THIS IS NOT MY WORK: I AM TRYING TO UNDERSTAND HOW THE CALCULATIONS WERE MADE SO THAT I CAN DO MY PROBLEM THAT IS SIMILIAR!

I do not understand how they came up with the xbar of 139.2 and the sample standard deviation of 9.20 . When these numbers are put into excel they do equal what the other calculations are but I need to understand how they came up with the xbar and standard deviation. The parts that I do not understand are put in BOLD

Significance Test for t (One Sample)

xbar 139.2

Hypothesized Value 140

Sample Standard Deviation 9.20

n 30

Significance Level (alpha) 0.01

Number of Tails 2

df 29

Test Statistic (t) -0.47628048

P-Value (Left) 0.63744347 3

Critical Value 2.75638590 4

Confidence Interval for t (One Sample)

Sample Mean (xbar) 139.2

Sample STDEV (s) 9.2

n 30

Confidence Level 0.99

df 29

tstar 2.75638590 4

Lower Limit 134.570146 8

Upper Limit 143.829853 2

PART A: THE TEST

The test being conducted today will help to determine reliability of being able to diagnose patients with diabetes through the form of blood testing. My test includes 30 patients to be tested for diabetes through their blood. The data was conducted using a randomize sample of glucose readings between 125 - 145. I was able to get a STD DEV (9.2). Above table will show the starting values to begin the testing process. The decision to use the t-test was based on having unknow data for sigma.

PART B:

Randomization is a useful tool as it will automatically sort data you need, you're your test with the given range you provide. I chose to randomize my blood test with a range of 125 to 145. This is using the normal range for a normal glucose reading of 140. The randomizer generated 30 different possible outcomes for blood levels. I received 30 sample values as this is my sample size of patient for the experiment. Knowing the reliability of using the randomization process was beneficial to my experiment.

PART C:

My experiment used a right tail t-test This helped to determine the inferential by using the hypothesized value of 140 also known to be the standard blood level for my testing. Using this testing helped to justify that my sigma was indeed an unknown value. Because my average level was 140, using this testing helped me to determine the number of values that were >140. Any of my patients that had a glucose level >140 can be at risk for having gestational diabetes. Knowing the outcome to be determined, the right tailed t-test is necessary for this determination.

PART D:

H0: = 139.2 (null)

H 1: > 139.2 (alternative)

Sample Mean: X 139.2 The hypothesized value is used to represent the population of

Sample STD DEV: s 9.2 the test.

Sample Size: n 30

Ho: u 139.2

Ha: u 139.2

Level of Significance: a 0.01

Part E:

Confidence Interval for t (One Sample)

Sample Mean (xbar) 139.2

Sample STDEV (s) 9.2

n 30

Confidence Level 0.99

df 29

tstar 2.756385904

Lower Limit 134.5701468

Upper Limit 143.8298532

PatientGlucose

1 153

2 145 D. STATISTICS

3 131 MEAN 140.37

4 151 MEDIAN 140

5 126 MODE 131

6 136 VARIANCE 70.30

7 135 STD DEV 8.53

8145

9131

10133

11146

12150

13142

14140

15133

16129

17152

18134

19139

20147

21131

22137

23140

24152

25144

26129

27154

28140

29133

30153

Because of random testing the numbers are always changing. The sample size was 30 and this was chosen to ensure we had enough patients to justify accurate data. The confidence level was 99%, the mean and STD DEV was also recorded in the section from the given randomized data set.

Lower Limit: =Sample Mean-(tstar*Std Dev/SQRT(n)) = 134.6

Upper Limit: =Sample Mean+(tstar*Std Dev/SQRT(n)) = 143.8

Part F:

The confidence interval is (135, 144). The sample mean is 139.2. This mean lies within the confidence interval. The p-value is 0.6374 which means it is closer to 100%; in other words, the results were significantly high. With all of the given information, I was able to calculate the data to gain the information needed to prove my testing of the reliability of monitoring diabetes through the blood. Because my t-value was less than 0.05, it was determined I did not fail to reject the null hypothesis. This mean the null is not accepted and the recipients had diabetes.