Scenario: To succeed in any industry that deals with massive amount of data, you must possess analytic skills. As you interview for an entry level position after receiving your undergraduate/graduate...

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Scenario:





To succeed in any industry that deals with massive amount of data, you must possess analytic skills. As you interview for an entry level position after receiving your undergraduate/graduate degree, let us say, you are given a series of four different analyses to

conduct

and

interpret
.






Objective:



Complete the tasks and corresponding questions given below.
All of the datasets are on 4 different sheets (named as PROBLEM1, PROBLEM2, PROBLEM3 & PROBLEM4, respectively) within ONE Excel worksheet,
titled

Project3.xlsx
.



The data will need to be imported into SAS prior to analysis. Some data have not been given to you in a format conducive to analysis and may need to be modified in order to conduct the appropriate such analysis.




Submission Procedure:



Please submit the following:



1. SAS program or R Codes


2. Word document with your answers










Problem 1:



· The dataset contains 2 variables
GROUP
(two levels:
BP_US
and
BP_FOREIGN) and
BLOODPRESSURE, denoting blood pressures of individuals born within (BP_US) and outside (BP_FOREIGN) of the United States.



· You wish to determine whether or not there is a significant difference in mean blood pressures between the two groups.



· Conduct an independent samples t-test to make this determination.
The data file for problem 1 has 10,000 observations (5000 observations for each level of GROUP).







Problem 2
:



· A study is conducted on mice with a glioblastoma (brain tumor) to determine if a certain drug improves survival.



· The dataset has two variables:
TREATMENT
and
DAYS_TO_DEATH.



TREATMENT
has 3 groups: CONTROL, TREAT_A (receives 100 mg of drug), and
TREAT_B (receives 200mg of the drug).



· Use a one-way ANOVA to determine if there is a significant difference in survival among the three groups.



· If significant differences exist, use the Bonferroni test to determine specifically which pair of treatment groups differs.
The data file for problem 2 has 300 observations (each group has 100 observations).









Problem 3:





· Assuming a causal relationship, can CALORIES intake be used to predict systolic blood pressure (SYSTOLIC_BP)?
The data file for problem 3 has 200 observations.




· Use SAS to perform a simple linear regression analysis and obtain the regression line. Based on the PROC REG output,




Problem 4:



· There are two variables in the final dataset. The variable,
BTHDEFECT
equals 1 if a child was born with a birth defect and 0 if not.



· Likewise, the variable,
PRETERM,
equals 1 if a child was born premature and 0 if not.



· Perform a chi-square analysis to determine if there is an association between
preterm birth
and
having a birth defect.
The data file for problem 4 has 10,000 observations.



Answer the following questions and submit the Word document. Please type in your answer into this Word document.






PROBLEM 1:






1.

Is there a statistically significant difference in the mean blood pressure between the two groups? Give reason for your answer.












PROBLEM 2:









1.

Does the variable, DAYS_TO_DEATH, have normal distribution for each level of the TREATMENT? Give reasons for your answer.









2.

Is there a statistically significant differences in the mean
days_to_death
among the three groups? Give reason for your answer.









3.

Which pair of the treatment groups differ statistically significantly? Use Bonferroni test.









PROBLEM 3:







1.

Obtain the linear regression equation.






2.

What percent of the variability observed in the systolic blood pressure scores can be accounted for by a linear relationship between systolic blood pressure and calorie intake?






3.

Fill in the blank. As predicted by the model, for a 100-unit increase in calorie consumption, the mean systolic blood pressure _______________________.



1.

(
HINT: think about the change in mean systolic blood pressure for a 1-unit increase and multiply by 100)







  1. Would you reject or fail to reject the null hypothesis that calorie intake is associated with systolic blood pressure? Why or why not?









PROBLEM 4:





1. What is the interpretation of the results based on the SAS output?







Answered Same DayApr 30, 2021

Answer To: Scenario: To succeed in any industry that deals with massive amount of data, you must possess...

Suraj answered on May 01 2021
136 Votes
Solution No.1
Apply Independent t-test
: There is no significance difference in mean blood pressures between the two groups.
: : There is a signif
icance difference in mean blood pressures between the two groups.
Now we solve this problem using R Software
Following is the R code for independent t-test
Data = read.csv("C:/Users/CNC/Desktop/PROBLEM1.csv",)
names(Data)
bp_us<-Data[0:5000,2:2]
bp_foreign<-Data[5001:10000,2:2]
test<-t.test(bp_us,bp_foreign,paired = FALSE)
print(test)
The following is the output from this test is
OUTPUT
    Welch Two Sample t-test
data: bp_us and bp_foreign
t = -32.752, df = 9996.7, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.533266 -4.908334
sample estimates:
mean of x mean of y
119.4314 124.6522

Since , p-value = 2.2e-16 ,
So, p-value < 0.05 ( α value)
Which is less then from 0.05 value so we Reject Null hypothesis
Hence we Conclude that there is a significance difference in mean blood pressures between the two groups.
Solution No. 2
One Way ANOVA
: There is no significant difference in survival among the three groups.
: There is a significant difference in survival among the three groups.
Now we solve this problem using R Software
Following is the R code for One Way ANOVA
Data = read.csv("C:/Users/CNC/Desktop/problem2.csv",)
names(Data)
attach(Data)
one_way_anova<-aov(DAYS_TO_DEATH~TREATMENT)
...
SOLUTION.PDF

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