Rata-rata dari rata-rata

Anda ingin mengetahui berapa rata-rata jumlah pengguna (num_users) per kesepakatan, tetapi Anda ingin mengetahui angka ini untuk seluruh perusahaan agar dapat melihat apakah kesepakatan Amir memiliki pengguna lebih banyak atau lebih sedikit dibandingkan rata-rata kesepakatan perusahaan. Masalahnya, selama setahun terakhir, perusahaan telah menangani lebih dari sepuluh ribu kesepakatan, sehingga tidak realistis untuk mengompilasi semua data. Sebagai gantinya, Anda akan memperkirakan mean dengan mengambil beberapa sampel acak dari kesepakatan, karena ini jauh lebih mudah daripada mengumpulkan data dari seluruh perusahaan.

Data pengguna untuk semua kesepakatan perusahaan tersedia di all_deals.

Latihan ini adalah bagian dari kursus

Pengantar Statistika di R

Petunjuk latihan

Atur seed acak ke 321.
Ambil 30 sampel berukuran 20 dari all_deals$num_users dan hitung mean dari setiap sampel. Simpan mean sampel dalam sample_means.
Hitung mean dari sample_means.
Hitung mean dari kolom num_users pada amir_deals.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set seed to 321
___

# Take 30 samples of 20 values of num_users, take mean of each sample
sample_means <- ___(___, ___(___, ___) %>% mean())

# Calculate mean of sample_means
___

# Calculate mean of num_users in amir_deals
___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Statistika di R

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: Mean and median Exercise 6: Mean vs. median 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: Distribusi normal Exercise 2: Distribusi penjualan Amir Exercise 3: Probabilitas dari distribusi normal Exercise 4: Mensimulasikan penjualan dalam kondisi pasar baru Exercise 5: Pasar mana yang lebih baik?Exercise 6: Teorema limit pusat Exercise 7: Memvisualisasikan sebaran penarikan contoh Exercise 8: CLT dalam praktik Exercise 9: Rata-rata dari rata-rata

Latihan Saat Ini

Exercise 10: Distribusi Poisson Exercise 11: Mengidentifikasi lambda Exercise 12: Melacak respons prospek Exercise 13: Lebih banyak sebaran probabilitas Exercise 14: Terlalu Banyak Distribusi Exercise 15: Memodelkan waktu antar prospek Exercise 16: Distribusi t

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: Correlation Exercise 2: Guess the correlation Exercise 3: Relationships between variables Exercise 4: Correlation caveats Exercise 5: What can't correlation measure?Exercise 6: Transforming variables Exercise 7: Does sugar improve happiness?Exercise 8: Confounders Exercise 9: Design of experiments Exercise 10: Study types Exercise 11: Longitudinal vs. cross-sectional studies Exercise 12: Congratulations!