HA1011S - Applied Qualitative Research Assignment

This assignment explores various aspects of applied qualitative research, including data analysis, statistical methods, and interpretation of results. It covers topics such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing. The assignment provides a comprehensive understanding of how to apply qualitative research methods to real-world problems.

HA1011S - Applied Qualitative

Table of Contents
Question 1.......................................................................................................................................4
(a).....................................................................................................................................................4
(b)....................................................................................................................................................4
(c).....................................................................................................................................................5
Question 2.......................................................................................................................................7
(a).....................................................................................................................................................7
(b)....................................................................................................................................................7
(c).....................................................................................................................................................7
(d)....................................................................................................................................................8
Question 3.......................................................................................................................................9
(a).....................................................................................................................................................9
(b)....................................................................................................................................................9
Question 4.....................................................................................................................................10
(a)...................................................................................................................................................10
(b)..................................................................................................................................................10
(c)...................................................................................................................................................10
(d)..................................................................................................................................................10
Question 5.....................................................................................................................................11
(A)..................................................................................................................................................11
(B)..................................................................................................................................................11
Question 6.....................................................................................................................................11
(A)..................................................................................................................................................11
Question 7.....................................................................................................................................11
(A)..................................................................................................................................................11

(B)......................................................................................................Error! Bookmark not defined.
Question 8.....................................................................................................................................13
(A)..................................................................................................................................................13
(B)..................................................................................................................................................13
References.....................................................................................................................................14

Question 1
(a)
Class limits Frequency Relative Frequency Cumulative
relative
frequency
Mid-point
169-969 36 0.60 0.6 569
969-1769 18 0.30 0.90 1369
1769-2569 2 0.03 0.93 2169
2569-3369 3 0.05 0.98 2969
3369-4169 0 0.00 0.98 3769
4169-4969 0 0.00 0.98 4569
4969-5769 0 0.00 0.98 5369
5769-6569 0 0.00 0.98 6169
6569-7369 0 0.00 0.98 6969
7369-8169 1 0.02 1.00 7769
Total 60 1.00 1
(b)
169-969 969-
1769 1769-
2569 2569-
3369 3369-
4169 4169-
4969 4969-
5769 5769-
6569 6569-
7369 7369-
8169
0
5
10
15
20
25
30
35
40
Class Limits
No. of passengers
Histogram showing the number of passengers at
each train station in Melbourne

(c)
No. of
passengers
169
248
262
267
268
272
318
323
338
344
379
382
399
401
401
410
429
435
456
494
530
538
548
579
583
637
648
658
682
697
733
862
866
878
906

962
982
1000
1021
1022
1113
1115
1178
1181
1189
1240
1311
1359
1442
1584
1606
1618
1632
1750
1946
2268
2630
2830
2958
7729
Mean 1033.43
Media
n
715
Mode 401

Question 2
(a)
The given data is not population and is merely a sample. This is because only a particular week
has been taken for the study purpose. For population, data relating to a particular year has to be
taken.
(b)
Weekly attendance Deviation from
the mean (x-mean) (x-mean)^2
472 -12.57 158.04
413 -71.57 5122.47
503 18.43 339.61
612 127.43 16238.04
399 -85.57 7322.47
538 53.43 2854.61
455 -29.57 874.47
Sum 3392 0.00 32909.71
Count 7 7 7
Average (Mean) 484.57
Variance 5484.95
Standard deviation 74.06
(c)
Number of
chocolate bars sold
6916
5884
7223
8158
6014
7209
6214
Quartile 1 6114

Quartile 3 7216
Inter Quartile
Range 1102
(d)
Weekly
attendance
Number of
chocolate bars
sold
472 6916
413 5884
503 7223
612 8158
399 6014
538 7209
455 6214
Column 1 Column 2
Column 1 1
Column 2 0.967992639 1
Thus, the coefficient of correlation between weekly attendance and no. of chocolate bars ist to be
0.97.

Question 3
(a)
Regression Statistics
Multiple R 0.967992639
R Square 0.93700975
Adjusted R Square 0.9244117
Standard Error 224.5951736
Observations 7
ANOVA
df SS MS F
Significance
F
Regression 1 3751816.754
375181
7 74.38 0.00
Residual 5 252214.9601 50443
Total 6 4004031.714
(b)
Sample size 7
Coefficient of
Determination 99%

Question 4
(a)
(35+92+12)/ (35+92+54+12) = 139/193 = 0.72
(b)
54/193 = 0.28
(c)
35/(35+92) = 0.275
(d)
Training and recruitment are two different concepts and aspects of human resource and thus both
involves require different resources. There can be different managers for training and
recruitment. Generally recruitment proceeds training.

Question 5
(A)
20%
(B)
20/ (20+35+60+90) = 0.10 or 10%
Question 6
(A)
The probability that only 2 or less of those 8 people will buy anything is 1/8 = 0.125 or 12.5%.
Question 7
(A)
z = x - / 
Z = 2 – 1.1/ 385000
Z = 2.34
P (0<z<2.34) = 0.4974
P (z>2.34) = 0.5 - P (0<z<2.34)
= 0.5 – 0.4974
= 0.0026
(B)
P (1 million < z < 1.1 million) = 1.1 – 1/385000 = 0.26
= 0.1026

P (1.1 million < z < 1.1 million) = 1.1 – 1.1 / 385000 = 0
P (1 million < z < 1.1 million) = Area A + Area B
= 0.1026 or 10.26% (as computed earlier)

Question 8
(A)
The basic advantage of z distribution is that it can be used even when the distribution is not
normal. Thus z distribution can be used in the situation when the distribution is skewed to the left
or right. Besides, it can be used when the researcher have exponential or even uniform
population. Thus it can be used in any situation.
(B)
Z distribution = x= p−P
√ p (¿❑ 1− p)/n¿
P = (z > 0.30- 0.24/0.24*0.76/45)
P (0 < z < 0.9375) = 0.3238 + 0.5000 = 0.8238
Thus the probability that more than 30% or more of the investors would like to commit $1
million or more of the investors would be willing to commit $1 million or more to the fund is
appro 82%.

References
 Barnes, E. A., & Barnes, R. J. 2015. Estimating linear trends: Simple linear regression versus
epoch differences. Journal of Climate, 28(24), 9969-9976.
 Elamir, E. A. 2015. Analysis of Mean Absolute Deviation for Randomized Block Design under
Laplace Distribution. American Journal of Theoretical and Applied Statistics, 4(3), 138-149.
 Figueiredo Filho, D. B., Paranhos, R., Rocha, E. C. D., Batista, M., Silva Jr, J. A. D., Santos, M.
L. W. D., & Marino, J. G. 2013. When is statistical significance not significant?. Brazilian
Political Science Review, 7(1), 31-55.
 Frost, J., 2013. How to Interpret Regression Analysis Results: P-values and Coefficients. The
Minitab Blog. [Online]. Also available at http://blog.minitab.com/blog/adventures-in-statistics-2/how-
to-interpret-regression-analysis-results-p-values-and-coefficients
 Main, M. E., & Ogaz, V. L. 2016. Common Statistical Tests and Interpretation in Nursing
Research. International Journal of Faith Community Nursing, 2(3), 5.
 Nahas, J. J., 2012. Statistical Design of Experiments‐Part IV Analysis of Variance. University of
Notre dam. [Online]. Also available at https://www3.nd.edu/~jnahas/DoE_III_ANOVA_V2.pdf.
 Schneider, J. W. 2015. Null hypothesis significance tests. A mix-up of two different theories: the
basis for widespread confusion and numerous misinterpretations. Danish Centre for Studies in
Research and Research Policy, 102(1), 411-432.
 Winter, B. 2015. The F distribution and the basic principle behind ANOVAs. Tutorial. Also
available at http://www.bodowinter.com/tutorial/bw_anova_general.pdf.