There is some speculation that a simple name change can result in a short-term increase in the price of certain business firms' stocks (relative to the prices of similar stocks). Suppose that to test the profitability of name changes, we analyze the stock prices of a large sample of corporations shortly after they changed names, and we find that the mean relative increase in stock price was about [tex]$0.73\%$[/tex], with a standard deviation of [tex]$0.20\%$[/tex]. Suppose that this mean and standard deviation apply to the population of all companies that changed names. Complete the following statements about the distribution of relative increases in stock price for all companies that changed names.

(a) According to Chebyshev's theorem, at least [tex]$84\%$[/tex] of the relative increases in stock price lie between [tex]$\square\%$[/tex] and [tex]$\square\%$[/tex]. (Round your answer to 2 decimal places.)

(b) According to Chebyshev's theorem, at least [tex]$\square$[/tex] (Choose one) of the relative increases in stock price lie between [tex]$0.33\%$[/tex] and [tex]$1.13\%$[/tex].



Answer :

To analyze the changes in stock prices and use Chebyshev's theorem to provide bounds on the data, follow these steps:

### (a) Calculating the Range for 84% Confidence Interval

First, determine the boundaries for the stock price increase where at least 84% of the data will lie based on Chebyshev's theorem.

The mean relative increase in stock price is [tex]\( \mu = 0.73\% \)[/tex], and the standard deviation is [tex]\( \sigma = 0.20\% \)[/tex].

Chebyshev's theorem states that for any distribution, at least [tex]\( \frac{1 - \frac{1}{k^2}}{100} \)[/tex]% of the data lies within [tex]\( k \)[/tex] standard deviations of the mean, where [tex]\( k \)[/tex] is a positive number.

To find [tex]\( k \)[/tex] for an 84% confidence interval:
[tex]\[ 1 - \frac{1}{k^2} = 0.84 \][/tex]
[tex]\[ \frac{1}{k^2} = 1 - 0.84 = 0.16 \][/tex]
[tex]\[ k^2 = \frac{1}{0.16} = 6.25 \][/tex]
[tex]\[ k = \sqrt{6.25} = 2.5 \][/tex]

Hence, we need to find the boundaries that lie within [tex]\( 2.5 \)[/tex] standard deviations of the mean:
[tex]\[ \text{Lower bound} = \mu - k \sigma = 0.73\% - 2.5 \times 0.20\% \][/tex]
[tex]\[ \text{Lower bound} = 0.73\% - 0.50\% = 0.23\% \][/tex]

[tex]\[ \text{Upper bound} = \mu + k \sigma = 0.73\% + 2.5 \times 0.20\% \][/tex]
[tex]\[ \text{Upper bound} = 0.73\% + 0.50\% = 1.23\% \][/tex]

Thus, according to Chebyshev's theorem, at least 84% of the relative increases in stock price lie between [tex]\( 0.23\% \)[/tex] and [tex]\( 1.23\% \)[/tex].

Statement (a):
According to Chebyshev's theorem, at least 84% of the relative increases in stock price lie between [tex]\( \mathbf{0.23\%} \)[/tex] and [tex]\( \mathbf{1.23\%} \)[/tex].

### (b) Percentage of Relative Increases in Given Interval

To determine the percentage of relative increases in stock price that lie between [tex]\( 0.33\% \)[/tex] and [tex]\( 1.13\% \)[/tex]:

Here, we need to find [tex]\( k \)[/tex] such that the interval [tex]\( [0.33\%, 1.13\%] \)[/tex] is within [tex]\( k \)[/tex] standard deviations of the mean.

First, calculate [tex]\( k \)[/tex]:
[tex]\[ \text{Upper bound} = 1.13\% \quad \text{and} \quad \text{Lower bound} = 0.33\% \][/tex]
[tex]\[ \text{Width from mean to upper bound} = 1.13\% - 0.73\% = 0.40\% \][/tex]

[tex]\[ k = \frac{0.40\%}{0.20\%} = 2 \][/tex]

Using Chebyshev's theorem:
[tex]\[ 1 - \frac{1}{k^2} = 1 - \frac{1}{2^2} = 1 - \frac{1}{4} = 0.75 \][/tex]

Thus, at least 75% of the relative increases in stock price lie within the interval from [tex]\( 0.33\% \)[/tex] to [tex]\( 1.13\% \)[/tex].

Statement (b):
According to Chebyshev's theorem, at least [tex]\( \mathbf{75\%} \)[/tex] of the relative increases in stock price lie between [tex]\( 0.33\% \)[/tex] and [tex]\( 1.13\% \)[/tex].

By understanding and applying Chebyshev's theorem, we can conclude that:
- At least 84% of the increases lie between 0.23% and 1.23%.
- At least 75% of the increases lie between 0.33% and 1.13%.