Simulating sales under new market conditions

The company's financial analyst is predicting that next quarter, the worth of each sale will increase by 20% and the volatility, or standard deviation, of each sale's worth will increase by 30%. To see what Amir's sales might look like next quarter under these new market conditions, you'll simulate new sales amounts using the normal distribution and store these in the new_sales data frame, which has already been created for you.

In addition, dplyr and ggplot2 libraries are loaded.

This exercise is part of the course

Introduction to Statistics in R

Exercise instructions

Currently, Amir's average sale amount is $5000. Calculate what his new average amount will be if it increases by 20% and store this in new_mean.
Amir's current standard deviation is $2000. Calculate what his new standard deviation will be if it increases by 30% and store this in new_sd.
Add a new column called amount to the data frame new_sales, which contains 36 simulated amounts from a normal distribution with a mean of new_mean and a standard deviation of new_sd.
Plot the distribution of the new_sales amounts using a histogram with 10 bins.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# 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 and Run Code

This exercise is part of the course

Introduction to Statistics in R

IntermediateSkill Level

4.8+

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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

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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?

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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

Current Exercise

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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!