Module 6 statistics part 2 Questions and
answers With complete solution Updated
100% PASS
Nursing Connections II!
l
How is Regression Analysis used in Nursing? An Example
II! II! II! II! II! II! II! II!
How is Regression Analysis used in Nursing? An Example
II! II! II! II! II! II! II! II!
Regression analysis can be used in a variety of settings in health care. Predicting future health concerns of
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
a patient, future staffing needs or future budgets are just some of the topics that may be reviewed.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis uses multiple variables that are known to be associated to predict future
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
needs/concerns.
II!
Is there a nursing shortage? Regression analysis is used to evaluate supply and demand data for the health
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
care industry and nursing occupations to identify existing and potential nursing workforce shortages
II! II! II! II! II! II! II! II! II! II! II! II! II!
and/or surpluses. Variables such as the number of currently licensed nurses, overall population growth,
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
the number of nursing planning to retire in the next 10 years, and enrollment in nursing education
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
programs are examined to predict future workforce needs within the nursing profession.
II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis* II!
When analyzing the relationship between two or more variables, regression analysis is a helpful tool.
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis* is used when multiple variables' quantities relate to each other. Regression analysis
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
is an extension of scatterplots, associations, and correlations. If we determine that an association exists
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
between two or more variables, we can use regression analysis for description and prediction. Regression
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
is used to describe a trend or predict values based on known values.
II! II! II! II! II! II! II! II! II! II! II! II! II!
Example of Regression Analysis II! II! II!
For example, a patient's weight is associated with blood pressure, cholesterol, and blood sugar. These
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
variables can be modeled with a regression analysis. Once their association has been established and
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
quantified, regression analysis can be used to predict future data values. So, we can quantify all of the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
health risks a patient may have based on a risk factor: weight.
II! II! II! II! II! II! II! II! II! II! II! II!
Recall that the response variable* is the
II! II! II! II! II! II!
variable whose value "responds" to the other variables in the equation; in the previous example, the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
response variable could be blood pressure, cholesterol, or blood sugar, factors that are influenced by a
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
person's weight. We assume in this course that explanatory variables* vary independently; in reality, they
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
may influence one another. For example, people of the same gender and age have a range of different
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
weights; those weights might affect other aspects of their health.
II! II! II! II! II! II! II! II! II! II!
,The response variable is written on the y -axis, while the explanatory variable is written on the x -axis. A
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
simple way to remember this fact is that the term "explanatory" has an 'x' in it.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis can help determine the association between risk factors (explanatory variables) and
II! II! II! II! II! II! II! II! II! II! II! II!
diseases or disorders (response variables). In each case, determine whether the variable in bold is the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
explanatory or response variable by typing "explanatory" or "response" into the textbox.
II! II! II! II! II! II! II! II! II! II! II! II!
1. How does the rate of lung disease depend on tobacco use?
II! II! II! II! II! II! II! II! II! II! II!
Check
How does the rate of lung disease depend on tobacco use?
II! II! II! II! II! II! II! II! II! II!
response is Correct II! II!
×
"The rate of lung disease" is the response variable.
II! II! II! II! II! II! II! II!
2. How does high blood pressure depend on physical inactivity?
II! II! II! II! II! II! II! II! II!
Check
How does high blood pressure depend on physical inactivity?
II! II! II! II! II! II! II! II!
response is Correct II! II!
×
"High blood pressure" is the response variable.
II! II! II! II! II! II!
3. How does blood sugar depend on a diet?
II! II! II! II! II! II! II! II!
Check
How does blood sugar depend on a diet?
II! II! II! II! II! II! II!
explanatory is Correct II! II!
×
"Diet" is the explanatory variable. II! II! II! II!
4. How does bone density depend on age?
II! II! II! II! II! II! II!
Check
. How does bone density depend on age?
II! II! II! II! II! II! II!
Check
explanatory is Correct II! II!
×
"Age" is the explanatory variable.
II! II! II! II!
5. How does mortality rate depend on Body Mass Index (BMI)
II! II! II! II! II! II! II! II! II! II!
5. How does mortality rate depend on Body Mass Index (BMI)
II! II! II! II! II! II! II! II! II! II!
explanatory is Correct II! II!
×
"BMI" is the explanatory variable. II! II! II! II!
,Complete the following sentence. Regression analysis is used to _________________. II! II! II! II! II! II! II! II! II!
calculate the probability that an event will occur. II! II! II! II! II! II! II!
determine if there is an association between two or more variables. II! II! II! II! II! II! II! II! II! II!
predict future values based on known values. II! II! II! II! II! II!
determine a cause and effect relationship between two or more values. II! II! II! II! II! II! II! II! II! II!
. Regression is used to describe a trend or predict future values based on known values.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
If a patient's risk of cancer is associated with genetics, diet, and tobacco use, which of these is the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
response variable.
II! II!
Risk of cancer II! II!
Genetics
Diet
Tobacco Use II!
The answer is a. In this example, the risk of cancer is the response variable because it "responds" to
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
genetics, diet, and tobacco use.
II! II! II! II! II!
If a patient's blood sugar, cholesterol, and blood pressure are all dependent on diet, which of these is the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
explanatory variable
II! II!
Blood sugar II!
Cholesterol level II!
Blood pressure II!
Diet
d is CorrectII! II!
×
The answer is d. In this example, diet is the explanatory variable because all of the other variables respond
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
to it.
II! II!
Which of the following represents the explanatory variable?
II! II! II! II! II! II! II!
Type of Tobacco Used II! II! II!
Cancer Mortality II!
Average Cigarettes Smoked per Day II! II! II! II!
Oxygen Saturation % II! II!
The answer is c. In this chart, Average Cigarettes Smoked per Day is the explanatory variable.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
, . From the chart above, Lung Cancer Mortality represents what type of variable?
II! II! II! II! II! II! II! II! II! II! II! II!
Categorical variable II!
Explanatory variable II!
Descriptive variable II!
Response variable II!
d is Correct II! II!
×
The answer is d. From this this chart, Lung Cancer Mortality, represents the response variable.
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
regression equation II!
`The regression equation is the algebraic equation that models the regression. Normally, the regression
II! II! II! II! II! II! II! II! II! II! II! II! II!
can be of any degree with any number of response variables. It need not be linear*. Here, we will use
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
simple linear regression.
II! II! II!
Simple linear regression* II! II!
Simple linear regression* is the prediction of one response variable's value from one or more explanatory
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
variables' values. In a simple linear regression, we model the relationship between the variables as
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
perfectly linear. So, the closer the data's correlation is to being perfectly linear, the more accurate the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
linear regression model will be.
II! II! II! II! II!
graphing linear regression on a coordinate plane II! II! II! II! II! II!
We are graphing these linear regressions on a coordinate plane. Therefore, simple linear regression is
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
usually represented by an equation in slope-intercept form:
II! II! II! II! II! II! II! II!
Slope-Intercept in Simple Linear Regression II! II! II! II!
y=mx+b
In this equation: II! II!
b is the y -intercept:
II! II! II! II!
In linear regression as with algebra, this is simply the value of y when x=0 .
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
m is the slope: II! II! II!
In regression analysis, for every additional unit of the explanatory variable ( x ), we can predict an increase
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
(or decrease, if negative) of m units of the response variable ( y ).
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
x is the x -value:
II! II! II! II!
This is the explanatory variable. II! II! II! II!
answers With complete solution Updated
100% PASS
Nursing Connections II!
l
How is Regression Analysis used in Nursing? An Example
II! II! II! II! II! II! II! II!
How is Regression Analysis used in Nursing? An Example
II! II! II! II! II! II! II! II!
Regression analysis can be used in a variety of settings in health care. Predicting future health concerns of
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
a patient, future staffing needs or future budgets are just some of the topics that may be reviewed.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis uses multiple variables that are known to be associated to predict future
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
needs/concerns.
II!
Is there a nursing shortage? Regression analysis is used to evaluate supply and demand data for the health
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
care industry and nursing occupations to identify existing and potential nursing workforce shortages
II! II! II! II! II! II! II! II! II! II! II! II! II!
and/or surpluses. Variables such as the number of currently licensed nurses, overall population growth,
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
the number of nursing planning to retire in the next 10 years, and enrollment in nursing education
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
programs are examined to predict future workforce needs within the nursing profession.
II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis* II!
When analyzing the relationship between two or more variables, regression analysis is a helpful tool.
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis* is used when multiple variables' quantities relate to each other. Regression analysis
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
is an extension of scatterplots, associations, and correlations. If we determine that an association exists
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
between two or more variables, we can use regression analysis for description and prediction. Regression
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
is used to describe a trend or predict values based on known values.
II! II! II! II! II! II! II! II! II! II! II! II! II!
Example of Regression Analysis II! II! II!
For example, a patient's weight is associated with blood pressure, cholesterol, and blood sugar. These
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
variables can be modeled with a regression analysis. Once their association has been established and
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
quantified, regression analysis can be used to predict future data values. So, we can quantify all of the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
health risks a patient may have based on a risk factor: weight.
II! II! II! II! II! II! II! II! II! II! II! II!
Recall that the response variable* is the
II! II! II! II! II! II!
variable whose value "responds" to the other variables in the equation; in the previous example, the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
response variable could be blood pressure, cholesterol, or blood sugar, factors that are influenced by a
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
person's weight. We assume in this course that explanatory variables* vary independently; in reality, they
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
may influence one another. For example, people of the same gender and age have a range of different
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
weights; those weights might affect other aspects of their health.
II! II! II! II! II! II! II! II! II! II!
,The response variable is written on the y -axis, while the explanatory variable is written on the x -axis. A
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
simple way to remember this fact is that the term "explanatory" has an 'x' in it.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
Regression analysis can help determine the association between risk factors (explanatory variables) and
II! II! II! II! II! II! II! II! II! II! II! II!
diseases or disorders (response variables). In each case, determine whether the variable in bold is the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
explanatory or response variable by typing "explanatory" or "response" into the textbox.
II! II! II! II! II! II! II! II! II! II! II! II!
1. How does the rate of lung disease depend on tobacco use?
II! II! II! II! II! II! II! II! II! II! II!
Check
How does the rate of lung disease depend on tobacco use?
II! II! II! II! II! II! II! II! II! II!
response is Correct II! II!
×
"The rate of lung disease" is the response variable.
II! II! II! II! II! II! II! II!
2. How does high blood pressure depend on physical inactivity?
II! II! II! II! II! II! II! II! II!
Check
How does high blood pressure depend on physical inactivity?
II! II! II! II! II! II! II! II!
response is Correct II! II!
×
"High blood pressure" is the response variable.
II! II! II! II! II! II!
3. How does blood sugar depend on a diet?
II! II! II! II! II! II! II! II!
Check
How does blood sugar depend on a diet?
II! II! II! II! II! II! II!
explanatory is Correct II! II!
×
"Diet" is the explanatory variable. II! II! II! II!
4. How does bone density depend on age?
II! II! II! II! II! II! II!
Check
. How does bone density depend on age?
II! II! II! II! II! II! II!
Check
explanatory is Correct II! II!
×
"Age" is the explanatory variable.
II! II! II! II!
5. How does mortality rate depend on Body Mass Index (BMI)
II! II! II! II! II! II! II! II! II! II!
5. How does mortality rate depend on Body Mass Index (BMI)
II! II! II! II! II! II! II! II! II! II!
explanatory is Correct II! II!
×
"BMI" is the explanatory variable. II! II! II! II!
,Complete the following sentence. Regression analysis is used to _________________. II! II! II! II! II! II! II! II! II!
calculate the probability that an event will occur. II! II! II! II! II! II! II!
determine if there is an association between two or more variables. II! II! II! II! II! II! II! II! II! II!
predict future values based on known values. II! II! II! II! II! II!
determine a cause and effect relationship between two or more values. II! II! II! II! II! II! II! II! II! II!
. Regression is used to describe a trend or predict future values based on known values.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
If a patient's risk of cancer is associated with genetics, diet, and tobacco use, which of these is the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
response variable.
II! II!
Risk of cancer II! II!
Genetics
Diet
Tobacco Use II!
The answer is a. In this example, the risk of cancer is the response variable because it "responds" to
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
genetics, diet, and tobacco use.
II! II! II! II! II!
If a patient's blood sugar, cholesterol, and blood pressure are all dependent on diet, which of these is the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
explanatory variable
II! II!
Blood sugar II!
Cholesterol level II!
Blood pressure II!
Diet
d is CorrectII! II!
×
The answer is d. In this example, diet is the explanatory variable because all of the other variables respond
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
to it.
II! II!
Which of the following represents the explanatory variable?
II! II! II! II! II! II! II!
Type of Tobacco Used II! II! II!
Cancer Mortality II!
Average Cigarettes Smoked per Day II! II! II! II!
Oxygen Saturation % II! II!
The answer is c. In this chart, Average Cigarettes Smoked per Day is the explanatory variable.
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
, . From the chart above, Lung Cancer Mortality represents what type of variable?
II! II! II! II! II! II! II! II! II! II! II! II!
Categorical variable II!
Explanatory variable II!
Descriptive variable II!
Response variable II!
d is Correct II! II!
×
The answer is d. From this this chart, Lung Cancer Mortality, represents the response variable.
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
regression equation II!
`The regression equation is the algebraic equation that models the regression. Normally, the regression
II! II! II! II! II! II! II! II! II! II! II! II! II!
can be of any degree with any number of response variables. It need not be linear*. Here, we will use
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
simple linear regression.
II! II! II!
Simple linear regression* II! II!
Simple linear regression* is the prediction of one response variable's value from one or more explanatory
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
variables' values. In a simple linear regression, we model the relationship between the variables as
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
perfectly linear. So, the closer the data's correlation is to being perfectly linear, the more accurate the
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
linear regression model will be.
II! II! II! II! II!
graphing linear regression on a coordinate plane II! II! II! II! II! II!
We are graphing these linear regressions on a coordinate plane. Therefore, simple linear regression is
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
usually represented by an equation in slope-intercept form:
II! II! II! II! II! II! II! II!
Slope-Intercept in Simple Linear Regression II! II! II! II!
y=mx+b
In this equation: II! II!
b is the y -intercept:
II! II! II! II!
In linear regression as with algebra, this is simply the value of y when x=0 .
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
m is the slope: II! II! II!
In regression analysis, for every additional unit of the explanatory variable ( x ), we can predict an increase
II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II! II!
(or decrease, if negative) of m units of the response variable ( y ).
II! II! II! II! II! II! II! II! II! II! II! II! II! II!
x is the x -value:
II! II! II! II!
This is the explanatory variable. II! II! II! II!
