CUMULATIVE PERCENTAGES AND PERCENTILE RANKS

A cumulative percentage distribution involves the summing of percentages from the top of a table to the bottom. Therefore, the bottom category has a cumulative percentage of 100. Cumulative
percentages can also be used to determine percentile ranks, especially when discussing standardized test scores. For example, if 75% of a group scored equal to or lower than a particular examinee’s
score, then that examinee’s rank is at the 75th percentile. When reported as a percentile rank, the percentage is often rounded to a whole number. Percentile ranks can be used to analyze ordinal
data that can be assigned to categories able to be ranked. Percentile ranks and cumulative percentages are often applied to exam scores, but may be used in any frequency distribution where subjects
have only one value for a variable. For example, demographic characteristics are usually reported with the frequency (f) or number (n) of subjects and percentage of subjects (%) for each level of a
demographic variable. Income level for 200 subjects is presented as an example.
Income Level
Frequency (f)
Percentage (%)
Cumulative %
1. < $40,000
20
10
10
2. $40,000–59,999
50
25
35
3. $60,000–79,999
80
40
75
4. $80,000–100,000
40
20
95
5. > $100,000
10
5
100

Katsma and Souza (2000) conducted a descriptive study using a convenience sample of long-term care nurses from six rural counties in California to evaluate the nurses’ knowledge base of assessment
and management of pain in the elderly. Questionnaires were mailed to selected nursing homes and 89 nurses responded. The nurses reviewed two scenarios and responded to questions related to these
scenarios. The scenarios were identical, except one involved a smiling patient and the other a grimacing patient. The researchers found that the nurses surveyed “were more likely to believe and
document the grimacing patient’s self-report of pain than the smiling patient” (Katsma & Souza, 2003, p. 88). Fewer than half of the respondents chose to increase the analgesic dose for either
patient scenario. “Nursing implications include the importance of ongoing pain assessment and management education tailored to the geriatric population and in long term care” (Katsma & Souza, 2000,
p. 88).
Relevant Study Results
Three tables adapted from Katsma and Souza’s (2000) study are presented in this section. The tables address the research questions and include the number (n) and percent (%) of the nurses’
assessment of elders’ pain score and their medication management of that pain. The tables might have been clearer if an f had been used for frequencies versus the n. Tables 1a and 1b contain the
nurses’ private opinions and documentations of their assessment of the patients’ self-reported pain score. Table 2 shows the numbers and percentages of nurses’ medication choices for the management
of the elders’ pain score. The younger nurses with less clinical experience were more likely to believe and document their patients’ self-report of pain and to manage that pain with medication than
the older more experienced nurses (Katsma & Souza, 2000).
TABLE 1a Nurses’ Belief and Documentation of Assessment of the Smiling Patient
OPINION
DOCUMENTATION
Pain Assessment Scale
n
%
Cumulative %
N
%
Cumulative %
0
7
8.1
8.1
3
3.5
3.5
1
7
8.1
16.2
5
5.8
9.3
2
5
5.8
22.0
6
7.0
16.3
3
8
9.4
31.4
4
4.7
21.0
4
10
11.6
43.0
4
4.7
25.7
5
11
12.8
55.8
7
8.0
33.7
6
5
5.8
61.6
1
1.2
34.9
7
2
2.3
63.9
0
0.0
34.9
8*
31
36.1
100.0
56
65.1
100.0
9
0
0.0
100.0
0
0.0
100.0
10
0
0.0
100.0
0
0.0
100.0
Adapted from Katsma, D. L., & Souza, C. H. (2000). Elderly pain assessment and pain management knowledge of long-term care nurses. Pain Management Nursing, 1 (3), p. 91. Copyright © 2000 with
permission from The American Society for Pain Management Nursing.
* Correct Answer.
TABLE 1b Nurses’ Belief and Documentation of Assessment of the Grimacing Patient
OPINION
DOCUMENTATION
Pain Assessment Scale
n
%
Cumulative %
N
%
Cumulative %
0
0
0.0
0.0
0
0.0
0.0
1
0
0.0
0.0
0
0.0
0.0
2
1
1.2
1.2
1
1.2
1.2
3
1
1.2
2.4
1
1.2
2.4
4
1
1.2
3.6
1
1.2
3.6
5
4
4.7
8.3
4
4.7
8.3
6
8
9.3
17.6
7
8.0
16.3
7
8
9.3
26.9
3
3.5
19.8
8*
48
55.8
82.7
60
69.8
89.6
9
8
9.3
92.0
6
7.0
96.6
10
7
8.0
100.0
3
3.4
100.0
* Correct Answer.
TABLE 2 Nurses’ Medication Choice
SMILING PATIENT
GRIMACING PATIENT
Medication Choice
n
%
Cumulative %
n
%
Cumulative %
No pain medication now
9
10.5
10.5
0
0.0
0.0
Extra Strength Tylenol
17
19.8
30.3
2
2.3
2.3
Vicodin 1 Tablet
33
38.4
68.7
37
43.0
45.3
Vicodin 2 Tablets*
26
30.1
98.8
47
54.7
100.0
Response Missing
1
1.2
100.0
0
0.0
100.0

STUDY QUESTIONS
1. What number and percentage of nurses documented the correct pain assessment score for the grimacing patient?
2. What number of nurses and cumulative percentage of nurses had an opinion lower than the self-reported pain score of the smiling patient?
3. How many nurses undermedicated the smiling patient?
4. What cumulative percentage of nurses undermedicated the grimacing patient?
5. What number of nurses and percentage of nurses chose the correct medication plan for the grimacing patient?
6. How many nurses had an opinion that differed from the grimacing patient’s self-report of a pain score of 8?
7. What cumulative percentage of nurses’ opinions was that the grimacing patient was in less pain than reported?
8. What number and percentage of nurses documented the smiling patient’s pain score at or below 6?
9. What percentage of nurses documented a pain score higher than the correct score for the grimacing patient? Compare that percentage with the percentage of nurses whose opinion was that the
grimacing patient was in more pain than reported.
10. Why do you think so many nurses undermedicated the grimacing patient?

ANSWERS TO STUDY QUESTIONS
1. 60 nurses or 69.8% of the nurses documented a pain score of 8 for the grimacing patient. The key for Table 1b indicates that * designates the correct answer; thus 8* is the correct pain
score for the grimacing patient.
2. 55 nurses or 63.9% of the nurses had the opinion that the smiling patient’s actual pain was less than his self-report. The 63.9% is obtained from Table 1a, and the figure of 55 nurses is
obtained by adding the number of nurses whose opinions were that the pain score was less than 8 or those who gave a score of 0 to 7.
3. 59 nurses undermedicated the smiling patient. This number is obtained by adding the number of nurses giving no pain medication (9), the number giving extra-strength Tylenol (17), and the
number giving 1 tablet of Vicodin (33), which equals 59 nurses.
4. 45.3% of nurses undermedicated the grimacing patient, which is found in Table 2.
5. 47 nurses or 54.7% chose to medicate the grimacing patient with 2 Vicodin tablets, which was the correct choice of medication indicated in Table 2.
6. 38 nurses’ opinions differed from the self-report of an 8 pain score by the grimacing patient. This number is found by adding the number of nurses who had an opinion that the pain score was
less than 8, which was 23 nurses, and those nurses who thought the pain score was greater than 8, which was 15 nurses. Thus, 23 + 15 = 38 nurses’ opinions differed from the reported pain score of 8
(see Table 1b).
7. 26.9% of nurses were of the opinion that the grimacing patient’s actual pain was less than 8 (see Table 1b).
8. 30 (34.9%) of the nurses documented a pain score of 6 or less for the smiling patient (see Table 1a).
9. 10.4% of nurses documented a higher pain score than the correct score of 8. This number is obtained by adding the percent of nurses giving a score of 9 and 10. 17.3% of the nurses were of
the opinion that the grimacing patient’s actual pain was higher than his self-reported pain score of 8. Thus, not all the nurses who had the opinion that the patients were in more pain (17.3%) than
reported documented (10.4%) his or her opinion. The difference was 17.3% – 10.4% = 6.9%.
10. Answers may vary. The grimacing patient may have been undermedicated for any of the following reasons: limited pain assessment skills of the nurse; underestimating the medication needed to
treat the pain; lack of knowledge or experience with pain medications; reluctance to give narcotics to the elderly; or nurses are often very cautious about overmedicating patients, sometimes
resulting in undermedication.

? EXERCISE 6 Questions to be Graded
1. What number and percentage of nurses documented the correct pain assessment for the smiling patient?
2. What number and cumulative percentage of nurses had an opinion that the smiling patient had a 5 or lower pain score?
3. What number and percentage of nurses chose the correct medication plan for the smiling patient?
4. What number and percentage of nurses documented the pain score higher than the correct score for the smiling patient?
5. What number and percentage of nurses documented a different pain score from the grimacing patient’s self-reported pain score of 8?
6. What cumulative percentage of nurses’ opinions was that the smiling patient was in less pain than reported?
7. What number and percentage of the nurses documented the grimacing patient’s pain score at or below 6?
8. Why do you think so many nurses undermedicated the smiling patient?
9. Is this study only applicable to the elderly population? Do you think younger patients’ self-reports of pain are believed and their pain appropriately treated?
10. What can you learn from this study for your practice?
(Grove 35)
Grove, Susan K. Statistics for Health Care Research: A Practical Workbook. W.B. Saunders Company, 022007. VitalBook file.
EXERCISE 8 INTERPRETING LINE GRAPHS
STATISTICAL TECHNIQUE IN REVIEW
Tables and figures are commonly used to present findings from a study or to provide a way for researchers to become familiar with research data (Burns & Grove, 2005). Using tables and figures,
researchers are able to illustrate the results from descriptive data analyses, assist in identifying patterns in data, and interpret exploratory findings. A line graph is a figure that is developed
by joining a series of points with a line to show how a variable changes over time. A line graph includes a horizontal scale or x-axis and a vertical scale or y-axis. The x-axis is used to document
time, and the y-axis is used to document the number or quantity of a variable. Below is an example line graph that documents time in weeks on the x-axis and weight loss in pounds on the y-axis.

RESEARCH ARTICLE
Source: Kang, N. M., Song, Y., Hyun, T. H., & Kim, K. N. (2005). Evaluation of the breastfeeding intervention program in a Korean community health center. International Journal of Nursing Studies,
42 (4), 409–13.
Introduction
Kang, Song, Hyun, and Kim (2005) conducted an observational study to examine a new breastfeeding intervention implemented in a Korean community health center. The purpose of the study was to
determine if breastfeeding rates increased after trained health care professionals and peers gave information on breastfeeding to pregnant and lactating women. Breastfeeding rates after the
educational program significantly increased, indicating that the community-based breastfeeding intervention program was effective in promoting breastfeeding among these women (Kang et al., 2005).
Relevant Study Results
The researchers presented their results in a line graph format to display outcomes comparing the pre-intervention group to the post-intervention group (see Figures 1 to 3). The x-axis represents
age of the babies in months in the three figures, and the y-axis represents breastfeeding rate in Figure 1, formula-feeding rate in Figure 2, and mixed-feeding rate in Figure 3.
FIGURE 1 Breastfeeding rate of pre- and post-intervention (* Significance <0.05 by ?2-test).

FIGURE 2 Formula-feeding rate of pre- and post-intervention (* Significance <0.05 by ?2-test).

FIGURE 3 Mixed-feeding rate of pre- and post-intervention.

STUDY QUESTIONS
1. Which axis shows the length of time of the study? Provide a rationale for the use of length of time in a line graph. What time interval was used in this study?
2. According to the line graphs in Figures 1–3, this study included babies up to how many months old?
3. What was the breastfeeding rate pre-intervention at 1 month?
4. What was the formula-feeding rate for babies pre-intervention at 5 months? Was this pre-intervention rate significantly different from the post-intervention rate? Provide a rationale for
your answer.
5. Was there a significant difference in breastfeeding pre- and post-intervention? Provide a rationale for your answer.
6. The highest percentage of formula-feeding occurred during which pre-intervention month? Was this an expected or unexpected finding? Provide a rationale for your answer.
7. What information does Figure 1 provide you about the effectiveness of the breastfeeding educational program?
8. What percentages of 7-month-old babies were breastfed pre-intervention? Was this breastfeeding rate significantly different from the post-intervention breastfeeding rate? Provide a
rationale for your answer.
9. Were formula-feeding rates affected by the intervention? Provide a rationale for your answer.
ANSWERS TO STUDY QUESTIONS
1. The x-axis of a line graph shows the length of time examined in a study. The use of time in a line graph helps you to see how a variable changes or varies over time. In this study, time was
measured in months to show a trend of feeding methods used for new babies over the course of 12 months.
2. This study included babies up to 12 months old.
3. The breastfeeding rate pre-intervention was 30% for the 1-month-old infants.
4. The pre-intervention formula-feeding rate for babies at 5 months was 60%. At 5 months the pre-intervention and post-intervention rates were significantly different, as indicated by the
asterisk (*) below the 5. The * indicates significant differences at p < 0.05.
5. Yes, at 1 month and 9 months there were significant differences in the breastfeeding rates pre- and post-intervention. This is represented by *s on the x-axis at 1 and 9 months, indicating
that at these two particular months, there were significant differences in pre- and post-intervention at p < 0.05. The content presented from the research article indicated that breastfeeding rates
after the educational program significantly increased, indicating that the community-based breastfeeding intervention program was effective in promoting breastfeeding among these women (Kang et
al., 2005).
6. The highest percentage of formula-feeding occurred at 12 months pre-intervention. This is an expected finding considering that most women decrease the amount of breastfeeding to their child
as the child gets older. So the 12th and final month of the study would be expected to show the highest formula-feeding rate.
7. Figure 1 indicates that breastfeeding rates for the post-intervention group were higher every month than those for the pre-intervention group. Although, no statistically significant
differences were found between pre- and post-intervention breastfeeding rates, except for months 1 and 9. Thus, the findings indicate that the educational program was effective in increasing
breastfeeding rates among the women, but with limited significant differences between pre- and post-intervention.
8. At 7 months pre-intervention, the breastfeeding rate was 20%, which was not significantly different from the post-intervention rate since there was no * below the month indicating a
statistically significant difference. In addition, the line diagram indicates very limited difference between the pre- and post-intervention groups at 7 months.
9. Yes. When compared to pre-intervention rates, formula-feeding rates declined post-intervention at the same time that the breastfeeding rates increased. This indicates that people changed
from formula-feeding to breastfeeding after the educational program.
? EXERCISE 8 Questions to be Graded
1. Can the exact percentage for the type of feeding rate per month for the pre- and post-intervention groups be determined from the line graphs? Provide a rationale for your answer.
2. Did the breastfeeding rate decline at the 12th month post-intervention? Provide a rationale for your answer.
3. If the level of statistical significance was determined at p < 0.05 level for this study, at what months were the rates of formula-feeding statistically significant between pre- and post-
interventions? Provide a rationale for you answer.
4. What were the trends for mixed-feeding rates post-intervention? Were these results significant? Provide a rationale for your answer.
5. At 9 months of age, the breastfeeding rate post-intervention (28%) was significantly different from the pre-intervention rate (18%). Is this statement true or false?
6. The breastfeeding rate post-intervention was greater than the pre-intervention rate over the 12 months of the study. Is this statement true or false? Provide a rationale for your answer.
7. Were the mixed-feeding rates for the pre-intervention and post-intervention groups significantly different at 7 months? Provide a rationale for your answer.
8. Do the results of this study support the hypothesis that the breastfeeding program would contribute to an increase in the breastfeeding rate in the community? Provide a rationale for you
answer.
9. Were the breastfeeding rates statistically significant at 1 and 9 months of age? Provide a rationale for your answer.
10. What implications for practice do you note from these study results?
(Grove 49)
Grove, Susan K. Statistics for Health Care Research: A Practical Workbook. W.B. Saunders Company, 022007. VitalBook file.

EXERCISE 27 SIMPLE LINEAR REGRESSION
STATISTICAL TECHNIQUE IN REVIEW
Linear regression provides a means to estimate or predict the value of a dependent variable based on the value of one or more independent variables. The regression equation is a mathematical
expression of a causal proposition emerging from a theoretical framework. The linkage between the theoretical statement and the equation is made prior to data collection and analysis. Linear
regression is a statistical method of estimating the expected value of one variable, y, given the value of another variable, x. The term simple linear regression refers to the use of one
independent variable, x, to predict one dependent variable, y.
The regression line is usually plotted on a graph, with the horizontal axis representing x (the independent or predictor variable) and the vertical axis representing the y (the dependent or
predicted variable) (see Figure 27-1). The value represented by the letter a is referred to as the y intercept or the point where the regression line crosses or intercepts the y-axis. At this point
on the regression line, x = 0. The value represented by the letter b is referred to as the slope, or the coefficient of x. The slope determines the direction and angle of the regression line within
the graph. The slope expresses the extent to which y changes for every 1-unit change in x. The score on variable y (dependent variable) is predicted from the subject’s known score on variable x
(independent variable). The predicted score or estimate is referred to as Y (expressed as y-hat) (Burns & Grove, 2005).
FIGURE 27-1 Graph of a Simple Linear Regression Line

Simple linear regression is an effort to explain the dynamics within a scatter plot by drawing a straight line through the plotted scores. No single regression line can be used to predict with
complete accuracy every y value from every x value. However, the purpose of the regression equation is to develop the line to allow the highest degree of prediction possible, the line of best fit.
The procedure for developing the line of best fit is the method of least squares. If the data were perfectly correlated, all data points would fall along the straight line or line of best fit.
However, not all data points fall on the line of best fit in studies, but the line of best fit provides the best equation for the values of y to be predicted by locating the intersection of points
on the line for any given value of x.
The algebraic equation for the regression line of best fit is: = a + b, where:
is the predicted value of y,
a is the y intercept and represents the value of y when x = 0 (see Figure 27-1),
a is also called the regression constant,
b is the slope of the line that is the amount of change in y for each one unit of change in x,
b is also called the regression coefficient.
In Figure 27-2, the x-axis represents Gestational Age and the y-axis represents Birth Weight. As gestational age increases from 20 weeks to 34 weeks, birth weight also increases. In other words,
the slope of the line is positive. This line of best fit can be used to predict the birth weight (dependent variable) for an infant based on his or her gestational age in weeks (independent
variable). Figure 27-2 is an example of a line of best fit and was not developed from research. In addition, the x-axis was started with 22 weeks rather than 0, which is the usual start in a
regression figure. Using the formula Y = a + bx, the birth weight of a baby born at 28 weeks of gestation is calculated below.
FIGURE 27-2 Example Line of Best Fit for Gestational Age and Birth Weight

Formula: = a + bx
In this example, a = 500, b = 20, and x = 28 weeks
= 500 + 20(28) = 500 + 560 = 1,060 grams
The regression line represents Y for any given value of x. As you can see, some data points fall above the line and some fall below the line. If we substitute any x value in the regression equation
and solve for y, we will obtain Y that will be somewhat different from the actual values. The distance between the Y and the actual value of y is called residual, and this represents the degree of
error in the regression line. The regression line or the line of best fit for the data points is the unique line that will minimize error and yield the smallest residual (Burns & Grove, 2005).
STUDY QUESTIONS
1. What are the variables on the x- and y-axes in Figure 27-2?
2. What is the name of the type of variable represented by x and y in Figure 27-2? Is x or y the score to be predicted?
3. What is the purpose of simple linear regression analysis and the regression equation?
4. What is the point where the regression line meets the y-axis called? Is there more than one term for this point?
5. In = a + bx, is a or b the slope? What does the slope represent in regression analysis?
6. Using the values a = 500 and b = 20 in Figure 27-2, what is the predicted birth weight in grams for an infant at 36 weeks of gestation?
7. Using the values a = 500 and b = 20 in Figure 27-2, what is the predicted birth weight in grams for an infant at 22 weeks of gestation?
8. Using the values a = 500 and b = 20 in Figure 27-2, what is the predicted birth weight in grams for an infant at 35 weeks of gestation?
9. Does Figure 27-2 have a positive or negative slope? Provide a rationale for your answer. Discuss the meaning of the slope of Figure 27-2.
ANSWERS TO STUDY QUESTIONS
1. The x variable is gestational age in weeks, and the y variable is birth weight in grams in Figure 27-2.
2. x is the independent or predictor variable. y is the dependent variable or the variable that is to be predicted by the independent variable, x.
3. Simple linear regression is conducted to estimate or predict the values of a dependent variable based on the values of an independent variable. Regression analysis is used to calculate a
line of best fit based on the relationship of the independent variable x with the dependent variable y. The formula developed with regression analysis can be used to predict the dependent variable
(y) values based on values of the independent variable x.
4. The point where the regression line meets the y-axis is called the y intercept and is also represented by a (see Figure 27-1). a is also called the regression constant. At the y intercept,
x = 0.
5. b is the slope of the line of best fit (see Figure 27-1). The slope of the line indicates the amount of change in y for each one unit of change in x. b is also called the regression
coefficient.
6. Y = a + bx
Y = 500 + 20(36) = 500 + 720 = 1,220 grams
7. Y = a + bx
Y = 500 + 20(22) = 500 + 440 = 940 grams
8. Y = a + bx
Y = 500 + 20(35) = 500 + 700 = 1,200 grams
9. Figure 27-2 has a positive slope since the line extends from the lower left corner to the upper right corner and shows a positive relationship. This line shows that the increase in x
(independent variable) is also associated with an increase in y (dependent variable). Thus, the independent variable gestational age is used to predict the dependent variable of birth weight. As
the weeks of gestation increase, the birth weight in grams also increases, which is a positive relationship.
RESEARCH ARTICLE
Source: LeFlore, J. L., Engle, W. D., & Rosenfeld, C. (2000). Determinants of blood pressure in very low birth weight neonates: Lack of effect of antenatal steroids. Early Human Development, 59
(1), 37–50.
Introduction
LeFlore, Engle, and Rosenfeld (2000) conducted a retrospective, cohort study (Group 1 received antenatal steroids [n = 70]) with matched controls (Group II did not receive antenatal steroids [n =
46]) to examine the effect of antenatal steroids on neonatal blood pressure (BP) in the first 72 hours of life in very low birth weight (VLBW) neonates. Additionally, the effect of other perinatal
factors on BP were studied, which included estimated gestational age (EGA), birth weight (BW), and postnatal age. The results indicate that there are positive linear relationships between BP and
BW, BP and EGA, and BP and postnatal age.
Relevant Study Results
BP for Group I and Group II were compared over the first 72 hours of the neonate’s life. Since there were no significant differences in initial and subsequent measurements of BP between the groups,
subsequent analyses were performed with the groups combined (n = 116). To assess the effect of BW on BP, the infants were grouped into those with BW = 1,000 grams (n = 36) and those with BW 1,001–
1,500 grams (n = 80). The researchers displayed the results of their analyses in figures. Figure 2 displays the relationships between postnatal age in hours and 3 BPs, systolic BP (SBP), diastolic
BP (DBP), and mean BP (MBP), for infants with BW = 1,000 grams. Figure 3 displays the relationship between postnatal age in hours and SBP, DBP, and MBP for infants with a BW 1,001–1,500 grams.
FIGURE 2 Change in (A) systolic blood pressure (SBP), (B) diastolic blood pressure (DBP), and (C) mean blood pressure (MBP) in neonates = 1,000 grams birth weight (n = 36) during the initial
72 hours postnatal. Lines represent means and 95% confidence intervals (p < 0.0001). Equations for lines of best fit were: SBP = 43.2 + 0.17x; DBP = 25.8 + 0.13x; MBP = 32.9 + 0.14x. In each
instance, the y intercept was significantly lower (p < 0.001) than the value for comparable lines of best fit in infants with birth weights 1,001–1,500 grams; however, no significant differences in
slopes for the lines of best fit were observed between the two birth weight groups.

LeFlore, J. L., Engle, W. D., & Rosenfeld, C. (2000). Determinants of blood pressure in very low birth weight neonates: Lack of effect of antenatal steroids. Early Human Development, 59 (1), p. 44
FIGURE 3 Change in (A) systolic blood pressure (SBP), (B) diastolic blood pressure (DBP), and (C) mean blood pressure (MBP) in neonates 1,001–1,500 grams birth weight (n = 80) during the
initial 72 hours postnatal. Lines represent means and 95% confidence intervals (p < 0.0001). Equations for lines of best fit were: SBP = 50.3 + 0.12x; DBP = 30.4 + 0.11x and MBP = 37.4 + 0.12x. In
each instance, the y intercept was significantly greater (p < 0.001) than the value for comparable lines of best fit in infants with birth weight =1,000 grams; however, no significant differences
in the slopes for the lines of best fit were observed between the two birth weight groups. LeFlore, J. L., Engle, W. D., & Rosenfeld, C. (2000). Determinants of blood pressure in very low birth
weight neonates: Lack of effect of antenatal steroids. Early Human Development, 59 (1), p. 45.
EXERCISE 27 Questions to be Graded
1. What are the independent and dependent variables in Figures 2, A, B, and C? How would you describe the relationship between the variables in Figures 2, A, B, and C?
2. What are the independent and dependent variables in Figures 3, A, B, and C? How would you describe the relationship between the variables in Figures 3, A, B, and C?
3. Was there a significant difference in the y intercept for the lines of best fit in Figure 2 from the y intercept for the lines of best fit in Figure 3? Provide a rationale for your answer.
4. Y represents the predicted value of y calculated using the equation Y = a + bx. In Figure 2, the formula for SBP is Y = 43.2 + 0.17x. Identify the y intercept and the slope in this formula.
What does x represent in this formula?
5. In the legend beneath Figure 2, the authors give an equation indicating that systolic blood pressure is SBP = 43.2 + 0.17x. If the value of x is postnatal age of 30 hours, what is the value
for Y or SBP for neonates =1,000 grams? Show your calculations.
6. In the legend beneath Figure 2, the authors give an equation indicating that systolic blood pressure is SBP = 50.3 + 0.12x. If the value of x is postnatal age of 30 hours, what is the value
for Y or SBP for neonates 1,001–1,500 grams? Show your calculations.
7. Compare the SBP readings you found in Questions 5 and 6. Explain the difference in these two readings.
8. In the legend beneath Figure 2, the authors give an equation indicating that diastolic blood pressure is DBP = 25.8 + 0.13x. If the value of x is postnatal age of 30 hours, what is the
value for Y for neonates = 1,000 grams? Show your calculations.
9. In the legend beneath Figure 3, the authors give an equation indicating that diastolic blood pressure is DBP = 30.4 + 0.11x. If the value of x is postnatal age of 30 hours, what is the
value for Y for neonates 1,001–1,500 grams? Show your calculations.
10. In the legend beneath Figure 3, the authors give an equation indicating that diastolic blood pressure is DBP = 30.4 + 0.11x. How different is the DBP when the value of x is postnatal age of
60 hours versus the 30 hours examined in Question 9?
(Grove 199)
Grove, Susan K. Statistics for Health Care Research: A Practical Workbook. W.B. Saunders Company, 022007. VitalBook file.