahagiaan?Exercise 8: Perancu Exercise 9: Rancangan eksperimen Exercise 10: Jenis studi Exercise 11: Studi longitudinal vs. potong lintang Exercise 12: Selamat!