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Part 3 of 5 Calculate the Pearson's index of skewness. (Round the mean, median and standard deviation to one decimal place and Pearson's index to two decima places.) It can be concluded that the distribution is (Choose one) not significantly significantly

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Mar 10,2025

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The Pearson's index of skewness is 0, indicating that the distribution is not significantly skewed.

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To calculate the Pearson's index of skewness, we need to follow these steps: 1. Calculate the mean of the data. 2. Calculate the median of the data. 3. Calculate the standard deviation of the data. 4. Calculate the Pearson's index of skewness using the formula: \[ \text{Pearson's Index of Skewness} = \frac{\text{Mean} - \text{Median}}{\text{Standard Deviation}} \] Let's assume we have the following data: 1, 2, 3, 4, 5. 1. Calculate the mean of the data: \[ \text{Mean} = \frac{1 + 2 + 3 + 4 + 5}{5} = \frac{15}{5} = 3 \] 2. Calculate the median of the data: Since the data is already sorted, the median is the middle value, which is 3. 3. Calculate the standard deviation of the data: \[ \text{Standard Deviation} = \sqrt{\frac{(1-3)^2 + (2-3)^2 + (3-3)^2 + (4-3)^2 + (5-3)^2}{5}} \] \[ \text{Standard Deviation} = \sqrt{\frac{4 + 1 + 0 + 1 + 4}{5}} \] \[ \text{Standard Deviation} = \sqrt{\frac{10}{5}} \] \[ \text{Standard Deviation} = \sqrt{2} \] 4. Calculate the Pearson's index of skewness: \[ \text{Pearson's Index of Skewness} = \frac{3 - 3}{\sqrt{2}} = 0 \] Since the Pearson's index of skewness is 0, it indicates that the distribution is not significantly skewed. Therefore, the distribution is not significantly skewed.

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To calculate Pearson's index of skewness, you'll use the formula: \( \text{Skewness} = 3 \times \left( \frac{\text{Mean} - \text{Median}}{\text{Standard Deviation}} \right) \). Once you've rounded your mean, median, and standard deviation to one decimal place, plug those values into the formula. If the skewness value is close to 0, the distribution is symmetric; if it's positive, it leans right, and if negative, it leans left. A skewness of more than 0.50 or less than -0.50 is typically considered significant. Real-world applications of skewness can be found in fields like finance, where understanding the distribution of asset returns helps gauge risk. For instance, if a mutual fund has a positively skewed return distribution, it might have a few large gains that could offset the smaller, more frequent losses, affecting investment strategies. Recognizing skewness can guide investors in making informed decisions, especially in volatile markets.

preguntas relacionadas

Question 12(Mulliple Choice Warth 5 points) \[ (04.06 \mathrm{HC}) \] A researcher wants to test the claim that the proportion of juniors who watch television regularly is greater than the proportion of seniors who watch television regularly She finds that 56 of 70 randomly selected juniors and 47 of 85 randomly selected seniors report watching television regularly. Construct \( 95 \% \) confidence intervals for each population proportion. Which of the statemente gives the correct outcome of the research or's tert of the dalim? The \( 95 \% \) confidence interval for juniors is (706, 894), and the \( 95 \% \) confidence interval for seniors is ( 447,659 ). Since the intervals overlap, there is not enough evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors. The \( 95 \% \) confidence interval for juniors is (721, 879), and the \( 95 \% \) confidence interval for seniors is (464, 642). Since the interval for juniors is higher than the interval for seniors, there is evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors. The \( 95 \% \) confidence interval for juniors is ( 706,894 ), and the \( 95 \% \) confidence interval for seniors is ( 447,659 ). Since the interval for juniors is higher than the interval for seniors, there is evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors. The \( 95 \% \) confidence interval for juniors is ( \( 721, .879 \) ), and the \( 95 \% \) confidence interval for seniors is (464, 642). Since the intervals overlap, there is not enough evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors.

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