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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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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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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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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
excelhomeworkhelp.com
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
excelhomeworkhelp.com
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
excelhomeworkhelp.com
-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).
excelhomeworkhelp.com
f.
excelhomeworkhelp.com
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
excelhomeworkhelp.com
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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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
excelhomeworkhelp.com
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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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
excelhomeworkhelp.com
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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Multiple Linear Regression Homework Help

  • 1. For any Homework related queries, Call us at:- +1 678 648 4277 You can mail us at:- info@excelhomeworkhelp.com or reach us at:- www.excelhomeworkhelp.com
  • 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. excelhomeworkhelp.com
  • 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%) excelhomeworkhelp.com
  • 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). excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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). excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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. excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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 excelhomeworkhelp.com
  • 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. excelhomeworkhelp.com