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2. Question
Evaluate if there is a relationship (predict) between the personal characteristics and
the screening tools with weight loss. Prepare a short description of what was done
and what you found. IV=independent variable, DV= dependent variable
Conduct a multiple linear regression to predict satisfaction using all of the personal
characteristics and perceptions variables (if appropriate).
Follow the guide in Module 9 of how to conduct this analysis and include in your
description what you did such as the following:
a) Define the hypothesis
b) Describe each variable using appropriate descriptive statistics; no need to
recode anything but make sure dummy coding is correct; create a ‘table1-
remember analysis exercise 1’for this step
c) Run bivariate associations (why? need IV by each IV to check for _________)
d) Run the full model (DV and multiple IVs ) –show evidence that you checked
assumptions, etc (for this exercise it is ok to enter the selected IVs all at once
in one ‘block’)
e) Summarize the above (a-d) and the results in your OWN words
f) Include IS raw output view or the Excel output.
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3. Solution
a) Hypothesis
Null hypothesis: there is no relationship between the dependent variable (Islost) and the
independent variable (sex, age, diet, exercise, confid, sedentary)
Alternative hypothesis: there is at least a relationship between the dependent (Islost) and
the independent variable (sex, age, diet, exercise, confid, sedentary)
a) Descriptive statistics
n mean Median Standard deviation
Age 51 26.94 23 8.09
Exercise 51 39.59 39 5.49
Confid 51 17.78 17 3.37
Sedentary 51 114.39 114 4.40
Ibslost 51 24.43 24 5.07
Sex 51 Female (57%) Male (43%)
diet 51 Yes (63%) No (37%)
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4. The table above shows the descriptive statistics of the weight dataset. 57% of the total
participants are female while 43% of the participant are male. 63% of the participant have
diet adherence while 37% do not have diet adherence. The average age of participants was
26.96 years (SD = 8.09). The mean and standard deviation of minutes exercising per day is
(39.59, 5.49), confidence in success (M = 17.78, SD = 3.37) respectively, minutes in active
per day (M = 114.39, 4.40), pounds lost since start of the program (24.43, 5.07).
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5. c. The bivariate association graph above shows the bivariate relationship between the
dependent variable and the independent variables. The dependent variable is weight loss
while the independents variables are age, exercise, sedentary, and confid.
d. The following are the assumption of multiple linear regression which is illustrate from
the graphs below;
•There exists a linear relationship between the dependent and independent variables
•The independent variables are not highly correlated with each other
•The variance of the residuals is constant
•Independence of observation
•Multivariate normality i.e. it follows a normal distribution
0
10
20
30
40
0 20 40 60 80 100 120
lbslost
Sample Percentile
Normal Probability Plot
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6. 0
10
20
30
40
0 20 40 60
lbslost
age
age Line Fit Plot
lbslost
Predicted lbslost
0
10
20
30
40
0 10 20 30
lbslost
confid
confid Line Fit Plot
lbslost
Predicted lbslost
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7. 0
10
20
30
40
0 20 40 60
lbslost
exercise
exercise Line Fit Plot
lbslost
Predicted lbslost
0
10
20
30
40
105 110 115 120 125
lbslost
sedentary
sedentary Line Fit Plot
lbslost
Predicted lbslost
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8. -20
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Line chart of Weighloss data
sex age diet exercise confid sedentary lbslost
e. Multiple regression (OLS) was used to estimate the ability sex, age, diet, exercise,
confidence in success, minutes inactive per day, in predicting weight loss. Forty-fivepercent
of the variance surrounding weight loss was explained by sex, age, diet, exercise, confidence
in success, minutes inactive per day weight (R2 = 0.4567). Overall, the model was statistically
significant weight loss (F = 6.1667, p = 0.000). Sex, Age, Exercise, Confidence in success, and
Minutes inactive per day was not statistically significant in the model (p > 0.05); whereas diet
was statistically significant (t = 2.096, p = 0.04). For every one cm increase in head
circumference, motor coordination scores increased by 0.65 points (beta = 0.65). Males were
also found to score higher than females. Males scores were .35 points higher (beta=.35,
p=.04).
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9. f.
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10. Part B. Multiple logistic regression
Question
Task: Now we would like to see if we can find a relationship (predict) between weight loss
and some of the personal characteristics and the chance of recommending the clinic to
others.Prepare a short description including the following information:
1. Is running a multiple logistic regression appropriate for this task? Explain why it is or is
not appropriate.
2. Define the hypotheses
3. How many and what percent of patients indicated they would recommend the clinic?
4. You do not need to run logistic regression in EXEL or IS. Use the output below to write a
summary of the relationship.
DV: recommend clinic to others (1=yes vs 0=no)
B S.E Sig OR 95% C I for OR
Lower Upper
Lbslost
Sex (female vs male)
Age
248
1.393
.011
.095
.694
.044
.009
.045
.801
1.282
4.028
1.011
1.064
1.034
.028
1.544
15.683
1.102
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11. Diet
Constant
.113
-7.228
.757
2.780
.882
.009
1.119
.001
.254 4.935
Solution
1. Is running a multiple logistic regression appropriate for this task? Explain why it is
or is not appropriate.
Answer: Yes, this is because the outcome or target variable is binary (yes or no) and
since the number of observations is greater than the number of features in the
datasets, there is no room for overfitting in the model.
2. Define the hypotheses
Ans: : There is a relationship between weight loss and some of the personal
characteristics and the chance of recommending the clinic to others.
i.e.
H1: There is a relationship between weight loss and some of the personal
characteristics and the chance of recommending the clinic to others.
i.e.
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12. 3. How many and what percent of patients indicated they would recommend the
clinic?
Ans: 25 (Twenty-five) patients and 49 % of patients indicated that they would
recommend the clinic.
4. Logistic multiple regression was used to estimate the ability of Age, Sex, Lbslost and
Diet in predicting if the patients will recommend the clinic (yes) or not (No). Age and
Diet were not statistically significant in the model (p > 0.05). A significant association
was found between variables: Lbslost, Sex and Patients recommending the Clinic and
there’s no significant relationship between Age, Diet and Patients recommending the
Clinic. An increase in sex of the patients will increase the odds of recommending the
clinics by four fold (Odds ratio= 4.03, 95% confidence interval= 1.034, 15.68, p<.001),
an increase in Lbslost of the patients will increase the odds of recommending the
Clinic by almost two fold (Odds ratio= 1.28, 95% confidence interval= 1.064, 1.544,
p<.001) and an increase in Age (Odds ratio= 1.011, 95% confidence interval= 0.928,
1.102, p<.001) and Diet (Odds ratio= 1.119, 95% confidence interval= 0.254, 4.935,
p<.001) of the patients will increase the odds of recommending the Clinic by one fold
respectively.
Part C. Sensitivity & Specificity
Question
Recall that our survey used a self-report measure of diet adherence. We want to assess if
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13. the results are valid and accurate by comparing the self-report with a gold standard
(stool sample detecting microbiome and should see only small amounts of fats and
sugars, etc). We identify 15 true positives out of the 32 clients who self-identified as
being diet adherent and 18 true negatives.
1. Fill in the following table
2. Calculate the sensitivity of the self-report measure.
3. Calculate the specificity of the self- report measure.
4. What does this mean—was our self-report of diet adherence a good measure?
What does having a good or poor measure mean when exploring relationships, how
do you think about it when applying these kinds of evidence based findings?
Gold standard
positive
Gold standard
negative
Total
Self-report +adherence
Self-reportnonadherence
Total
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14. Solution
1. Fill in the following table
2. Calculate the sensitivity of the self-report measure.
Sensitivity = 15/32 = 0.46875
3. Calculate the specificity of the self- report measure.
Specificity = 18/32 = 0.5625
4. What does this mean—was our self-report of diet adherence a good measure? What
does having a good or poor measure mean when exploring relationships, how do you
think about it when applying these kinds of evidence based findings?
Since both sensitivity and specificity have average values, it does not indicate a good
measure.
Gold standard
positive
Gold standard
negative
Total
Self-report +adherence 15 17 32
Self-reportnonadherence 14 18 32
Total 29 35 64
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15. Part D. Run Chart
1. Did the proportion of women administered RhoGam vaccination change—what was
the mean before and after the program change?
From the data provided, I notice that the proportion of women administered RhoGam
vaccination change, the mean before program change is 51.89 while the mean after
program change is 51.33
2. Did all the changes that were made lead to improvements?
The changes that were lead does not lead to much improvement based on the data
analyzed using the run chart.
3. What data would you want to start collecting to determine other steps for quality
improvement in these patients?
In other to determine other steps for quality improvement in the patients, I will suggest
that data can be collected on the average glucose intake, percentage of
time in hypoglycemic ranges, and percentage of time in hyperglycemic range.
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