# Chapter 12,13,14 Assignment

Question

Chapter 12 Assignment Name

Description of the Data: The data set contains part of the data for a study of oral condition of cancer patients conducted at the Mid-Michigan Medical Center. The oral conditions of the patients were measured and recorded at the initial stage, at the end of the second week, at the end of the fourth week, and at the end of the sixth week. The variables age, initial weight and initial cancer stage of the patients were recorded. Patients were divided into two groups at random: one group received a placebo and the other group received aloe juice treatment.

Sample size:, n = 25 patients with neck cancer. The treatment is Aloe Juice. (See the Excel File “Chapter 12 Dataset” to complete the questions below.)

Variable Names:

ID- Patient ID

TRT- treatment group: 0 = placebo; 1 = aloe juice

AGE- patient’s age in years

WEIGHTIN- patient’s weight at the initial stage

STAGE- initial cancer stage, coded 1 through 4

TOTALCIN- oral condition at the initial stage

TOTALCW2- oral condition at the end of week 2

TOTALCW4- oral condition at the end of week 4

TOTALCW6- oral condition at the end of week 6

Research Question: Does using aloe juice affect the oral condition of patients with neck cancer after controlling for weight, age, and cancer stage? Is there a change in oral condition from week 1 to week 2 to week 4 to week 6?

1. What type of test will we perform?

2. What are the independent variables, dependent variables, and covariates?

Independent variable:

Dependent variables:

Covariates:

3. List the assumptions for this type of test.

4. Are the dependent variables a continuous measurement?

5. Is the independent variable categorical? What are the categories?

6. Identify the null and alternative hypothesis.

H_0:

H_1:

?

Now, let’s perform our analysis.

Refer to the output below to answer the questions.

Multivariate Testsb

Effect Value F Hypothesis df Error df Sig.

week Pillai’s Trace .018 .097a 3.000 16.000 .960

Wilks’ Lambda .982 .097a 3.000 16.000 .960

Hotelling’s Trace .018 .097a 3.000 16.000 .960

Roy’s Largest Root .018 .097a 3.000 16.000 .960

week * AGE Pillai’s Trace .065 .374a 3.000 16.000 .773

Wilks’ Lambda .935 .374a 3.000 16.000 .773

Hotelling’s Trace .070 .374a 3.000 16.000 .773

Roy’s Largest Root .070 .374a 3.000 16.000 .773

week * WEIGHIN Pillai’s Trace .038 .209a 3.000 16.000 .888

Wilks’ Lambda .962 .209a 3.000 16.000 .888

Hotelling’s Trace .039 .209a 3.000 16.000 .888

Roy’s Largest Root .039 .209a 3.000 16.000 .888

week * STAGE Pillai’s Trace .167 1.066a 3.000 16.000 .391

Wilks’ Lambda .833 1.066a 3.000 16.000 .391

Hotelling’s Trace .200 1.066a 3.000 16.000 .391

Roy’s Largest Root .200 1.066a 3.000 16.000 .391

week * TRT Pillai’s Trace .018 .099a 3.000 16.000 .960

Wilks’ Lambda .982 .099a 3.000 16.000 .960

Hotelling’s Trace .018 .099a 3.000 16.000 .960

Roy’s Largest Root .018 .099a 3.000 16.000 .960

a. Exact statistic

b. Design: Intercept + AGE + WEIGHIN + STAGE + TRT

Within Subjects Design: week

Mauchly’s Test of Sphericityb

Measure:MEASURE_1

Within Subjects Effect Mauchly’s W Approx. Chi-Square df Sig. Epsilona

Greenhouse-Geisser Huynh-Feldt Lower-bound

dimension1 week .672 6.657 5 .248 .789 1.000 .333

Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.

a. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.

b. Design: Intercept + AGE + WEIGHIN + STAGE + TRT

Within Subjects Design: week

Tests of Within-Subjects Effects

Measure:MEASURE_1

Source Type III Sum of Squares df Mean Square F Sig.

week Sphericity Assumed 2.109 3 .703 .134 .939

Greenhouse-Geisser 2.109 2.368 .890 .134 .905

Huynh-Feldt 2.109 3.000 .703 .134 .939

Lower-bound 2.109 1.000 2.109 .134 .718

week * AGE Sphericity Assumed 5.577 3 1.859 .355 .786

Greenhouse-Geisser 5.577 2.368 2.354 .355 .738

Huynh-Feldt 5.577 3.000 1.859 .355 .786

Lower-bound 5.577 1.000 5.577 .355 .559

week * WEIGHIN Sphericity Assumed 5.138 3 1.713 .327 .806

Greenhouse-Geisser 5.138 2.368 2.169 .327 .758

Huynh-Feldt 5.138 3.000 1.713 .327 .806

Lower-bound 5.138 1.000 5.138 .327 .574

week * STAGE Sphericity Assumed 25.527 3 8.509 1.626 .194

Greenhouse-Geisser 25.527 2.368 10.778 1.626 .205

Huynh-Feldt 25.527 3.000 8.509 1.626 .194

Lower-bound 25.527 1.000 25.527 1.626 .218

week * TRT Sphericity Assumed 2.756 3 .919 .176 .912

Greenhouse-Geisser 2.756 2.368 1.164 .176 .873

Huynh-Feldt 2.756 3.000 .919 .176 .912

Lower-bound 2.756 1.000 2.756 .176 .680

Error(week) Sphericity Assumed 282.539 54 5.232

Greenhouse-Geisser 282.539 42.632 6.627

Huynh-Feldt 282.539 54.000 5.232

Lower-bound 282.539 18.000 15.697

7. According to the output, does our dataset satisfy the requirement of sphericity?

8. Identify the appropriate test statistic and p-value for testing the claim that aloe juice is an effective treatment for improving oral condition in neck cancer patients.

Test statistic:

p-value:

9. If appropriate, use post hoc tests to determine if there is a change in oral condition from week 1 to week 2 to week 4 to week 6. If not, explain why. State your conclusion and reasons for your decision.

10. Use APA style reporting to write up your results of this analysis.

Chapter 13 Assignment Name

Description of the Data: The data set contains part of the data for a study of oral condition of cancer patients conducted at the Mid-Michigan Medical Center. The oral conditions of the patients were measured and recorded at the initial stage, at the end of the second week, at the end of the fourth week, and at the end of the sixth week. The variables age, initial weight and initial cancer stage of the patients were recorded. Patients were divided into two groups at random: one group received a placebo and the other group received aloe juice treatment.

Sample size:, n = 25 patients with neck cancer. The treatment is Aloe Juice. (See the Excel File “Chapter 13 Dataset” to complete the questions below.)

Variable Names:

ID- Patient ID

TRT- treatment group: 0 = placebo; 1 = aloe juice

AGE- patient’s age in years

WEIGHTIN- patient’s weight at the initial stage

STAGE- initial cancer stage, coded 1 through 4

TOTALCIN- oral condition at the initial stage

TOTALCW2- oral condition at the end of week 2

TOTALCW4- oral condition at the end of week 4

TOTALCW6- oral condition at the end of week 6

Research Question: Is there a difference in oral condition at the end of week 2 and at the end of week 4? In other words, we want to know if there is a difference between two related samples. (Assume the samples are not normally distributed.)

1. What type of test will we perform?

2. What is the parametric version of this type of test?

3. List some of the pros and cons of using nonparametric tests.

4. List the assumptions for this type of analysis.

5. Are the dependent variables a continuous measurement?

6. Identify the null and alternative hypothesis.

H_0:

H_1:

Now, let’s perform our analysis.

Refer to the output below to answer the questions.

Statistical Output

Descriptive Statistics

N Mean Std. Deviation Minimum Maximum

TOTALCW2 25 8.28 2.54 4.00 16.00

TOTALCW4 25 10.36 3.47 6.00 17.00

Ranks

N Mean Rank Sum of Ranks

TOTALCW2 – TOTALCW4 Negative Ranks 16 10.16 162.50

Positive Ranks 3 9.17 27.50

Ties 6

Total 25

Test Statistics

TOTALCW2 – TOTALCW4

Z -2.72

Asymp. Sig. (2 tailed) .006

7. According to the analysis, what are the medians for each group?

Week 2:

Week 4:

8. Identify the appropriate test statistic and p-value for testing the claim that there is a difference in oral condition at the end of week 2 and week 4.

Test statistic:

p-value:

9. Using the analysis, calculate the effect size. r=z/?N

r =

10. Use APA style reporting to write up your results of this analysis.

Chapter 14 Assignment Name Key

SOURCE: Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X. (2013) Applied Logistic Regression: Third Edition. These data are copyrighted by John Wiley & Sons Inc. and must be acknowledged and used accordingly. Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986.

Description of the Data: The goal of this study was to identify risk factors associated with giving birth to a low birth weight baby (weighing less than 2500 grams). Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy. (See the Excel File “Chapter 14 Dataset” to complete the questions below.)

Variable Names:

ID: Identification Code

LOW: Low Birth Weight (0 = Birth Weight >= 2500g, 1 = Birth Weight < 2500g)

AGE: Age of the Mother in Years

LWT: Weight in Pounds at the Last Menstrual Period

RACE: Race (1 = White, 2 = Black, 3 = Other)

SMOKE: Smoking Status During Pregnancy (1 = Yes, 0 = No)

PTL: History of Premature Labor (0 = None 1 = One, etc.)

HT: History of Hypertension (1 = Yes, 0 = No)

UI: Presence of Uterine Irritability (1 = Yes, 0 = No)

FTV: Number of Physician Visits During the First Trimester (0 = None, 1 = One, 2 = Two, etc.)

BWT: Birth Weight in Grams

Research Question: Is there a relationship between low birth weight (LOW) and smoking status during pregnancy (SMOKE)?

1. What type of test will we perform?

2. Create a contingency table of the data. To do this, you’ll have to count the number of subjects who meet each of the desired criteria (i.e. LOW = 0 and SMOKE = 0, LOW = 0 and SMOKE = 1, etc.) [*Hint: Try using the COUNTIFS function in Excel for easier counting.)

LOW = 0 LOW = 1 Total

SMOKE = 0

SMOKE = 1

Total 189

3. List the assumptions for this type of analysis.

4. Identify the null and alternative hypothesis.

H_0:

H_1:

Now, let’s perform our analysis.

In the Excel file, click on Sheet 1 at the bottom of the screen. Insert your values from question 2 into the table. You’ll notice the chi square test statistic, critical value, p-value, and effect sizes are calculated for you. (You’re welcome .)

5. According to the analysis, what is the expected number of babies with low birth weight born to mothers who smoked during pregnancy? How does this value compare to the observed frequency?

6. Identify the Chi-square test statistic and p-value for testing the claim that there is no association between smoking and low birth weight. Calculate the degrees of freedom and input this value into Excel.

Test statistic:

p-value:

Degrees of freedom:

7. Identify the effect size. What type of association is indicated by the effect size?

Calculate the odds ratio of a mother who smokes during pregnancy giving birth to a low birth weight baby compared to a mother who does not smoke. Input this value into the Excel file to see the confidence interval of the ratio.

8. What is your calculated odds ratio?

Odds Ratio:

9. How would you interpret the odds ratio?

10. Use APA style reporting to write up your results of this analysis.

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