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Homework answers / question archive / 1)The terms central location or central tendency refer to the way quantitative data tend to cluster around some middle or central value

1)The terms central location or central tendency refer to the way quantitative data tend to cluster around some middle or central value.

- the median is the middle value of a data set.
- The median is always the 50
^{th}percentile. If*n*is odd,*L*_{50}= (*n*+ 1)/2 is an integer directly defining the unique middle position in the sorted data set. If*n*is even,*L*_{50}= (*n*+ 1)/2 is the average of the two middle positions*n*/2 and*n*/2 + 1, and hence the median is the average of the corresponding two middle values. - The median is defined as
- The mode is defined as
- Which of the following is the most influenced by outliers?
- How do we find the median if the number of observations in a data set is odd
- Is it possible for a data set to have no mode?
- Is it possible for a data set to have more than one mode?
- The Boom company has recently decided to raise the salaries of all employees by 10 percent. Which of the following is (are) expected to be affected by this raise?
- The owner of a company has recently decided to raise the salary of one employee, who was already making the highest salary, by 20%. Which of the following is (are) expected to be affected by this raise?
- The table below gives the deviations of a portfolio's annual total returns from its benchmark's annual returns, for a 6-year period ending in 2011.

The arithmetic mean return and median return are*closest*to: - (Sum of all deviation in prtofolio) / ( number of deviations in portfolio) = Arithmetic mean return

- Approximately 60% of the observations in a data set fall below the 60
^{th}percentile. - In a data set, an outlier is a large or small value regarded as an extreme value in the data set
- A box plot is useful when comparing similar information gathered at different places or times
- Which of the following statements is
*most*accurate when defining percentiles?

- Approximately
*p*% of the observations are less than the*p*^{th}percentile, and approximately (100-*p*)% of the observations are greater than the*p*^{th}percentile. - Which five values are graphed on a box plot?
- What is the interquartile range?
- Consider the following data: 1, 2, 4, 5, 10, 12, 18. The 30
^{th}percentile is*closest*to - (The digits after the decimal point is the percentage between two #'s in the data)( for example 2.4 means 40% between two terms since after decimal point it says 0.40)

- Calculate the interquartile range from the following data: 1, 2, 4, 5, 10, 12, 18?

- The geometric mean is a multiplicative average of a data set.
- In what way(s) is the concept of geometric mean useful?
- The concept of geometric mean is used to evaluate investment returns and calculate average growth rates
- What is/are characteristic(s) of the geometric mean?
- The geometric mean is never greater than the arithmetic mean; it is also less sensitive to outliers.
- Sales for Adidas grew at a rate of 0.5196 in 2006, 0.0213 in 2007, 0.0485 in 2008, and -0.0387 in 2009. The average growth rate for Adidas during these four years is
*closest*to:

- that this formula involes finding the" 4th root",theirs also such thing called the" cubed root(3)"or the "square root"
- if you lack 4root on calculator simnply put find the square roots square root.
- , it only asks for the 4th root because theirs for years of growth rates, if it was the 3 years of rates it would be cubed root, or twos years would be square root
- Distractors: Wrong answers include arithmetic mean, median, and using positive value of growth rate instead of negative value of growth rate in last year.
- Total Revenue, in $millions, for Apple Computers was 42,905 in 2009, 65,225 in 2010, and 108,249 in 2011. The average growth rate of revenue during these three years is
*closest*to: - for find average revenue you only need the first and last years of revenue numbers, ignore the ones in the middle, thus you only need to use the square root not cubed.

- The following data represent monthly returns (in percent):

The geometric mean return is*closest*to: - you find the product of all terms plus 1 to each term, then Find the 5th root of the product since theirs 5 terms. then Subtract 1.

A portfolio manager generates a 5% return in 2008, a 12% return in 2009, a negative 6% return in 2010, and a return of 2% (non-annualized) in the first quarter of 2011. The annualized return for the entire period is*closest*to:- you find the 3.25th root instead of the full 4th root.

- The MAD is a much more effective measure. The average deviation from the mean is actually useless because it is always zero.
- The variance and standard deviation are the most widely used measures of dispersion
- The standard deviation is the positive square root of the variance
- The variance is an average squared deviation from the mean
- The coefficient of variation is a unit free measure of dispersion.
- A portfolio's annual total returns (in percent) for a 5-year period are:

The median and the standard deviation for this sample are*closest*to:

- Mean-variance analysis suggests that investments with lower average returns are also associated with lower risks
- The Sharpe ratio measures the extra reward per unit of risk
- For
*k*> 1, Chebyshev's theorem is useful in estimating the proportion of observations that fall within: - For any data set and
*k*> 1, at least (1 - 1/*k*^{2})100% of observations lie within*k*standard deviations from the mean. - The Sharpe ratio measures :
- Chebyshev's theorem is valid for both sample and population data
- The empirical rule is only applicable for approximately bell-shaped data.
- Chebyshev's theorem is applicable when the data are :
- What capabilities do Chebyshev's theorem and the empirical rule provide? What is the difference between Chebyshev's theorem and the empirical rule?
- In its standard form, Chebyshev's theorem provides a lower bound on
- The empirical rule can be used to estimate some proportions when data has what shape?

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