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Biostatistics Answer the following questions. Copy and paste any required data charts or summaries into this Word document. Include the file naming convention. I. Descriptive Statistics: Download the...

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Biostatistics

Answer the following questions. Copy and paste any required data charts or summaries into this Word document.

Include the file naming convention.

I. Descriptive Statistics:

Download the data set Final_1.sav. Complete the following:

1) List the level of measurement for the variables, AGE, SEX, AGEGRP, SBP1 in the data set and describe the appropriate numerical and descriptive statistics based on these.

Record Number

AGE

1

3

2

11

3

15

4

46

5

14

6

35

7

46

8

35

9

40

10

29

11

22

12

16

2) Calculate (by hand) the mean and standard deviation for the first 12 records for age in the data set.

3) Generate numerical and graphical descriptive statistics for each of the variables, namely, AGE, SEX, AGEGRP and SBP1.

4) Interpret the output you generated in part 2 for each of the variables in the data set.

I. Paired and Independent ttests:

Download the data set Final_2.sav and use SPSS to complete the following calculations:

1) Use the 5-step approach to hypothesis testing and the calculation of the 95% confidence intervals to answer the following research question: Was a significant difference in Systolic Blood Pressure (SBP) observed over the course of the study?

2) Use the 5-step approach to hypothesis testing and the calculation of the 95% confidence intervals to answer the following research question: Is there a difference in SBP1 based on HIV status? (Hint: Assign Y as group 1 and N as group 2)

II. Cross-Tabulation:

III. Download the data set Final_3.savand use SPSS to complete the following calculations.

1) Use the 5-step approach to hypothesis testing to answer the following research question:

2) In the sample provided in Final_3.sav, are the variables income and Bladder Cancer independent of each other? (Note: The question could also be asked: Is there an association between the variables because the lack of independence implies an association)?

2) Answer the following based on the cross-tabulation of alcohol consumption and Bladder Cancer:

Alcohol consumption * Bladder Cancer Crosstabulation

Count

Bladder Cancer

Total

No

Yes

Alcohol consumption

"Less than 1 drink per week"

30

54

84

4 or more drinks per month

22

115

137

Total

52

169

221

  • Calculate the odds ratio.
  • Describe how the odds ratio differs from the relative risk or risk ratio and why you would chose it here.
  • Interpret the odds ratio and how it might impact the practice of public health practitioners.
  • If you wanted to know whether this relationship was statistically significant what test(s) could you use?

IV. ANOVA:

Download the data set Final_4.sav and use SPSS to complete the following calculations.

1) Produce box plots of income for each region of the US in the data set and interpret them. Based on the box plots do you expect to find a difference between any of the groups?

2) Create descriptive statistics for each region, using the variable income.

Include skewness and kurtosis in the output.

Create a histogram for each group.

3) Run the ANOVA for income based on region. Include the ANOVA table and the test for

Homogeneity of Variance. Interpret the results.

5) Conduct post hoc analysis using Bonferroni and LSD methods to control for multiple testing.

  • Provide the output.
  • Interpret your results.
  • Why do you need to use methods like Bonferroni and LSD with the ANOVA?

V. Regression:

VI. Download the data set Final_5.sav and use SPSS to complete the following calculations.

1) Use an independent t test and simple linear regression to identify whether a relationship exists between gender and BMI.

  • Run the appropriate t test in SPSS, report the significance of the difference in means and the confidence interval, and interpret the results.
  • Run the simple linear regression in SPSS, report the significance of the variable gender and the overall fit of the model (using r2). Interpret the results.
  • How are these two approaches different?
  • Are your conclusions the same using both tests?

2) Answer the questions using the provided output:

Multiple Linear Regression

Researchers looked at the Emergency Department Records of 60 adults ages 22 to 46 years who arrived in the ED complaining of chest pain during a 6 month period of time. They did not use a random sample as they wanted 30 males and 30 females in the study. They collected information on BMI (a measure of overweight/obesity), Age, SBP (Systolic Blood Pressure) and the diagnosis of Diabetes. Their first hypothesis (alternative) was that the dependent variable SBP is associated with BMI, Age, Diabetes, and Gender. They conducted a multiple linear regression to test their hypothesis. Here are the results (note that they had two models and chose to use the second one):

Model Summaryc

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.796a

.634

.608

5.443

2

.792b

.627

.607

5.445

a. Predictors: (Constant), Diabetes, Age, Gender, BMI

b. Predictors: (Constant), Age, Gender, BMI

c. Dependent Variable: SBP

ANOVAc

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

XXXXXXXXXX

4

706.242

23.839

.000a

Residual

XXXXXXXXXX

55

29.626

Total

XXXXXXXXXX

59

2

Regression

XXXXXXXXXX

3

931.407

31.418

.000b

Residual

XXXXXXXXXX

56

29.646

Total

XXXXXXXXXX

59

a. Predictors: (Constant), Diabetes, Age, Gender, BMI

b. Predictors: (Constant), Age, Gender, BMI

c. Dependent Variable: SBP

Coefficientsa

Model

Standardized Coefficients

t

Sig.

95.0% Confidence Interval for B

Beta

Lower Bound

Upper Bound

1

(Constant)

8.092

.000

57.471

95.309

Gender

-.189

-2.100

.040

-6.381

-.149

BMI

.557

6.130

.000

1.213

2.392

Age

.507

6.067

.000

.426

.847

Diabetes

-.089

-1.019

.313

-4.752

1.549

2

(Constant)

8.885

.000

55.243

87.407

Gender

-.173

-1.950

.056

-6.054

.081

BMI

.574

6.413

.000

1.276

2.436

Age

.517

6.243

.000

.441

.859

a. Dependent Variable: SBP

1) Which variables in model 1 are significant?

2) Which variables in model 2 are significant?

3) Why did they choose model 2?

4) What is the “fit” of model 2 (the one they chose to use)?

5) Is this a good model, why or why not?

  • Multiple Logistic Regression

The Emergency Department Researchers selected another 60 adults and again looked at Age, SBP, BMI, Gender, and Diabetes. This time however, they also collected information on whether the chest pain was diagnosed as an MI (aka Heart Attack) or something else. Now their alternative hypothesis was that gender was related to the diagnosis of an MI, after controlling for Age, SBP, BMI, and Diabetes. They used multiple logistic regression to test their hypothesis and these are their results (note that there are multiple models and they chose to use the final one):

Model Fitting Information

Model

Model Fitting Criteria

Likelihood Ratio Tests

-2 Log Likelihood

Chi-Square

df

Sig.

Intercept Only

74.995

Final

16.398

58.598

5

.000

Pseudo R-Square

Cox and Snell

.623

Nagelkerke

.866

McFadden

.767

Parameter Estimates

Heart Attacka

B

Std. Error

Wald

df

Sig.

Exp(B)

No

Intercept

115.037

43.679

6.936

1

.008

BMI

-1.400

.572

5.995

1

.014

.247

Age

.037

.116

.099

1

.753

1.037

Diabetes

.811

1.471

.304

1

.581

2.251

SBP

-.469

.213

4.849

1

.028

.626

[Gender=1]

-11.866

4.695

6.389

1

.011

7.025E-6

[Gender=2]

0b

.

.

0

.

.

Parameter Estimates

Heart Attacka

95% Confidence Interval for Exp(B)

Lower Bound

Upper Bound

No

Intercept

BMI

.080

.756

Age

.826

1.303

Diabetes

.126

40.193

SBP

.412

.950

[Gender=1]

7.088E-10

.070

[Gender=2]

.

.

1) Is the final model significant?

2) What are the odds ratios for each of the significant variables, and what do they mean? 3) Will this model help the researchers, why or why not?

Answered Same Day Dec 20, 2021

Solution

Robert answered on Dec 20 2021
114 Votes
Biostatistics
Answer the following questions. Copy and paste any required data charts or summaries into this Word
document.
Include the file naming convention.
I. Descriptive Statistics:
Download the data set Final_1.sav. Complete the following:
1) List the level of measurement for the variables, AGE, SEX, AGEGRP, SBP1 in the data set and describe
the appropriate numerical and descriptive statistics based on these.
Age: This variable is numerical in nature and its level of measurement is ratio. This is because the data
classifications are ordered according to the amount of the characteristics they posses
Sex: This variable is nominal in nature since Males (M) and females (F) are just names of categories.
There is no intrinsic ordering between them
AgeGrp: This is an ordinal variable as it assign numbers to rank-ordered categories ranging from low to
high. In this data set, age group is ranked as 1 to 5
SBP1: This is also a numerical variable and is ordinal in nature. This is a continuous variable as it
epresents blood pressure in continuous series
2) Calculate (by hand) the mean and standard deviation for the first 12 records for age in the data set.
Record
Number
AGE AGE - MEAN (AGE - MEAN)^2
1 3 -23 529
2 11 -15 225
3 15 -11 121
4 46 20 400
5 14 -12 144
6 35 9 81
7 46 20 400
8 35 9 81
9 40 14 196
10 29 3 9
11 22 -4 16
12 16 -10 100
SUM 2302

Mean =

3) Generate numerical and graphical descriptive statistics for each of the variables, namely, AGE, SEX,
AGEGRP and SBP1.
NUMERICAL AND GRAHICAL REPRESENTATION
AGE
Valid 78
Missing 0
27.93
27.56
14.487
63
1
64
Range
Minimum
Maximum
age
N
Mean
Median
Std. Deviation
Mean =



Mean =


26
Standard deviation = √



Standard deviation = √


14.467
SEX
AGEGRP
N Valid 78
Missing 0
Frequency Percent
Valid
Percent
Cumulativ
e Percent
F 38 48.7 48.7 48.7
M 40 51.3 51.3 100
Total 78 100 100
Valid
Statistics
sex
Valid 78
Missing 0
2.81
3
2
a
5
1
6
Range
Minimum
Maximum
Statistics
agegrp
N
Mean
Median
Mode
SBP1
4) interpret the output you generated in part 2 for each of the variables in the data set.
From the results generated in part 2, we concluded that the average age of the individuals is 26 years
with standard deviation of about 14.47. From the value of standard deviation, we can say that there is
lot of variation in the age of the individuals in the given dataset.
I. Paired and Independent t tests:
Download the data set Final_2.sav and use SPSS to complete the following calculations:
1) Use the 5-step approach to hypothesis testing and the calculation of the 95% confidence intervals to
answer the following research question: Was a significant difference in Systolic Blood Pressure (SBP)
observed over the course of the study?
One-Sample Statistics
N Mean Std. Deviation Std. E
or Mean
sbp1 72 110.50 16.014 1.887
One-Sample Test
sbp1
N Valid 78
Missing 0
Mean 108.94
Median 111.29
Std.
Deviation
16.308
Range 82
61
143
Minimum
Maximum
Test Value = 0
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the
Difference
Lower Upper
sbp1 58.551 71 .000 110.499 106.74 114.26
2) Use the 5-step approach to hypothesis testing and the calculation of the 95% confidence intervals to
answer the following research question: Is there a difference in SBP1 based on HIV status? (Hint: Assign
Y as group 1 and N as group 2)
II. Cross-Tabulation:
III. Download the data set Final_3.sav and use SPSS to complete the following calculations.
1) Use the 5-step approach to hypothesis testing to answer the following research question:
2) In the sample provided in Final_3.sav, are the variables income and Bladder Cancer independent of
each other? (Note: The question could also be asked: Is there an association between the variables
ecause the lack of independence implies an association)?
2) Answer the following based on the cross-tabulation of alcohol consumption and Bladder Cancer:
Alcohol consumption * Bladder Cancer Crosstabulation
Count
Bladder Cancer
Total No Yes
Alcohol consumption "Less than 1 drink per
week"
30 54 84
4 or more drinks per
month
22 115 137
Total 52 169 221
Calculate the odds ratio.
Odds Ratio (of having bladder cancer by taking less than1 per drink per week or vs. 4 or more per
month) = (54/30)/ (115/22) = 0.34
Describe how the odds ratio differs from the relative risk or risk ratio and why you would chose it here.
Relative risk (of bladder cancer by having less than1 per drink per week or vs. 4 or more per month) =
(54/84)/ (115/137) = 0.77
Therefore, we choose relative risk because it is much easier to interpret and makes much more sense to
the layman. In this case, a relative risk of 0.77 means that the affected group has lesser risk of a non-
affected group (no bladder cancer).
Interpret the odds ratio and how it might impact the practice of public health practitioners.
An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents
the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome
occu
ing in the absence of that exposure. In this case, the odds ratio means that the public health
practitioners believe that people drinking alcohol one drink per week are likely to get less affected by
the bladder cancer
If you wanted to know whether this relationship was statistically significant what test(s) could you use?
In this situation, we will conduct a chi-square test. This is because for such contingency tables,
hypothesis testing is done using the Chi-square statistic in order to decide whether or not effects are
present
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Pearson...
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