Mensimulasikan penjualan dalam kondisi pasar baru

Analis keuangan perusahaan memperkirakan bahwa pada kuartal berikutnya, nilai setiap penjualan akan meningkat 20% dan volatilitas, atau simpangan baku, dari nilai setiap penjualan akan meningkat 30%. Untuk melihat seperti apa penjualan Amir pada kuartal berikutnya di bawah kondisi pasar baru ini, Anda akan mensimulasikan nilai penjualan baru menggunakan distribusi normal dan menyimpannya dalam data frame new_sales, yang telah dibuat untuk Anda.

Selain itu, pustaka dplyr dan ggplot2 sudah dimuat.

Latihan ini adalah bagian dari kursus

Pengantar Statistika di R

Petunjuk latihan

Saat ini, nilai penjualan rata-rata Amir adalah $5000. Hitung berapa nilai rata-rata barunya jika meningkat 20% dan simpan dalam new_mean.
Simpangan baku Amir saat ini adalah $2000. Hitung berapa simpangan baku barunya jika meningkat 30% dan simpan dalam new_sd.
Tambahkan kolom baru bernama amount ke data frame new_sales, yang berisi 36 nilai simulasi dari distribusi normal dengan mean new_mean dan simpangan baku new_sd.
Plot sebaran amount pada new_sales menggunakan histogram dengan 10 bin.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate new average amount
new_mean <- ___

# Calculate new standard deviation
new_sd <- ___

# Simulate 36 sales
new_sales <- new_sales %>% 
  mutate(amount = ___)

# Create histogram with 10 bins
___

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

Latihan Saat Ini

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 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!