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Evaluation The process of evaluating health education programs can be complex. Discuss the significance of evaluation in program planning. If you were a director or unit supervisor of a health education program, would you undertake a program evaluation or hire an outside consultant? Explain your answer. .
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The following facts pertain to a noncancelable lease agreement between Windsor Leasing Company and Sheridan Company, a lessee.
Inception date:
May 1, 2017
Annual lease payment due at the beginning of
each year, beginning with May 1, 2017
$21,737.01
Bargain-purchase option price at end of lease term
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Lease term
5
years
Economic life of leased equipment
10
years
Lessor’s cost
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Fair value of asset at May 1, 2017
$93,000
Lessor’s implicit rate
10
%
Lessee’s incremental borrowing rate
10
%
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Click here to view factor tables
(c)
Your answer is partially correct. Try again.
Prepare a lease amortization schedule for Sheridan Company for the 5-year lease term.
(Round present value factor calculations to 5 decimal places, e.g. 1.25125 and Round answers to 2 decimal places, e.g. 15.25.)
SHERIDAN COMPANY (Lessee)
Lease Amortization Schedule
Date
Annual Lease Payment Plus
BPO
Interest on
Liability
Reduction of Lease
Liability
Lease Liability
5/1/17
$
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record depreciation.)
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record interest.)
1/1/18
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record second payament.)
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Pearl Corporation manufactures replicators. On January 1, 2017, it leased to Althaus Company a replicator that had cost $100,000 to manufacture. The lease agreement covers the 5-year useful life of the replicator and requires 5 equal annual rentals of $40,200 payable each January 1, beginning January 1, 2017. An interest rate of 12% is implicit in the lease agreement. Collectibility of the rentals is reasonably assured, and there are no important uncertainties concerning costs.
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.
)
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Date
Account Titles and Explanation
Debit
Credit
January 1, 2017
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record the lease.)
January 1, 2017
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record cost.)
January 1, 2017
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record first lease payment.)
6 years ago
16.01.2017
8
Report Issue
Answer
(
0
)
Bids
(
0
)
other Questions
(
10
)
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Exercise 29
Calculating Simple Linear Regression
Simple linear regression
is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (
Cohen & Cohen, 1983
). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in
Exercise 14
,
Figure 14-1
illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.
A regression equation can be generated with a data set containing subjects'
x
and
y
values. Once this equation is generated, it can be used to predict future subjects'
y
values, given only their
x
values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable
y
(dependent variable, or outcome) is predicted from the same subject's known score on variable
x
(independent variable, or predictor).
Research Designs Appropriate for Simple Linear Regression
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Gliner et al., 2009
). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.
Statistical Formula and Assumptions
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Zar, 2010
):
1.
Normal distribution of the dependent (
y
) variable
2.
Linear relationship between
x
and
y
3.
Independent observations
4.
No (or little) multicollinearity
5.
Homoscedasticity
320
Data that are
homoscedastic
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x
, the distribution of
y
values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (
Cohen & Cohen, 1983
;
Zar, 2010
). Visual examples of homoscedasticity and heteroscedasticity are presented in
Exercise 30
.
In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters
a
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b
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Exercise 19-8 (Part Level Submission)
Wildhorse
Company has the following two temporary differences between its income tax expense and income taxes payable.
2017
2018
2019
Pretax financial income
$799,000
$944,000
$951,000
Excess depreciation expense on tax return
(29,100
)
(39,300
)
(10,200
)
Excess warranty expense in financial income
19,500
9,800
8,100
Taxable income
$789,400
$914,500
$948,900
The income tax rate for all years is 40%.
(a)
Your answer is correct.
Assuming there were no temporary differences prior to 2017, prepare the journal entry to record income tax expense, deferred income taxes, and income taxes payable for 2017, 2018, and 2019.
(Credit account titles are automatically indented when amount is entered. Do not indent manually. If no entry is required, select "No Entry" for the account titles and enter 0 for the amounts.)
Account Titles and Explanation
Debit
Credit
2017
2018
2019
Click if you would like to Show Work for this question:
Open Show Work
SHOW LIST OF ACCOUNTS
SHOW SOLUTION
LINK TO TEXT
LINK TO TEXT
Attempts: 1 of 3 used
(b)
Your answer is partially correct. Try again.
Indicate how deferred taxes will be reported on the 2019 balance sheet.
Wildhorse’s
product warranty is for 12 months.
Wildhorse
Company
Balance Sheet
$
Click if you would like to Show Work for this question:
Open Show Work
SHOW LIST OF ACCOUNTS
SHOW SOLUTION
LINK TO TEXT
LINK TO TEXT
Attempts: 3 of 3 used
(c)
Prepare the income tax expense section of the income statement for 2019, beginning with the line “Pretax financial income.”
(Enter loss using either a negative sign preceding the number e.g. -45 or parentheses e.g. (45).)
Wildhorse
Company
Income Statement (Partial)
$
$
$
Exercise 19-17
Vaughn Co. establishes a $128,000,000 liability at the end of 2017 for the estimated site-cleanup costs at two of its manufacturing facilities. All related closing costs will be paid and deducted on the tax return in 2018. Also, at the end of 2017, the company has $64,000,000 of temporary differences due to excess depreciation for tax purposes, $8,960,000 of which will reverse in 2018.
The enacted tax rate for all years is 40%, and the company pays taxes of $81,920,000 on $204,800,000 of taxable income in 2017. Vaughn expects to have taxable income in 2018.
Determine the deferred taxes to be reported at the end of 2017.
Deferred tax assets
$
Deferred tax liabilities
$
SHOW LIST OF ACCOUNTS
LINK TO TEXT
LINK TO TEXT
Indicate how the deferred taxes computed above are to be reported on the balance sheet.
Vaughn
Co.
Balance Sheet
$
SHOW LIST OF ACCOUNTS
LINK TO TEXT
LINK TO TEXT
Assuming that the only deferred tax account at the beginning of 2017 was a deferred tax liability of $
12,800,000,
draft the income tax expense portion of the income statement for 2017, beginning with the line “Income before income taxes.” (
.
Exercise 14Understanding Simple Linear RegressionStatistical Tec.docx
Exercise 14
Understanding Simple Linear Regression
Statistical Technique in Review
In nursing practice, the ability to predict future events or outcomes is crucial, and researchers calculate and report linear regression results as a basis for making these predictions.
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 focus of this exercise is
simple linear regression
, which involves the use of one independent variable,
x
, to predict one dependent variable,
y
.
The regression line developed from simple linear regression 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 14-1
). 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 one-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
Ŷ
(expressed as
y
-hat) (
Cohen, 1988
;
Grove, Burns, & Gray, 2013
;
Zar, 2010
).
FIGURE 14-1
GRAPH OF A SIMPLE LINEAR REGRESSION LINE
140
Simple linear regression is an effort to explain the dynamics within a scatterplot (see
Exercise 11
) 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
.
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y
=
bx
+
a
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y
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Exercise University CultureThe purpose of this exercise is to e.docx
Exercise: University Culture
The purpose of this exercise is to explore how organizational culture is transmitted through observable artifacts.
The college used will be Texas A&M Central Texas( TAMUCT), and the classes taken are all online.
Each question (
total of 2
) must have
2 complete and fully proofread paragraphs (total of 4 paragraphs).
Use the following steps:
Question 1
- Using the following table, consider the observable artifacts that transmit the organizational culture.
Symbols
Think about the logo and images associated with your university. What messages do they convey about the university culture?
Physical Structure
Think about the most visible physical structure on campus. What do those structures say about your university’s culture?
Language
Think about the jargon, slogans, and saying associated with your university. What insight do they offer into the university’s culture?
Stories
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Rituals
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Ceremonies
What are the formal events and celebrations that occur at your university, and what culture signals do they convey?
Question 2
- Consider the sorts of values listed in the table. If you consider the symbols, physical structures, languages, stories, rituals, and ceremonies identified in step 1, what core values seem to summarize your universities culture? Using a transparency, laptop, or board, list the ones value that seems to be most central to your university’s culture. Then list the three cultural artifacts that are most responsible for transmitting that core value.
.
EXERCISE 27I WILL SEND THE DATA TO WHOM EVER WILL DO THE ASSIGNMEN.docx
EXERCISE 27
I WILL SEND THE DATA TO WHOM EVER WILL DO THE ASSIGNMENT NEEDED NO LATER THAN 11pm
Calculating Descriptive Statistics
There are two major classes of statistics: descriptive statistics and inferential statistics. Descriptive statistics are computed to reveal characteristics of the sample data set and to describe study variables. Inferential statistics are computed to gain information about effects and associations in the population being studied. For some types of studies, descriptive statistics will be the only approach to analysis of the data. For other studies, descriptive statistics are the first step in the data analysis process, to be followed by inferential statistics. For all studies that involve numerical data, descriptive statistics are crucial in understanding the fundamental properties of the variables being studied. Exercise 27 focuses only on descriptive statistics and will illustrate the most common descriptive statistics computed in nursing research and provide examples using actual clinical data from empirical publications.
Measures of Central Tendency
A
measure of central tendency
is a statistic that represents the center or middle of a frequency distribution. The three measures of central tendency commonly used in nursing research are the mode, median (
MD
), and mean (
). The mean is the arithmetic average of all of a variable's values. The median is the exact middle value (or the average of the middle two values if there is an even number of observations). The mode is the most commonly occurring value or values (see
Exercise 8
).
The following data have been collected from veterans with rheumatoid arthritis (
Tran, Hooker, Cipher, & Reimold, 2009
). The values in
Table 27-1
were extracted from a larger sample of veterans who had a history of biologic medication use (e.g., infliximab [Remicade], etanercept [Enbrel]).
Table 27-1
contains data collected from 10 veterans who had stopped taking biologic medications, and the variable represents the number of years that each veteran had taken the medication before stopping.
TABLE 27-1
DURATION OF BIOLOGIC USE AMONG VETERANS WITH RHEUMATOID ARTHRITIS (
n
= 10)
Duration of Biologic Use (years)
0.1
0.3
1.3
1.5
1.5
2.0
2.2
3.0
3.0
4.0
Because the number of study subjects represented below is 10, the correct statistical notation to reflect that number is:
Note that the
n
is lowercase, because we are referring to a sample of veterans. If the data being presented represented the entire population of veterans, the correct notation is the uppercase
N.
Because most nursing research is conducted using samples, not populations, all formulas in the subsequent exercises will incorporate the sample notation,
n.
Mode
The
mode
is the numerical value or score that occurs with the greatest frequency; it does not necessarily indicate the center of the data set. The data in
Table 27-1
contain two
292
modes: 1.5 and 3.0. Each of these numbers occurred twice in the data s.
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
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Exercise 21-8 (Part Level Submission)
The following facts pertain to a noncancelable lease agreement between Windsor Leasing Company and Sheridan Company, a lessee.
Inception date:
May 1, 2017
Annual lease payment due at the beginning of
each year, beginning with May 1, 2017
$21,737.01
Bargain-purchase option price at end of lease term
$3,800
Lease term
5
years
Economic life of leased equipment
10
years
Lessor’s cost
$68,000
Fair value of asset at May 1, 2017
$93,000
Lessor’s implicit rate
10
%
Lessee’s incremental borrowing rate
10
%
The collectibility of the lease payments is reasonably predictable, and there are no important uncertainties surrounding the costs yet to be incurred by the lessor. The lessee assumes responsibility for all executory costs.
Click here to view factor tables
(c)
Your answer is partially correct. Try again.
Prepare a lease amortization schedule for Sheridan Company for the 5-year lease term.
(Round present value factor calculations to 5 decimal places, e.g. 1.25125 and Round answers to 2 decimal places, e.g. 15.25.)
SHERIDAN COMPANY (Lessee)
Lease Amortization Schedule
Date
Annual Lease Payment Plus
BPO
Interest on
Liability
Reduction of Lease
Liability
Lease Liability
5/1/17
$
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record depreciation.)
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record interest.)
1/1/18
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record second payament.)
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.
)
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Debit
Credit
January 1, 2017
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January 1, 2017
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[removed]
[removed]
[removed]
[removed]
[removed]
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6 years ago
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EXERCISE 9.4 Using Structured Interview Questions from the GSS
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Some questions are asked on every survey, other questions have rotated, and some have been used only in a single survey.
The GSS measures a great many variables using answers to questions as indicators.
The variables included on past surveys cover a wide range of behaviors, among them drinking behavior, membership in voluntary associations and the practice of religion, and numerous attitudes, such as opinions about abortion, affirmative action, capital punishment, family roles, and confidence in the military.
Browse through the archives of the GSS to find questions asked between 1972 and 2008, which is available at
http://sda.berkeley.edu/archive.htm
and select a few variables of interest.
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2.Comment on the specific questions that were used. Do you think they are valid indicators for the variables? Are the questions clear and easy to understand?
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Exercise 29Calculating Simple Linear RegressionSimple linear reg.docx
Exercise 29
Calculating Simple Linear Regression
Simple linear regression
is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (
Cohen & Cohen, 1983
). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in
Exercise 14
,
Figure 14-1
illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.
A regression equation can be generated with a data set containing subjects'
x
and
y
values. Once this equation is generated, it can be used to predict future subjects'
y
values, given only their
x
values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable
y
(dependent variable, or outcome) is predicted from the same subject's known score on variable
x
(independent variable, or predictor).
Research Designs Appropriate for Simple Linear Regression
Research designs that may utilize simple linear regression include any associational design (
Gliner et al., 2009
). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.
Statistical Formula and Assumptions
Use of simple linear regression involves the following assumptions (
Zar, 2010
):
1.
Normal distribution of the dependent (
y
) variable
2.
Linear relationship between
x
and
y
3.
Independent observations
4.
No (or little) multicollinearity
5.
Homoscedasticity
320
Data that are
homoscedastic
are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of
x
, the distribution of
y
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Cohen & Cohen, 1983
;
Zar, 2010
). Visual examples of homoscedasticity and heteroscedasticity are presented in
Exercise 30
.
In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters
a
and
b
are computed to obtain a regression equatio.
Exercise 19-8 (Part Level Submission)Wildhorse Company has the f.docx
Exercise 19-8 (Part Level Submission)
Wildhorse
Company has the following two temporary differences between its income tax expense and income taxes payable.
2017
2018
2019
Pretax financial income
$799,000
$944,000
$951,000
Excess depreciation expense on tax return
(29,100
)
(39,300
)
(10,200
)
Excess warranty expense in financial income
19,500
9,800
8,100
Taxable income
$789,400
$914,500
$948,900
The income tax rate for all years is 40%.
(a)
Your answer is correct.
Assuming there were no temporary differences prior to 2017, prepare the journal entry to record income tax expense, deferred income taxes, and income taxes payable for 2017, 2018, and 2019.
(Credit account titles are automatically indented when amount is entered. Do not indent manually. If no entry is required, select "No Entry" for the account titles and enter 0 for the amounts.)
Account Titles and Explanation
Debit
Credit
2017
2018
2019
Click if you would like to Show Work for this question:
Open Show Work
SHOW LIST OF ACCOUNTS
SHOW SOLUTION
LINK TO TEXT
LINK TO TEXT
Attempts: 1 of 3 used
(b)
Your answer is partially correct. Try again.
Indicate how deferred taxes will be reported on the 2019 balance sheet.
Wildhorse’s
product warranty is for 12 months.
Wildhorse
Company
Balance Sheet
$
Click if you would like to Show Work for this question:
Open Show Work
SHOW LIST OF ACCOUNTS
SHOW SOLUTION
LINK TO TEXT
LINK TO TEXT
Attempts: 3 of 3 used
(c)
Prepare the income tax expense section of the income statement for 2019, beginning with the line “Pretax financial income.”
(Enter loss using either a negative sign preceding the number e.g. -45 or parentheses e.g. (45).)
Wildhorse
Company
Income Statement (Partial)
$
$
$
Exercise 19-17
Vaughn Co. establishes a $128,000,000 liability at the end of 2017 for the estimated site-cleanup costs at two of its manufacturing facilities. All related closing costs will be paid and deducted on the tax return in 2018. Also, at the end of 2017, the company has $64,000,000 of temporary differences due to excess depreciation for tax purposes, $8,960,000 of which will reverse in 2018.
The enacted tax rate for all years is 40%, and the company pays taxes of $81,920,000 on $204,800,000 of taxable income in 2017. Vaughn expects to have taxable income in 2018.
Determine the deferred taxes to be reported at the end of 2017.
Deferred tax assets
$
Deferred tax liabilities
$
SHOW LIST OF ACCOUNTS
LINK TO TEXT
LINK TO TEXT
Indicate how the deferred taxes computed above are to be reported on the balance sheet.
Vaughn
Co.
Balance Sheet
$
SHOW LIST OF ACCOUNTS
LINK TO TEXT
LINK TO TEXT
Assuming that the only deferred tax account at the beginning of 2017 was a deferred tax liability of $
12,800,000,
draft the income tax expense portion of the income statement for 2017, beginning with the line “Income before income taxes.” (
.
Exercise 14Understanding Simple Linear RegressionStatistical Tec.docx
Exercise 14
Understanding Simple Linear Regression
Statistical Technique in Review
In nursing practice, the ability to predict future events or outcomes is crucial, and researchers calculate and report linear regression results as a basis for making these predictions.
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 focus of this exercise is
simple linear regression
, which involves the use of one independent variable,
x
, to predict one dependent variable,
y
.
The regression line developed from simple linear regression 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 14-1
). 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 one-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
Ŷ
(expressed as
y
-hat) (
Cohen, 1988
;
Grove, Burns, & Gray, 2013
;
Zar, 2010
).
FIGURE 14-1
GRAPH OF A SIMPLE LINEAR REGRESSION LINE
140
Simple linear regression is an effort to explain the dynamics within a scatterplot (see
Exercise 11
) 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
y
=
bx
+
a
, where:
y
=
dependent
variable
(
outcome
)
x
=
independen.
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"Executive Pay"
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Exercise: University Culture
The purpose of this exercise is to explore how organizational culture is transmitted through observable artifacts.
The college used will be Texas A&M Central Texas( TAMUCT), and the classes taken are all online.
Each question (
total of 2
) must have
2 complete and fully proofread paragraphs (total of 4 paragraphs).
Use the following steps:
Question 1
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Symbols
Think about the logo and images associated with your university. What messages do they convey about the university culture?
Physical Structure
Think about the most visible physical structure on campus. What do those structures say about your university’s culture?
Language
Think about the jargon, slogans, and saying associated with your university. What insight do they offer into the university’s culture?
Stories
What anecdotes, accounts, legends, and myths are associated with your university? What messages do they convey about your university’s culture?
Rituals
What are the daily or weekly routines that occur at your university, and what do they say about the culture?
Ceremonies
What are the formal events and celebrations that occur at your university, and what culture signals do they convey?
Question 2
- Consider the sorts of values listed in the table. If you consider the symbols, physical structures, languages, stories, rituals, and ceremonies identified in step 1, what core values seem to summarize your universities culture? Using a transparency, laptop, or board, list the ones value that seems to be most central to your university’s culture. Then list the three cultural artifacts that are most responsible for transmitting that core value.
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EXERCISE 27
I WILL SEND THE DATA TO WHOM EVER WILL DO THE ASSIGNMENT NEEDED NO LATER THAN 11pm
Calculating Descriptive Statistics
There are two major classes of statistics: descriptive statistics and inferential statistics. Descriptive statistics are computed to reveal characteristics of the sample data set and to describe study variables. Inferential statistics are computed to gain information about effects and associations in the population being studied. For some types of studies, descriptive statistics will be the only approach to analysis of the data. For other studies, descriptive statistics are the first step in the data analysis process, to be followed by inferential statistics. For all studies that involve numerical data, descriptive statistics are crucial in understanding the fundamental properties of the variables being studied. Exercise 27 focuses only on descriptive statistics and will illustrate the most common descriptive statistics computed in nursing research and provide examples using actual clinical data from empirical publications.
Measures of Central Tendency
A
measure of central tendency
is a statistic that represents the center or middle of a frequency distribution. The three measures of central tendency commonly used in nursing research are the mode, median (
MD
), and mean (
). The mean is the arithmetic average of all of a variable's values. The median is the exact middle value (or the average of the middle two values if there is an even number of observations). The mode is the most commonly occurring value or values (see
Exercise 8
).
The following data have been collected from veterans with rheumatoid arthritis (
Tran, Hooker, Cipher, & Reimold, 2009
). The values in
Table 27-1
were extracted from a larger sample of veterans who had a history of biologic medication use (e.g., infliximab [Remicade], etanercept [Enbrel]).
Table 27-1
contains data collected from 10 veterans who had stopped taking biologic medications, and the variable represents the number of years that each veteran had taken the medication before stopping.
TABLE 27-1
DURATION OF BIOLOGIC USE AMONG VETERANS WITH RHEUMATOID ARTHRITIS (
n
= 10)
Duration of Biologic Use (years)
0.1
0.3
1.3
1.5
1.5
2.0
2.2
3.0
3.0
4.0
Because the number of study subjects represented below is 10, the correct statistical notation to reflect that number is:
Note that the
n
is lowercase, because we are referring to a sample of veterans. If the data being presented represented the entire population of veterans, the correct notation is the uppercase
N.
Because most nursing research is conducted using samples, not populations, all formulas in the subsequent exercises will incorporate the sample notation,
n.
Mode
The
mode
is the numerical value or score that occurs with the greatest frequency; it does not necessarily indicate the center of the data set. The data in
Table 27-1
contain two
292
modes: 1.5 and 3.0. Each of these numbers occurred twice in the data s.
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