Term Paper on "SPSS Data Analysis American Heart Association Prediction"
Term Paper 9 pages (2342 words) Sources: 0
[EXCERPT] . . . .
SPSS Data AnalysisAmerican Heart Association Prediction of Stroke Risks
Over a ten-year study, the American Heart Association collected data on age, blood pressure level, and smoking information in order to calculate the risk of strokes within the sample population. Within the context of this study, risk is interpreted by the probability (times 100) that the patient will have a stroke over the next ten-year period. With those who smoke, there is a dummy variable assigned to correlate the data. In this case a 1 indicates a smoker, and 0 indicates a nonsmoker.
Data Set
Risk
Age
Blood Pressure
Smoker
Using the data, develop an estimated regression equation that relates the risk of a stroke to the person's age, blood pressure, and whether the person is a smoker
With the three separate independent variables representing the individual's age, blood pressure, and whether or not the smoke, the regression equation must reflect a multi-linear regression analysis. Here, the dependent variable equates to the numeric value of the risk level for each individual depending on their relation of their age, blood pressure, and smoking habits. With the regression analysis done using the data set above, the constant value equates to -93.401; each independent variable also has its own coefficient which must be used within the final regression equation. Thus, the equation goes as follows:
Y = a + b1*X1 + b2*X2 + b3*X3
And equates to the following with the constant and independent coefficients p
download full paper ⤓
Y = -93.401 + 0.98869x1 + 0.2994x2 + 6.5766x3
B. Use the regression analysis tool to obtain a complete diagnostics.
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
smoker paitient, blood pressure, paitient age (years)a
Enter
a. All requested variables entered.
b. Dependent Variable: Risk of stroks (%)
Model Summaryb
Model
R
R Square
Adjusted R. Square
Std. Error of the Estimate
1
.935a
.873
.850
5.75657
a. Predictors: (Constant), smoker paitient, blood pressure, paitient age (years)
b. Dependent Variable: Risk of stroks (%)
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
3
36.823
.000a
Residual
16
33.138
Total
19
a. Predictors: (Constant), smoker paitient, blood pressure, paitient age (years)
b. Dependent Variable: Risk of stroks (%)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-91.759
15.223
-6.028
.000
paitient age (years)
1.077
.166
.697
6.488
.000
blood pressure
.252
.045
.553
5.568
.000
smoker paitient
8.740
3.001
.302
2.912
.010
a. Dependent Variable: Risk of stroks (%)
Casewise Diagnosticsa
Case Number
Std. Residual
Risk of stroks (%)
Predicted Value
a. Dependent Variable: Risk of stroks (%)
Residuals Statisticsa
Minimum
Maximum
Mean
Std. Deviation
N
Predicted Value
4.4606
54.1511
26.9500
13.88058
20
Std. Predicted Value
-1.620
1.960
.000
1.000
20
Standard Error of Predicted Value
1.903
3.532
2.538
.445
20
Adjusted Predicted Value
4.8474
54.2600
26.8973
13.98313
20
Residual
-13.10645
8.55608
.00000
5.28260
20
Std. Residual
-2.277
1.486
.000
.918
20
Stud. Residual -2.418
1.678
.004
1.016
20
Deleted Residual
-14.78714
10.90265
.05268
6.48651
20
Stud. Deleted Residual
-2.940
1.790
-.025
1.107
20
Mahal. Distance 1.127
6.203
2.850
1.340
20
Cook's Distance
.000
.193
.057
.070
20
Centered Leverage Value
.059
.326
.150
.071
20
a. Dependent Variable: Risk of stroks (%)
Curve Fit
Case Processing Summary
N
Total Cases
20
Excluded Casesa
0
Forecasted Cases
0
Newly Created Cases
0
a. Cases with a missing value in any variable are excluded from the analysis.
Variable Processing Summary
Variables
Dependent
Independent
paitient age (years)
blood pressure smoker paitient
Risk of stroks (%)
Number of Positive Values
20
20
10
20
Number of Zeros
0
0
10
0
Number of Negative Values
0
0
0
0
Number of Missing Values
User-Missing
0
0
0
0
System-Missing
0
0
0
0
Model Description
Model Name
MOD_1
Dependent Variable
1
paitient age (years)
2
blood pressure
3
smoker paitient
Equation
1
Linear
Independent Variable
Risk of stroks (%)
Constant
Included
Variable Whose Values Label Observations in Plots
Unspecified
Model Summary and Parameter Estimates
Dependent Variable:paitient age (years)
Equation
Model Summary
Parameter Estimates
R Square
F
df1
df2
Sig.
Constant
b1
Linear
.423
13.186
1
18
.002
58.104
.421
The independent variable is Risk of stroks (%) .
C. Is smoking a significant factor in the risk of a stroke? Explain. Use a=0.05
With the regression analysis previously conducted, the factor of whether or not smoking proves to be a significant factor within the risk of a stroke can be sufficiently examined. In order to conduct this regression analysis, the following equation was used in the examination of only the smoking variable in comparison to the dependent numeric value of predicted risk of stroke.
As the graph and equation shows, there is a significant impact on risk factor if the individual smokes. Although the other independent variables, including age and blood pressure, also play a factor, smoking seems to show a significant increase in the predicted risk of a stroke within the individuals included in the data set. Thus, it can be sufficiently assumed that smoking itself is a significant signal in an increased risk factor for predicted strokes.
D. What is the probability of a stroke over the next ten years for Thompson, a 68-year-old smoker who has a blood pressure of 175?
Coefficients from SPSS Regression Analysis age.697
smoking.302
With the equation formulated earlier that computes the overall numeric risk value being Y = a + b1*X1 + b2*X2 + b3*X3, we can now begin to plug in both the computed constant and coefficients along with new independent variables of an individual not included in the original data set. The equation with the constant and coefficients included, the final equation to be used with new variable sets is Y = -93.401 + .697x1 + 0.553x2 + .302x3. Here, we must first define the variables used in the regression analysis. Variable 0 represented the age of each individual within the data set, Variable 2 represented blood pressure, and variable 3 represented smoking habits. Variable 1 is equated to the dependent variable, or numeric risk value, and so is represented as Y. Thus, with a 68-year-old man who smokes and has a blood pressure of 175, shows an equation to:
Y=-93.401 + .697 (age) + .553 (blood pressure) + .302 (smoking)
Y = -93.401 + .697 (68) + .553(175)+ .302(1)
Y=-93.401 + 47.396 + 96.775+ .302
Y=51.072
Here then, the risk level is at 51.072, and can then be rounded down to 51. The individual in question here then has a risk factor of 51 in terms of his risk for having a stroke within the next ten years, meaning that the base probability is estimated at .51072. It is clear that the man's blood pressure and smoking habits are the two independent variable factors that play the most significant role in formulating such a high risk of stroke within the next ten years in comparison to the other individuals within the original data set.
Question 2
A. Fuel Additives and Mileage
Data Table
Sample a
Sample B
17.3
18.7
18.4
17.8
19.1
21.3
16.7
21
18.2
22.1
18.5
18.7
17.5
19.8
20.7
20.2
Data Rank
Rank a
Rank B
2
8.5
6
4
10
15
1
14
5
16
7
8.5
5
11
13
12
Data Set
Sum
Mean
17.9571429
5.14285714
Variance
0.6795238
2.2641071
Rank Sum
Rank Mean
4.9
11.3
Combined Sum
Combined Median Rank
8.1
Testing commenced on two separate fuel additives in order to test their differing effect on the mileage of the cars. One sample included seven cars, and the other nine cars. Their mileage per gallon can then be used to determine if there is a significant difference between the two additives in terms of mileage information. The two sets of data represent comparable observations and measurable central tendencies. Both include the mileage of vehicles which were used as sampling for the fuel additives in question. Additionally, each sample test is independent of the other and the observations in each sample itself are also independent of each other. Thus, the Mann-Whitney statistical test proves a viable option to compare the two sets of data from the two different and independent fuel additives. The Mann-Whitney test allows for the observation of one sample population in regards to how it fairs in comparison to another sample population, where the variances are equal amongst both sample groups.
Thus, the following equation can be implemented within the computation of the Mann-Whitney statistical test.
UA= nanb +na (na+1) -- TA
2
na= 7 (critical values for U)
nb= 9
TA= the sum of the ranks of Sample a
nanb +na (na+1) = the maximum value of TA
2
With these values, the following computations were made, including the value of U, P (1), and P (2), which can then be analyzed to show if there is a significant difference between the two additives and how they affect the mileage rate of the vehicles they are used in.
Ranks
fuel additives (per m)
N
Mean Rank
Sum of Ranks… READ MORE
Quoted Instructions for "SPSS Data Analysis American Heart Association Prediction" Assignment:
We will pay $240.00 for this order!!
All specifications will be provided via email.
How to Reference "SPSS Data Analysis American Heart Association Prediction" Term Paper in a Bibliography
“SPSS Data Analysis American Heart Association Prediction.” A1-TermPaper.com, 2010, https://www.a1-termpaper.com/topics/essay/spss-data-analysis-american-heart/81604. Accessed 5 Oct 2024.
Related Term Papers:
American Psychological Association/Adult Development and Aging Research Proposal
American Psychological Association/Adult Development and Aging
The American Psychological Association as we know it today is structured as a collection of special interest divisions. When it was formed in 1945… read more
Research Proposal 1 pages (375 words) Sources: 3 Style: APA Topic: Aging / Death / Gerontology
SPSS Statistics Data Analysis Essay
SPSS Statistics: Data Analysis
Many factors are associated with satisfaction with one's job. Look at the variable satjob. (Respondents were asked, "On the whole, how satisfied are you with the… read more
Essay 2 pages (662 words) Sources: 1 Topic: Education / Teaching / Learning
SPSS Statistics Data Analysis Term Paper
SPSS Statistics: Data Analysis
Education
The average number (mean) of years that participants attended school was 13.0, with data ranging from 0 years to 20 years. The median was 12… read more
Term Paper 2 pages (494 words) Sources: 1 Topic: Family / Dating / Marriage
Non-Parametric Testing Data Analysis Chapter
SPSS Statistics: Non-Parametric Data Analysis
Non-Parametric Data Analysis
Examine the relationship between education (degree) and perception of life (life). Can you reject the null hypothesis that education and perception of… read more
Data Analysis Chapter 2 pages (621 words) Sources: 1 Topic: Women / Feminism
American Foreign Policy Since Its Inception Term Paper
American Foreign Policy
In his farewell address, given to Congress on September 17, 1796, the father of the country, George Washington warned his fellow Americans against "the insidious wiles of… read more
Term Paper 5 pages (1508 words) Sources: 3 Topic: American History / United States
Sat, Oct 5, 2024
If you don't see the paper you need, we will write it for you!
We can write a new, 100% unique paper!