Demographic Data Analysis - Study Material | Desklib

The article provides a detailed analysis of demographic data including age, sex, education, income, and cars. It includes descriptive statistics, pie charts, and cross-tabs for categorical vs categorical data. The article also includes T-tests for categorical vs scale variable. The study material is available for download at Desklib.

0Running head: DEMOGRAPHIC DATA ANALYSIS
DEMOGRAPHIC DATA ANALYSIS
Name of the student
Name of the university
Author’s note

DEMOGRAPHIC DATA ANALYSIS
Executive summary

DEMOGRAPHIC DATA ANALYSIS
Table of Contents
Introduction......................................................................................................................................2
Analysis...........................................................................................................................................2
Conclusion.......................................................................................................................................2
References........................................................................................................................................3
Appendix..........................................................................................................................................4

DEMOGRAPHIC DATA ANALYSIS
Introduction
Analysis
Descriptive statistics of demographic variable of the sample:
For age:
Table 1: Frequency table for Age
Categorie
s
Frequenc
y
Others 1
35 or less 20
36-45 45
46-55 51
56-65 18
65+ 10
Missing
data 10
Total 145
Others
1% 35 or less
13%
36-45
29%
46-55
33%
56-65
12%
65+
6%
Missing data
6%
Pie chart on age.

DEMOGRAPHIC DATA ANALYSIS
Fig 1: Pie chart for Age.
The age range starts from 0 years to more than 65 years. It can be seen from the
frequency table and the pie chart that people with ages in the range of 46 – 55 has the maximum
frequency that is of 51 the second highest frequency is of people in the age range 36 – 45. The
lowest frequency is of the group with age 65+ (Sharpe 2015). Rest of the groups of ages falls in
middle. There are few missing in the age data set.
For Sex:
Table 2: Frequency table for Sex
Categories
Frequenc
y
Female 57
Male 88
Missing
value 10
Total 155

DEMOGRAPHIC DATA ANALYSIS
Female
37%
Male
57%
Missing value
6%
Pie chart on Sex.
Fig 3: Pie chart for Sex.
The sex range is divided into male and female. It can be seen from the frequency table
and the pie chart that people that female population has the maximum frequency that is of 88
and the frequency of male population is a little less than this that is of 57 (Shi 2018). There are
few missing in the age data set.
For Education:
Table 3: Frequency table for education.
Category
Frequenc
y
Less than HS 2
High school
diploma 13
Some college 34
College grads 62
Graduate degree 33
Missing system 11

DEMOGRAPHIC DATA ANALYSIS
Total 155
Less than HS
1% High school diploma
8%
Some college
22%
college grads
40%
Graduate
degree
21%
Missing system
7%
Pie chart for education.
The education range starts from less than high school to graduate degree. It can be seen
from the frequency table and the pie chart that people who are college grads has the maximum
frequency that is of 62 and the second highest frequency is of people who are some college
grads and the frequency is 34. The lowest frequency is of the group of people whose highest
degree is less than HS.. Rest of the groups falls in middle. There are few missing in the age data
set.
Figure 4: Pie chart for Education.
For Income:-
Table 5: Frequency table for Income.
Category Frequenc

DEMOGRAPHIC DATA ANALYSIS
y
35K - 50K 10
50K - 65K 8
>65K 123
Missing
system 14
Total 155
35K - 50K
6% 50K - 65K
5%
>65K
79%
Missing system
9%
Pie chart for income.
Figure 5: Pie chart for income.
The income range starts from 35K to more than 65K. It can be seen from the frequency
table and the pie chart that people with income in the range of more than 65K has maximum
frequency and has the maximum percentage in the whole population (Kenny 2014). The lowest
frequency fall in the group 35K – 50K and with 5% people from the population. Another group
with income rage of 50K – 65K and with 7%people in the whole population falls in the middle.
There are few missing in the age data set.

DEMOGRAPHIC DATA ANALYSIS
For Cars:
Table 6: Frequency table for Cars.
Categor
y
Frequen
cy
Americ
an 58
Europe
an 38
Japane
se 59
Total 155
American
37%
European
25%
Japanese
38%
Pei chart for car.
Figure: Pie chart for Car.
The car range has three divisions and they are American, European and Japanese (Manrai
2016). It can be seen from the frequency table and the pie chart that people with Japanese car has
the highest frequency that is of 59 and consist of 38%of the population. People with American
cars have the second highest frequency that is of 58 and consist of 37% of the population. People

DEMOGRAPHIC DATA ANALYSIS
with European cars have the lowest frequency that is of 38 and consist of 25% of the whole
frequency.
Proposition and testing of hypothesis:-
Cross-tabs for categorical vs categorical data:-
1. Sex and Education:
A crosstab has been formed with the two categorical variable sex and education. The two
level of sex that is male and female are being kept in row division (Farg and Khalil 2015). The
five levels of education that is less than HS, high school diploma, some college, college grads
and graduate degree are being divided according to the rows. Cross-tab for the divisions is shown
below:
Table 7: Cross tab for sex and education.
Independent Samples Test
Levene's Test for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of the
Difference
Lower Upper
educ
Equal variances
assumed
6.571 .011 2.405 142 .017 .38474 .15998 .06849 .70099
Equal variances not
assumed
2.321
103.53
2
.022 .38474 .16579 .05595 .71353

DEMOGRAPHIC DATA ANALYSIS
It can be clearly seen from the table that the female population has a single person with
less than Hs degree, 8 people with high school diploma, 18 people with degree of some college,
158 people are college grads and 11 people has the graduate degree.
Similarly, it can be clearly seen from the table that the male population has a single
person with less than Hs degree, 5 people with high school diploma, 16 people with degree of
some college, 44 people are college grads and 22 people has the graduate degree (Pandis 2016).
The data can be further tested for a relation between the two variable with chi-square test.
Test details are:
Test hypothesis:-
H0 : There is no association between the two variables.
Vs.
H1 : The is association between the two variables.
Test statistic: ∑ (Oi−Ei )2 / Ei
Table for the test is given below:
Table 8: Chi square test table for Sex and education.
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 8.698a 4 .069
Likelihood Ratio 8.631 4 .071
Linear-by-Linear Association 5.597 1 .018
N of Valid Cases 144
a. 2 cells (20.0%) have expected count less than 5. The minimum
expected count is .78.

DEMOGRAPHIC DATA ANALYSIS
It can be seen from the table that calculated chi square is 8.698 and significant chi square is
0.069 which is less than tabulated one. Therefore, the null hypothesis can be rejected here and it
can be said that the two variables are not independent.
2. Car and sense of accomplishment:-
A crosstab has been formed with the two categorical variables car and sense of
accomplishment (Koletsi and Pandis 2016). The three categories of car that is American,
European and Japanese are being kept in row division. The 7 levels of sense of accomplishment
that is 1,2,3,4,5,6 and 7 are being divided according to the rows. Cross-tab for the divisions is
shown below:
Table 9: Cross tab for cars and sense of accomplishment
car * sense of accomplishment Crosstabulation
Count
sense of accomplishment Total
1.00 2.00 3.00 4.00 5.00 6.00 7.00
car
American 12 20 11 12 0 1 2 58
European 9 14 9 1 2 1 1 37
Japanese 12 18 18 0 2 5 3 58
Total 33 52 38 13 4 7 6 153
It can be clearly seen from the table that among people with American cars, 12 people
has satisfaction degree 1, 20 people has satisfaction degree 2, 11 people has satisfaction degree,

DEMOGRAPHIC DATA ANALYSIS
12 people has satisfaction degree 4, 0 people has satisfaction degree 5, 1 person has satisfaction
degree 6 and 2 people has satisfaction degree 7.
Again, it can be clearly seen from the table that the among people with European cars, 9
people has satisfaction degree 1, 14 people has satisfaction degree 2, 9 people has satisfaction
degree, 1 people has satisfaction degree 4, 2 people has satisfaction degree 5, 1 person has
satisfaction degree 6 and 1 person has satisfaction degree 7.
Similarly, it can be clearly seen from the table that the among people with Japanese car,
12 people has satisfaction degree 1, 18 people has satisfaction degree 2, 18 people has
satisfaction degree, 0 people has satisfaction degree 4, 2 people has satisfaction degree 5, 5
person has satisfaction degree 6 and 3 people has satisfaction degree 7.
The data can be further tested for a relation between the two variable with chi-square test.
Test details are:
Test hypothesis:-
H0 : There is no association between the two variables.
Vs.
H1 : The is association between the two variables.
Test statistic: ∑ (Oi−Ei )2 / Ei
Table for the test is given below:
Table 9: Chi square test table for Car and level of satisfaction.
Chi-Square Tests
Value df Asymp. Sig. (2-sided)

DEMOGRAPHIC DATA ANALYSIS
Pearson Chi-Square 25.297a 12 .013
Likelihood Ratio 29.006 12 .004
Linear-by-Linear
Association
.370 1 .543
N of Valid Cases 153
a. 12 cells (57.1%) have expected count less than 5. The minimum
expected count is .97.
It can be seen from the table that calculated chi square is 25.297 and significant chi
square is 0.13 which is less than tabulated one. Therefore, the null hypothesis can be rejected
here and it can be said that the two variables are not independent.
T-test for categorical vs scale variable:-
1. For age and sex:
Hypothesis are:
H0: mean of age is equal to mean of sex..
Vs.
H1 : mean of age is not equal to mean of sex..
Test statistic:- (x̅ - μ)/ σ/√n.
Table of calculation here is given below:-
Table 10:- T-test.
Independent Samples Test
Levene's Test for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of the
Difference
Lower Upper

DEMOGRAPHIC DATA ANALYSIS
age
Equal variances
assumed
2.568 .111 .361 143 .719 .06778 .18787 -.30358 .43915
Equal variances not
assumed
.349
106.48
7
.728 .06778 .19415 -.31712 .45269
It can be seen from the table that calculated t is 0.361 with the assumption of equal variance
and 0.349 without the assumption of equal variance. Again, significant t is 0.719 with equal
variance and 0.728 with unequal variance. Therefore, significant t is less than tabulated t in both
the cases and therefore the null hypothesis can be accepted in both the cases.
2. For Sex and education:-
Hypothesis are:
H0: mean of age is equal to mean of education.
Vs.
H1 : mean of age is not equal to mean of education.
Test statistic:- (x̅ - μ)/ σ/√n.
Table of calculation here is given below:-
Table 10:- T-test.

DEMOGRAPHIC DATA ANALYSIS
Independent Samples Test
Levene's Test for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of the
Difference
Lower Upper
educ
Equal variances
assumed
6.571 .011 2.405 142 .017 .38474 .15998 .06849 .70099
Equal variances not
assumed
2.321
103.53
2
.022 .38474 .16579 .05595 .71353
It can be seen from the table that calculated t is 2.405 with the assumption of equal
variance and 2.321 without the assumption of equal variance. Again, significant t is 0.719 with
equal variance and 0.022 with unequal variance (Helwani 2015). Therefore, significant t is less
than tabulated t in both the cases and therefore the null hypothesis can be accepted in both the
cases.
ANOVA for categorical vs scale variable:
1. Anova test for income and car can be written as:
Hypothesis are:
H0: mean of income is equal to mean of car.
Vs.
H1 : mean of income is not equal to mean of car.
Test statistic:- Mean square between group/mean square of error.

DEMOGRAPHIC DATA ANALYSIS

DEMOGRAPHIC DATA ANALYSIS
The analysis table is:
Table 11: ANOVA of car and income.
ANOVA
income
Sum of Squares df Mean Square F Sig.
Between Groups
(Combined) 1.182 1 1.182 3.984 .048
Linear Term
Unweighted 1.182 1 1.182 3.984 .048
Weighted 1.182 1 1.182 3.984 .048
Within Groups 41.257 139 .297
Total 42.440 140
It can be seen here the test statistic value is 3.984 and the significant value is 0.48 which
is less than statistic value (Burger et al. 2015). Therefore, the null hypothesis can be rejected
here and it can be predicted that the means are not equal.
2. Anova test for income and age can be written as:
Hypothesis are:
H0: mean of income is equal to mean of age.
Vs.
H1 : mean of income is not equal to mean of age.
Test statistic: - Mean Square between group/mean square errors.

DEMOGRAPHIC DATA ANALYSIS
The analysis table is:
Table 11: ANOVA of car and income.
ANOVA
income
Sum of Squares df Mean Square F Sig.
Between Groups
(Combined) 1.581 2 .791 2.671 .073
Linear Term
Unweighted .236 1 .236 .797 .374
Weighted .236 1 .236 .797 .374
Deviation 1.346 1 1.346 4.545 .035
Within Groups 40.858 138 .296
Total 42.440 140
It can be seen here the test statistic value is 2.671 and the significant value is 0.073 which
is less than statistic value (Yang et al. 2014). Therefore, the null hypothesis can be rejected here
and it can be predicted that the means are not equal.
Correlation for scale vs. scale:-
1. For sex and issue 1:-
Hypothesis are:
H0: They are not correlated.
Vs.
H1 : They are correlated.
Test statistic: - r*√(n-2)/√(1-r^2).

DEMOGRAPHIC DATA ANALYSIS
The table for the test is given below:-
Table 12: Table for correlation test
Correlations
sex major part of my fun
sex
Pearson Correlation 1 -.216**
Sig. (2-tailed) .009
Sum of Squares and
Cross-products
34.593 -26.278
Covariance .240 -.184
N 145 144
major part of my fun
Pearson Correlation -.216** 1
Sig. (2-tailed) .009
Sum of Squares and
Cross-products
-26.278 456.260
Covariance -.184 2.982
N 144 154
**. Correlation is significant at the 0.01 level (2-tailed).
It can be seen here that value of pearsonian statistic is -0.027 and the significant value is
0.784 which is greater than the value of statistic. Therefore, the null hypothesis can be accepted
here and it can be said that there is no correlation between issue 1 and age.
Bivariate regression for scale verses scale.
1. For being good to myself and major part of my fun:-
Hypothesis are:

DEMOGRAPHIC DATA ANALYSIS
H0: They are not related.
Vs.
H1 : They are related.
Table for test results is:-
Table 13: Regression table.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 147.535 1 147.535 72.639 .000b
Residual 308.724 152 2.031
Total 456.260 153
a. Dependent Variable: major part of my fun
b. Predictors: (Constant), being good to myself
It can be shown from the table that the value of f statistic is 72.639 and its significant value is
0.766 Which is smaller than the f statistic value (Näsman 2015). Therefore, there exist a relation
between being good to myself and major part of my fun.s
The regression line can be calculated here. The table for co-efficient is given below:
Table 14: Table for coefficients.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 147.535 1 147.535 72.639 .000b
Residual 308.724 152 2.031
Total 456.260 153

DEMOGRAPHIC DATA ANALYSIS
a. Dependent Variable: major part of my fun
b. Predictors: (Constant), being good to myself
From the table of coeffic8ient it can be seen that the tabulated t value of being good to
myself is 8.523 and the significant t value is 1.96 which is smaller than the significant t.
therefore, the null hypothesis can be rejected and the factor can be accepted here. Therefore, the
regression line is:
major part of my fun = 2.147 + 0.509 * being good to myself.
2. For age and being good to myself:
Hypothesis are:
H0: They are not related.
Vs.
H1 : They are related.
The required regression table is:
Table 14: Regression table.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .079 1 .079 .065 .799b
Residual 174.679 143 1.222
Total 174.759 144
a. Dependent Variable: age

DEMOGRAPHIC DATA ANALYSIS
b. Predictors: (Constant), being good to myself
It can be seen from the table that the tabulated F value is 0.065 and the critical F value is 0.762
which s greater than the calculated value. Therefore, the null hypothesis can be rejected here and
it can be said that the two variables are related.
The regression line can be calculated here. The coefficient table is attached below:
Table 15: Coefficient table.
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 3.615 .183 19.767 .000
being good to myself .012 .048 .021 .255 .799
a. Dependent Variable: age
It can be seen from the table that the t statistic value is 19.767 and the t significant value
is 1.96, which is smaller than t significant value. Therefore, the coefficient value of the factor can
be accepted here and the regression line can be calculated as
Sex = 3.615 + 0.012*being good to myself.

DEMOGRAPHIC DATA ANALYSIS
Interpretation of the findings:
It can be interpreted here that sex and education are interrelated that is the level of
education is related with sex. Sense of accomplishment and cars are related.. Again, the means
between age and sex are the same and the means between sex and education are not same. There
may not be an equal division of the two sex regarding age groups and education is also not
divided equally among the two sex group. The mean income and the mean age are also not the
same. Mean income and mean purchase of the category of cars are not the same. There is no
correlation between being good to myself and age and there exists a relation between good to
myself and main reason of my fun. Age and being good to myself are also related to each other.
Conclusion:
There are numbers relations that can be explained with the study. Member of a particular
sex have an easy assess for education and it is not that easy an assess for members of the
opposite sex. People with higher education levels can be proved to be more intellectual
employees. People with a particular type of car are more accomplished than the other and can
increase the sale of that car type. Sex is not equally divided in the different age groups and all of
the age divisions does not earn equally. Therefore, this can effect demand of few product which
are sex centric. Earning cause a huge impact on purchase and demand. Age is not related to
being good to one’s own self but being good to one’s own self is related to having fun. The fun
factor can effect purchase market as fun can be materialistic.

DEMOGRAPHIC DATA ANALYSIS
Recommendations
It can be recommended here that since the sex division is not equal, market should focus
more on demand type of the dominant sex. Company should also focus on hiring intellectual
employee that is educated employee which is also dependent on sex.

DEMOGRAPHIC DATA ANALYSIS
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DEMOGRAPHIC DATA ANALYSIS
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