Refer to the previous exercise for a description of the problem and data set. The financial analyst wants to find out if there are any missing values in the data set. 

a. Are there any missing values in the data set? If there are, which variables have missing values? Which observations have missing values? How many missing values are there in the data set? 

b. Use the omission strategy to handle missing values. How many observations are removed due to missing values? 

c. Return to the original data set. If there are missing values for Price, Dividend, Book Value, 52 wk low, or 52 wk high, replace the missing value with “M,” which stands for “Missing.” If there are missing values for PE, EPS, Market cap, or EBITDA, use the imputation strategy to replace the missing values with the median of the variable. What are the imputed values for the variables with missing data?

Data from Exercise 31

Investors usually consider a variety of information to make investment decisions. The accompanying table displays a sample of large publicly traded corporations and their financial information. Relevant information includes stock price (Price), dividend as a percentage of share price (Dividend), price to earnings ratio (PE), earnings per share (EPS), book value, lowest and highest share prices within the past 52 weeks (52 wk low and 52 wk high), market value of the company’s shares (Market cap), and earnings before interest, taxes, depreciation, and amortization (EBITDA in $ billions).

 

Transcribed Image Text:

Name Price 3M 189.09 Abbott Lab 45.00 ⠀ Zoetis # 53.07 Dividend 2.48 2.34 1 0.79 ⠀ EBITDA 8.70 4.59 ⠀ 1.70