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"Use the patient data to...

"Use the patient data to do the following:

(a) Compute the mean, median, first quartile, and third quartile for the income variable.

(b) Compute the variance, standard deviation, range, interquartile range, coefficient of variation, skewness, and Z scores for the income variable.

(c) Are the data skewed? If so, how?

(d) Based on the results of (a) through (c), what conclusions can you reach concerning patient income?

(e) Calculate the proportion of patient incomes that are +/- 1, +/- 2, and +/- 3 standard deviations of the mean.

(f) Compare and contrast your findings with what would be expected on the basis of the empirical rule.

Excel Tips:

Excel has built-in functions to calculate the mean (AVERAGE), median (MEDIAN), quartiles (QUARTILE), variance (VAR for samples, VARP for populations), and standard deviation (STDEV for samples, STDEVP for populations). You might also think to use the MIN and MAX commands in calculating range. Search the Excel helpfile for the appropriate syntax of these commands. You should note that the method Excel uses to calculate quartiles differs slightly from the method outlined in the book.

I mentioned the COUNTIF command above in the Excel Tips accompanying 1.2, and you may have thought to use COUNTIF to accomplish part (e). However, a single COUNTIF command cannot handle more than one condition, so if you want to use COUNTIF you should absolutely (hint hint) think outside the box a bit. Or, if you are using Excel 2007 or later, there is a COUNTIFS command that handles multiple conditions." Problem 1.5

"Use the patient data to do the following:

(a) Compute the covariance between income and amount past due.

(b) Compute the coefficient of correlation between income and amount past due.

(c) Construct a scatterplot between income and amount past due with income on the Y-Axis.

(d) Which of (a)-(c) do you think is the most valuable means to understand the relationship between income and amount past due?

(e) What conclusions can you reach about the relationship between house income and amount past due?

Excel Tips:

Excel has built-in functions to calculate the covariance (COVAR) and correlation coefficient (CORREL). Search the Excel helpfile for the appropriate syntax of these commands. " Income Past Due Amount Insurance Years Since Last Visit Year Born Age Gender Hospitalizations Ethnicity Marital Status

25967 0 Medicare 1 1940 72 Male 9 Caucasian Divorced

93126 0 Medicare 0 1938 74 Female 9 Caucasian Divorced

29863 0 Medicare 3 1940 72 Female 9 Caucasian Divorced

152525 0 Medicare 0 1939 73 Female 3 Caucasian Married

7277 0 None 0 1991 21 Female 0 Asian Divorced

188619 112 Private 0 1973 39 Female 4 African American Divorced

20578 183 Medicaid 2 1987 25 Female 2 Caucasian Married

45927 0 Medicare 0 1932 80 Female 3 Caucasian Married

69403 0 Private 0 1971 41 Male 2 Caucasian Divorced

17826 338 Private 0 1966 46 Female 4 Caucasian Married

134442 38 Private 1 1949 63 Female 3 Caucasian Married

6468 111 Private + Medicare 0 1941 71 Male 4 Caucasian Divorced

19215 0 None 1 1963 49 Male 4 Caucasian Married

36404 343 Medicare 3 1933 79 Male 11 Caucasian Married

22708 0 Medicaid 4 1977 35 Female 3 Caucasian Single

7603 3815 Private 0 1950 62 Male 3 Asian Married

57471 324 Medicare 0 1933 79 Female 3 Caucasian Divorced

176686 270 Private 0 1962 50 Female 0 Caucasian Married

160798 0 Private 0 1992 20 Female 1 Caucasian Separated

61134 0 Medicare 0 1940 72 Male 4 Hispanic Married

28663 0 Medicaid 0 1967 45 Male 3 Caucasian Divorced

180261 0 Private 0 1951 61 Female 6 Other Married

26684 0 Medicaid 0 1947 65 Male 3 Caucasian Divorced

286416 5 Private + Medicare 0 1941 71 Female 3 Caucasian Widowed NNN-NN-NNNNPrivat e 1 1963 49 Male 1 Asian Married

20725 420 Medicaid 0 1950 62 Female 4 Caucasian Married NNN-NN-NNNNMedicaid 1 1974 38 Male 3 Caucasian Divorced

247492 0 Medicare 1 1935 77 Female 9 Caucasian Divorced

116300 342 Private + Medicare 0 1944 68 Female 2 Cauc asian Separated

19147 22 Medicaid 0 1963 49 Male 1 Asian Married

9767 0 Private 0 1983 29 Male 3 Hispanic Single

44196 0 Medicare 0 1941 71 Female 6 Caucasian Married

23745 112 Medicaid 0 1955 57 Female 1 Caucasian Married

78770 0 Private 0 1959 53 Male 4 Caucasian Separated

26861 0 Medicare 0 1941 71 Male 2 Hispanic Widowed

91325 476 Medicare 2 1935 77 Female 8 Other Separated

21630 0 Medicaid 2 1985 27 Female 2 Hispanic Divorced

141267 645 Private 0 1958 54 Male 4 Hispanic Married

79202 77 None 1 1978 34 Male 0 Caucasian Single

27084 0 Medicaid 0 1950 62 Male 6 Caucasian Married

29400 50 Medicaid 0 1985 27 Female 3 Caucasian Single

37131 454 Medicaid 3 1971 41 Female 4 Hispanic Married

107750 353 Private 0 1955 57 Female 5 Caucasian Married

7368 0 None 0 1991 21 Male 2 Caucasian Married

256314 0 Medicare 3 1935 77 Female 10 Caucasian Widowed

94430 0 Private 3 1975 37 Male 0 Caucasian Divorced

101372 109 Private 3 1988

(a) Compute the mean, median, first quartile, and third quartile for the income variable.

(b) Compute the variance, standard deviation, range, interquartile range, coefficient of variation, skewness, and Z scores for the income variable.

(c) Are the data skewed? If so, how?

(d) Based on the results of (a) through (c), what conclusions can you reach concerning patient income?

(e) Calculate the proportion of patient incomes that are +/- 1, +/- 2, and +/- 3 standard deviations of the mean.

(f) Compare and contrast your findings with what would be expected on the basis of the empirical rule.

Excel Tips:

Excel has built-in functions to calculate the mean (AVERAGE), median (MEDIAN), quartiles (QUARTILE), variance (VAR for samples, VARP for populations), and standard deviation (STDEV for samples, STDEVP for populations). You might also think to use the MIN and MAX commands in calculating range. Search the Excel helpfile for the appropriate syntax of these commands. You should note that the method Excel uses to calculate quartiles differs slightly from the method outlined in the book.

I mentioned the COUNTIF command above in the Excel Tips accompanying 1.2, and you may have thought to use COUNTIF to accomplish part (e). However, a single COUNTIF command cannot handle more than one condition, so if you want to use COUNTIF you should absolutely (hint hint) think outside the box a bit. Or, if you are using Excel 2007 or later, there is a COUNTIFS command that handles multiple conditions." Problem 1.5

"Use the patient data to do the following:

(a) Compute the covariance between income and amount past due.

(b) Compute the coefficient of correlation between income and amount past due.

(c) Construct a scatterplot between income and amount past due with income on the Y-Axis.

(d) Which of (a)-(c) do you think is the most valuable means to understand the relationship between income and amount past due?

(e) What conclusions can you reach about the relationship between house income and amount past due?

Excel Tips:

Excel has built-in functions to calculate the covariance (COVAR) and correlation coefficient (CORREL). Search the Excel helpfile for the appropriate syntax of these commands. " Income Past Due Amount Insurance Years Since Last Visit Year Born Age Gender Hospitalizations Ethnicity Marital Status

25967 0 Medicare 1 1940 72 Male 9 Caucasian Divorced

93126 0 Medicare 0 1938 74 Female 9 Caucasian Divorced

29863 0 Medicare 3 1940 72 Female 9 Caucasian Divorced

152525 0 Medicare 0 1939 73 Female 3 Caucasian Married

7277 0 None 0 1991 21 Female 0 Asian Divorced

188619 112 Private 0 1973 39 Female 4 African American Divorced

20578 183 Medicaid 2 1987 25 Female 2 Caucasian Married

45927 0 Medicare 0 1932 80 Female 3 Caucasian Married

69403 0 Private 0 1971 41 Male 2 Caucasian Divorced

17826 338 Private 0 1966 46 Female 4 Caucasian Married

134442 38 Private 1 1949 63 Female 3 Caucasian Married

6468 111 Private + Medicare 0 1941 71 Male 4 Caucasian Divorced

19215 0 None 1 1963 49 Male 4 Caucasian Married

36404 343 Medicare 3 1933 79 Male 11 Caucasian Married

22708 0 Medicaid 4 1977 35 Female 3 Caucasian Single

7603 3815 Private 0 1950 62 Male 3 Asian Married

57471 324 Medicare 0 1933 79 Female 3 Caucasian Divorced

176686 270 Private 0 1962 50 Female 0 Caucasian Married

160798 0 Private 0 1992 20 Female 1 Caucasian Separated

61134 0 Medicare 0 1940 72 Male 4 Hispanic Married

28663 0 Medicaid 0 1967 45 Male 3 Caucasian Divorced

180261 0 Private 0 1951 61 Female 6 Other Married

26684 0 Medicaid 0 1947 65 Male 3 Caucasian Divorced

286416 5 Private + Medicare 0 1941 71 Female 3 Caucasian Widowed NNN-NN-NNNNPrivat e 1 1963 49 Male 1 Asian Married

20725 420 Medicaid 0 1950 62 Female 4 Caucasian Married NNN-NN-NNNNMedicaid 1 1974 38 Male 3 Caucasian Divorced

247492 0 Medicare 1 1935 77 Female 9 Caucasian Divorced

116300 342 Private + Medicare 0 1944 68 Female 2 Cauc asian Separated

19147 22 Medicaid 0 1963 49 Male 1 Asian Married

9767 0 Private 0 1983 29 Male 3 Hispanic Single

44196 0 Medicare 0 1941 71 Female 6 Caucasian Married

23745 112 Medicaid 0 1955 57 Female 1 Caucasian Married

78770 0 Private 0 1959 53 Male 4 Caucasian Separated

26861 0 Medicare 0 1941 71 Male 2 Hispanic Widowed

91325 476 Medicare 2 1935 77 Female 8 Other Separated

21630 0 Medicaid 2 1985 27 Female 2 Hispanic Divorced

141267 645 Private 0 1958 54 Male 4 Hispanic Married

79202 77 None 1 1978 34 Male 0 Caucasian Single

27084 0 Medicaid 0 1950 62 Male 6 Caucasian Married

29400 50 Medicaid 0 1985 27 Female 3 Caucasian Single

37131 454 Medicaid 3 1971 41 Female 4 Hispanic Married

107750 353 Private 0 1955 57 Female 5 Caucasian Married

7368 0 None 0 1991 21 Male 2 Caucasian Married

256314 0 Medicare 3 1935 77 Female 10 Caucasian Widowed

94430 0 Private 3 1975 37 Male 0 Caucasian Divorced

101372 109 Private 3 1988

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