Math 1065 Quantitative Methods In Assessment Answers
Questions:
chart.
Answer:
it can be inferred that the percentage of people who took a nap and who did not take a nap in the age group of 80+ is approximately equal. For other groups, 18 – 29, 30 – 49, 40 – 69 and 70 – 79 the percentage of people who did not take a nap is more than the percentage of people who did take a nap.
it can be inferred that percentage of people who were pretty happy and very happy when they did not take a nap was more than when they did take a nap.
Table 1: Distribution Age and Nap
Rows: Age Columns: Nap
|
No |
Yes |
All |
|
|
|
|
18-29 |
163 |
82 |
245 |
|
66.53 |
33.47 |
100.00 |
|
|
|
|
30-49 |
193 |
106 |
299 |
|
64.55 |
35.45 |
100.00 |
|
|
|
|
50-69 |
280 |
135 |
415 |
|
67.47 |
32.53 |
100.00 |
|
|
|
|
70-79 |
265 |
140 |
405 |
|
65.43 |
34.57 |
100.00 |
|
|
|
|
80+ |
40 |
44 |
84 |
|
47.62 |
52.38 |
100.00 |
|
|
|
|
All |
941 |
507 |
1448 |
|
64.99 |
35.01 |
100.00 |
Cell Contents
Count
% of Row
From the above table it is found that 52.38% of 80+ age citizens take a nap. 67.47% of people in the age group of 50 – 69 years’ age do not take a nap.
Table 2: Distribution mood and Nap
Rows: Mood Columns: Nap
|
No |
Yes |
All |
|
|
|
|
Not too happy |
303 |
267 |
570 |
|
53.16 |
46.84 |
100.00 |
|
|
|
|
Pretty happy |
385 |
138 |
523 |
|
73.61 |
26.39 |
100.00 |
|
|
|
|
Very happy |
253 |
102 |
355 |
|
71.27 |
28.73 |
100.00 |
|
|
|
|
All |
941 |
507 |
1448 |
|
64.99 |
35.01 |
100.00 |
Cell Contents
Count
% of Row
From the above table it can be inferred that of “pretty happy” citizens 73.61% did not take a nap. Similarly, “very happy” citizens 71.27% did not take a nap. On the other hand, from “Not too happy” people 53.16% did not take a nap and 46.84% did take a nap.
The histogram (figure 3) of Cold days of the two groups suggests that the distribution of cold days is positively skewed. Thus it can be inferred that the mean number of cold days is less than the median number of cold days.
From the (figure 4) there is no indication of outliers in number of cold days.
From both the histogram and boxplot it can be inferred that the data for cold days is right skewed. Since the data is not normally distributed, hence median would be the appropriate measure of central tendency. The median number of Cold days for Elderberry is 4.0 and for Placebo is 6.0 respectively. The median is used as a measure of central tendency. Thus IQR would be the ideal measure of dispersion. The IQR for cold days for Elderberry is 3.750 (6.750 – 3.000). The IQR for cold days for Placebo is 4.500 (9.50 – 4.00).
The mean number of cold days using Elderberry is 4.667 while for placebo is 6.882. The maximum number of cold days in Elderberry is 9.00 while for Placebo is 15.00. Moreover, the median number of cold days for Elderberry is 4.00 and for Placebo is 6.00. Thus it can be inferred that by using Elderberry the number of Cold days is less as compared to Placebo.
Let X denote the mean glucose level
The mean glucose level is normally distributed with and
The probability that the mean glucose level is P(X>140mg/dl)
Method 1: Use Standard Normal Tables
z-score for
Thus,
From z-table it is found that P(z = -1.5) = 0.933
Therefore, the probability that the patient has gestational diabetes = 0.067
Using MINITAB, a normal distributed random sample of size 5 is created.
Sample |
121.085 |
138.256 |
110.773 |
138.132 |
133.129 |
The mean and standard of the sample is 128.27 and 12.02 respectively.
Method 1: Use Standard Normal Tables
z-score for
Thus,
From z-table it is found that P(z = ) = 0.83522
Therefore, the probability that the patient has gestational diabetes = 0.16478
Method 2: Minitab
Method 1: Use Standard Normal Tables
Rearranging the distribution from ascending to descending:
Sample |
110.773 |
121.085 |
133.129 |
138.132 |
138.256 |
The median value is = 133.29
The first quartile =
The third quartile =
Thus, the inter-quartile range =
Method 2: Minitab
The third quartile value for glucose = 138.19.
The first quartile value for glucose = 115.93
Thus the Inter quartile range for the amount of glucose = 138.19-115.93 = 22.26
Method 1: Use Standard Normal Tables
Let the mean waiting time be represented as
Let the standard deviation of the waiting time be represented as
Thus the probability that the mean waiting time is less than 15 minutes is represented as
Further, the probability that the mean waiting time is more than 60 minutes is represented as
From calculations it is found that for P = 0.25, z = -0.6745
For P(z<-06745), z = -0.6745
From calculations it is found that for P = 0.05, z = -1.6449
For P(z>-1.6449), z = 1.6449
Thus, (i)
and (ii)
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