The document describes the research methodology used in a study of Qubee, a telecommunications provider. It discusses: 1. Data was collected through a questionnaire survey of 50 respondents to understand perceptions of Qubee's services. 2. The survey data was input into SPSS and descriptive statistics were calculated, including measures of central tendency, variability, and distributions. 3. The descriptive statistics are interpreted to analyze aspects of Qubee's services like customer satisfaction, loyalty, and how service attributes like price and quality impact customer perceptions.

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 Methodology:
Data Collection: Questionnaire & Survey
Nowadays, internet turned out as a basic need for survival where Qubee has created its own appreciative brand value. Based on a semi-structured
questionnaire, the perceptions and relevant data have been collected from the 50 respondents which ultimately considered as the sample of this
research.
Beneath the particular questionnaire the number of queries, like- frequency of using service, customer service, problem solving etc. have been
covered. In fact, the recommendations are also taken from the respondents for the purpose of improving present lacking or existing features.
Data Input: SPSS (Variable)
After completing the survey all the variables are given input in the SPSS beneath the “Variable View” option. However the values, patterns, measures
of those variables have also been set. For example for the variable “Service”- the data pattern was numeric, measure was in scale.
Value Setting: SPSS (Data View)
As the variables are set the values of the variables are given input under the “Data View” section of SPSS, alike: for the variable “Service” (duration
of using Qubee), in a particular questionnaire the value was “3” (3 years to less than 5 years). As like as the variable “Service” all the values of other
variables have been located.
Analysis:
Afterwards, based on the data input the analysis is done by calculating the following-
1. Descriptive Statistics
2. Correlation &
3. Regression
Interpretations:
At the end of getting the output or results of the calculation shown above the proper interpretations of those is prepared. However, depending on the
separate statistical calculation separate interpretation has been stated that reflects how effectively Qubee is operating its operations. Moreover, from
the regression analysis Qubee may came to know how long its customers will show their loyalty towards their service.
Beneath, the separate calculation separate interpretations have been stated below:

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1. Interpretation of Descriptive Statistics-
Descriptive Statistics
N Range Minimum
Maximu
m
Mean
Std.
Deviation
Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std. Error Statistic
Std.
Error
Service 50 3.00 .00 3.00 1.5400 .15175 1.07305 1.151 -.005 .337 -1.233 .662
Frequency 50 4.00 .00 4.00 1.7000 .16963 1.19949 1.439 .392 .337 -.890 .662
Professionalism (1) 50 3.00 .00 3.00 .8200 .12352 .87342 .763 .940 .337 .331 .662
Quality_ product 50 3.00 .00 3.00 .6600 .11990 .84781 .719 1.357 .337 1.486 .662
Customer needs 50 4.00 .00 4.00 1.0600 .14402 1.01840 1.037 .842 .337 .235 .662
Sales_ staff 50 3.00 .00 3.00 1.2200 .13489 .95383 .910 .418 .337 -.659 .662
Price 50 4.00 .00 4.00 1.4600 .14058 .99406 .988 .634 .337 -.326 .662
Resolved_ problem 50 3.00 .00 3.00 1.3400 .13282 .93917 .882 .332 .337 -.680 .662
Follow_ up_ call 50 1.00 .00 1.00 .3400 .06767 .47852 .229 .697 .337 -1.580 .662
Professionalism (2) 50 3.00 .00 3.00 .9200 .13352 .94415 .891 .771 .337 -.287 .662
Responsiveness 50 3.00 .00 3.00 .8400 .11198 .79179 .627 .812 .337 .527 .662
Politeness 50 3.00 .00 3.00 .7600 .11270 .79693 .635 1.222 .337 1.783 .662
Knowledge_ of _
Problem
50 3.00 .00 3.00 1.1200 .10552 .74615 .557 .414 .337 .212 .662
Efficiency_ in _solving_
problem
50 3.00 .00 3.00 1.2400 .11270 .79693 .635 .542 .337 .164 .662
Manner_ of_ handling_
follow-up_ questions
50 4.00 .00 4.00 1.1800 .13906 .98333 .967 .830 .337 .385 .662
Satisfaction_ with_
Customer_ support
50 3.00 .00 3.00 1.0200 .12286 .86873 .755 .739 .337 .172 .662
Satisfaction_ overall 50 3.00 .00 3.00 1.0600 .12259 .86685 .751 .665 .337 .048 .662
Continue_ doing_
business
50 3.00 .00 3.00 1.0400 .13389 .94675 .896 .670 .337 -.342 .662
Recommendation 50 3.00 .00 3.00 1.3000 .14070 .99488 .990 .000 .337 -1.158 .662
Gender 50 1.00 .00 1.00 .4800 .07137 .50467 .255 .083 .337 -2.078 .662
Age 50 7.00 .00 7.00 2.5400 .23072 1.63145 2.662 .822 .337 .890 .662
Income 50 7.00 .00 7.00 3.1200 .35873 2.53659 6.434 .376 .337 -1.250 .662

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Descriptive Statistics
N Range Minimum
Maximu
m
Mean
Std.
Deviation
Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std. Error Statistic
Std.
Error
Service 50 3.00 .00 3.00 1.5400 .15175 1.07305 1.151 -.005 .337 -1.233 .662
Frequency 50 4.00 .00 4.00 1.7000 .16963 1.19949 1.439 .392 .337 -.890 .662
Professionalism (1) 50 3.00 .00 3.00 .8200 .12352 .87342 .763 .940 .337 .331 .662
Quality_ product 50 3.00 .00 3.00 .6600 .11990 .84781 .719 1.357 .337 1.486 .662
Customer needs 50 4.00 .00 4.00 1.0600 .14402 1.01840 1.037 .842 .337 .235 .662
Sales_ staff 50 3.00 .00 3.00 1.2200 .13489 .95383 .910 .418 .337 -.659 .662
Price 50 4.00 .00 4.00 1.4600 .14058 .99406 .988 .634 .337 -.326 .662
Resolved_ problem 50 3.00 .00 3.00 1.3400 .13282 .93917 .882 .332 .337 -.680 .662
Follow_ up_ call 50 1.00 .00 1.00 .3400 .06767 .47852 .229 .697 .337 -1.580 .662
Professionalism (2) 50 3.00 .00 3.00 .9200 .13352 .94415 .891 .771 .337 -.287 .662
Responsiveness 50 3.00 .00 3.00 .8400 .11198 .79179 .627 .812 .337 .527 .662
Politeness 50 3.00 .00 3.00 .7600 .11270 .79693 .635 1.222 .337 1.783 .662
Knowledge_ of _
Problem
50 3.00 .00 3.00 1.1200 .10552 .74615 .557 .414 .337 .212 .662
Efficiency_ in _solving_
problem
50 3.00 .00 3.00 1.2400 .11270 .79693 .635 .542 .337 .164 .662
Manner_ of_ handling_
follow-up_ questions
50 4.00 .00 4.00 1.1800 .13906 .98333 .967 .830 .337 .385 .662
Satisfaction_ with_
Customer_ support
50 3.00 .00 3.00 1.0200 .12286 .86873 .755 .739 .337 .172 .662
Satisfaction_ overall 50 3.00 .00 3.00 1.0600 .12259 .86685 .751 .665 .337 .048 .662
Continue_ doing_
business
50 3.00 .00 3.00 1.0400 .13389 .94675 .896 .670 .337 -.342 .662
Recommendation 50 3.00 .00 3.00 1.3000 .14070 .99488 .990 .000 .337 -1.158 .662
Gender 50 1.00 .00 1.00 .4800 .07137 .50467 .255 .083 .337 -2.078 .662
Age 50 7.00 .00 7.00 2.5400 .23072 1.63145 2.662 .822 .337 .890 .662
Income 50 7.00 .00 7.00 3.1200 .35873 2.53659 6.434 .376 .337 -1.250 .662
Valid N (list-wise) 50

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 Number of Observations (N)-
Number of observation=50
 Range-
Range indicates to the inconsistency of the data set.
For the variable “Service”, data set variability is up to 3.
For the variable “Frequency” data set variability is up to 4.
However, for the variable “Follow up call” variability is only up to 1.
For the “Age” and “Income” the variability is up to 7.
All in all, the average variability is around 3.5.
 Minimum-
Data set of all the variables has minimum value of 0.
 Maximum-
For the variable “Service” & “Quality_ Service”, maximum data set value is 3.
For the variable “Frequency” & “Customer Needs”, maximum data set value is 4.
However, for the variable “Age” & “Income”, maximum data set value is 7
And, for the variable “Follow up call” & “Gender”, maximum data set value is 1.
Moreover, the average maximum data set value is approximately 3.6.
 Mean-
Mean signifies as the mathematics standard value of the data set which is also called central tendency.
For the variable “Service”, mean is “1.54”. That means, all the respondents use Qubee’s service around 3-4 years.
For the variable “Price”, mean is “1.46”. That means, all the respondents are somewhat satisfied and to some extent neutral about the price of service
of Qubee
In addition, for the variable “Frequency”, mean is 1.7 which means, the respondents use the service of Qubee habitually.
For the variable “Age” and “Income” the mean is 2.54 and 3.12, that indicates the entire respondent’s buying or use of the service of Qubee depends
or varies much on the basis of age and income.

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However, “Satisfaction with Customer Support” and “Gender”, both the variables has the lowest mean among all the other variables that are 1.02
and .48. It designates that the factor gender doesn’t create any differences in the uses or purchase of Qubee’s service where the satisfaction with the
Qubee’s customer support seems low.
 Standard Error-
Standard error is simply the sampling error that measures how much the sample is statistically viable.
For the variables “Follow up call” and “Gender” the data set range is 1 for both where the standard error of “.06767” (Follow up call) and “.07137”
(Gender) seems very low.
For the variable “Frequency” of purchase, data set range is 3 where the standard error of “.16963” is comparatively high.
Conversely, the data set range of the variable “Age” are 7 in which standard error of “.23072” is moderately low from the variable “Frequency”.
But the data range of “Income” was also 7 where the standard error is “.35873” which is somewhat high in respect of data range.
 Standard Deviation-
Standard deviation; also designated as mean deviation, evaluates how much individual values fluctuates from the mean.
However, all the Standard deviations significantly differ from the mean.
For the variable “Age” the mean was “2.5400” but the standard deviation of this is “1.63145”. The deviation seem high and thus it’s can’t be an
appropriate representative of the distribution.
For the variable “Frequency”, Standard deviation is “1.19949” which is relatively high. Consequently, the mean value is not a good representative of
the distribution.
For the variable “Follow up call”, standard deviation is “.47852” which is relatively low from the previous one. Hence, it can be said that the mean
value is a good representative of the distribution.
 Variance
For almost all the variables “variance” seems near to the mean of “mean”, likewise-
For the variable “Service” the variance is “1.151” which differs from the mean only by “.389”. It means it’s a good representative of mean.
But for the variables like “Sales staff”, “Price” and “Resolved problem” the variances are “.910”, ”.988” and “.882”, which is pretty much
deviated. Thus it’s not a good representative of the mean.
Variance measures the same as the standard deviation. The only difference between variance and standard deviation is that standard deviation
measures deviation from mean in the same unit as of the data set while variance measures the deviation in the square unit of the data set.

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 Skewness
o However, if the value is negative, skewness will be on left.
o If the value is positive skewness will be on right.
Skewness of normal distribution is 0. For the variable “Recommendation”
shows “.000” which seems the distribution has very high middle skewnwss
that is normal distribution.
The skewness of the variable “Service” is “-.005” which means that the
distribution has very low left skewness and is close to normal distribution.
In comparison, the skewness of the variable “Politeness” is “1.222”, which means that the distribution is highly right skewed.
 Kurtosis
Kurtosis designates to whether the distribution is steep or flat than the normal
distribution.
The kurtosis of the normal distribution is 3.
However, the values of kurtosis beneath all the variables are less than 3
Even some of them are negative which indicates to that all the distributions are
flatthan the normal distribution.

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2. Interpretation of Correlations-
Correlations
Variables Product
Quality _
product
Price Sales staff
Resolved_
Problem
Follow up
call
Income
Continue
doing
Business
Service
Pearson Correlation 1 -.400**
-.257 -.198 -.368**
-.365**
.081 -.564**
Sig. (2-tailed) .004 .072 .168 .009 .009 .578 .000
N 50 50 50 50 50 50 50 50
Quality_ Service
Pearson Correlation -.400**
1 .601**
.549**
.379**
.291*
.029 .704**
Sig. (2-tailed) .004 .000 .000 .007 .041 .842 .000
N 50 50 50 50 50 50 50 50
Price
Pearson Correlation -.257 .601**
1 .472**
.244 .351*
-.144 .587**
Sig. (2-tailed) .072 .000 .001 .087 .012 .319 .000
N 50 50 50 50 50 50 50 50
Sales staff
Pearson Correlation -.198 .549**
.472**
1 .348*
.190 .124 .442**
Sig. (2-tailed) .168 .000 .001 .013 .185 .392 .001
N 50 50 50 50 50 50 50 50
Resolved_
Problem
Pearson Correlation -.368**
.379**
.244 .348*
1 .419**
.171 .329*
Sig. (2-tailed) .009 .007 .087 .013 .002 .235 .020
N 50 50 50 50 50 50 50 50
Follow up call
Pearson Correlation -.365**
.291*
.351*
.190 .419**
1 .218 .375**
Sig. (2-tailed) .009 .041 .012 .185 .002 .128 .007
N 50 50 50 50 50 50 50 50
Income
Pearson Correlation .081 .029 -.144 .124 .171 .218 1 .023
Sig. (2-tailed) .578 .842 .319 .392 .235 .128 .872
N 50 50 50 50 50 50 50 50
Continue_ doing_
business
Pearson Correlation -.564**
.704**
.587**
.442**
.329*
.375**
.023 1
Sig. (2-tailed) .000 .000 .000 .001 .020 .007 .872
N 50 50 50 50 50 50 50 50
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

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Correlation
The correlation co-efficient of the variables “Quality _ Service” and “Continue_ doing_ business” is “.704” which signifies that there is a strong
and positive correlation between them and they move in the same direction.
The significant value reflects the risk of rejecting of null hypothesis. The significant value for the variables “Quality_ Service” and “Continue_
doing_ business” usage” is “.000”. It means that there is no risk of risk of rejecting the null hypothesis and the correlation between the variables is
highly statistically significant.
In other worlds, the correlation between the variables “Quality_ service” and “Continue_ doing_ business” is highly
statistically significant.
On the other hand, the correlation co-efficient of the variables “Service” and “Sales staff” is “-.198” which means that there is a weak and negative
correlation between the variables and they move in the opposite direction.
However, the significant value for the variables is “.168” which is very high than the level of significance (α=5%). That is, the risk of rejecting null
hypothesis is 16.8% which is very high.
So the null hypothesis cannot be rejected. It has to be accepted which signifies that there is no statistically significant relationship between the variables
“Service” & “Sales staff”

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3. Interpretation of Regression-
Coefficient
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) .164 .239 .688 .495
Quality_ service .582 .148 .521 3.924 .000
Price .252 .123 .265 2.051 .046
Resolved_
Problem
.061 .112 .061 .547 .587
Income .013 .039 .036 .345 .732
a. Dependent Variable: Continue doing business
From the above table-
α= .164
β1=.582
β2= .252
β3=.061 and
β4=.013
So, the regression equation is-
Continue doing business= .164+.582 β1+.252 β2+.061 β3+.013 β4
i.e. Continue doing business=.164+.582 Quality_ Service +.252Price +.061 Resolved Problem +.013Income
Thus, if “Quality_ Service” is increased or decreased by 1 unit, “Continue doing business” is increased or decreased respectively by “0.582” unit.
 t value
“t” value is the assessment statistics of hypothesis. It measures how much “B” is statistically significant and to verify whether B is
statistically significant or not, significant value is used.
The significant value for “Quality_ service” and “Price” are “.000” and “.046” respectively which are lower than α (5%), that is the risk of
rejecting null hypothesis in the former case is zero and in the latter case is very low. So, the null hypothesis (β1=0 & β2=0) can be rejected.
Therefore it can be said that “Continue doing business” has a high statistically significant relationship with both “Quality_ service” and
“Price”.

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On the other hand, the significant value for “Income” is “.732” which is very high than α (5%), that is the risk of rejecting null hypothesis is
very high (73.2%). Therefore, the null hypothesis (β4=0) should be accepted.
Consequently, it can be said that B is not statistically significant and there is no relationship between “Continue doing Business” and
“Income”.
ANOVA
Model
Sum of
Squares
df
Mean
Square
F Sig.
1
Regression 23.833 4 5.958 13.348 .000a
Residual 20.087 45 .446
Total 43.920 49
a. Predictors: (Constant), Income, Quality_ service, Resolved Problem, Price
b. Dependent Variable: Continuing usage
 F Value
“F” is the test statistic which shows the effect of multiple variables at the same time; that is the joint significance of β1, β2, β3 and β4.
In this case-
o Hο: β1=β2=β3=β4=0
o H1: β1≠β2≠β3≠β4≠0
The significant value is “0.000” which means that there is no risk of rejecting the null hypothesis. The null hypothesis can be rejected which
ensures that jointly, dependent variable that is “Continue doing Business” has a high statistically significant relationship with independent
variables alike- “Income”, “Quality_ service”, “Resolved problem” and “Price”.

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Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .737a
.543 .502 .66811
a. Predictors: (Constant), Income, Quality_ service,
Resolved Problem, Price
 R Square
“R square” is the major decisive value of regression scrutiny which imitates the generally illustrative supremacy of the regression form and
signifies whether to accept the regression model or not. However, it also connotes to what extent deviation in dependent variable is explained
by independent variables.
Here the value of R Square is “0.543” which represents that only 54.3% variation is “Continue doing business” is explained by “Income”,
“Quality_ service”, “Resolved problem” and “Price” since “Continue doing business” is determined not only by these variables but by many
other variables.
Hence it can be said that the overall explanatory power of the regression model is not good.
 Adjusted R Square
For multiple regression equation, there is a penalty for additional independent variable. Adjusted R Square is the adjusted penalty for additional
independent variable.
The more independent variables the greater the overall explanatory power of the regression model but lower the statistical significance of the
model.
Here, the value of Adjusted R Square is “0.502” which mean that the penalty for additional independent variables is very high.
Thus, it can be said that the overall regression model is not good.