How many sales will be won?

Now Amir wants to know how many deals he can expect to close each week if his win rate changes. Luckily, you can use your binomial distribution knowledge to help him calculate the expected value in different situations. Recall from the video that the expected value of a binomial distribution can be calculated by \(n \times p\).

This exercise is part of the course

Introduction to Statistics in R

Exercise instructions

Calculate the expected number of sales out of the 3 he works on that Amir will win each week if he maintains his 30% win rate.
Calculate the expected number of sales out of the 3 he works on that he'll win if his win rate drops to 25%.
Calculate the expected number of sales out of the 3 he works on that he'll win if his win rate rises to 35%.

Hands-on interactive exercise

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

# Expected number won with 30% win rate
won_30pct <- ___ * ___
won_30pct

# Expected number won with 25% win rate
won_25pct <- ___
won_25pct

# Expected number won with 35% win rate
won_35pct <- ___
won_35pct

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

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?

Current Exercise

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.

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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!