Quantitative Skills for Business: Data Analysis Report

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Quantitative skills for business
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Table of Contents
Description of data.........................................................................................................................3
Data analysis...................................................................................................................................3
References.......................................................................................................................................7
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Description of data
Three variables have been taken in this report and accordingly have discusses throughout the
report. It can be said that the main purpose of the report is to identify any relationship that exist
between these three variables and if any relationship do exist to determine the nature and extent
of relationship.
For the purpose of better outcome or result, three variables have been taken into consideration.
Out of these variables, two of the variables are independent and the remaining variable is
dependent (Harding, et. al., 2014).
Impact of each independent variable has been determined on the dependent variable so that an
appropriate conclusion can be obtained therefrom. For the purpose of data analysis and
interpretation, regression analysis has been performed.
Data analysis
The units of all the three variables are same and akin. In other words, all these concerned
variables have been expressed in currency. However, the independent variable, namely, ‘real
personal income’ is in million dollars and the other remaining variables are in billion dollars.
Due to such significant difference in denomination, there will be significant variation or impact
on the measures of central tendency along with different measures of descriptive statistics. All
these variables pertain to the 10 years, i.e., 2008 – 17. Such longer duration has been taken into
consideration so that impact of independent variable on dependent variable can be better
understood.
Real personal income (millions of
chained (2012) dollars)
Mean 14179340.22
Standard Error 335738.70
Median 13992781.80
Mode #N/A
Standard
Deviation
1061698.99
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Sample Variance 1127204740299.56
Kurtosis -1.28
Skewness 0.36
Range 3049488.30
Minimum 12807725.40
Maximum 15857213.70
Sum 141793402.20
Count 10.00
1
2
3
4
5
6
7
8
9
10
0 10000 20000 30000 40000 50000 60000 70000
Current receipts (Billion of dollars)
Fixed assets and consumer durable goods (Billion of dollars)
Fixed assets and consumer
durable goods (Billion of
dollars)
Mean 55986.00
Standard Error 1708.22
Median 55182.90
Mode #N/A
Standard
Deviation
5401.87
Sample Variance 29180235.9
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Kurtosis -1.17
Skewness 0.44
Range 15357.70
Minimum 49751.40
Maximum 65109.10
Sum 559860.00
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Count 10.00
Current receipts (Billion of
dollars)
Mean 4619.18
Standard Error 206.54
Median 4573.35
Mode #N/A
Standard
Deviation
653.13
Sample Variance 426573.5
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Kurtosis -1.79
Skewness 0.01
Range 1785.10
Minimum 3696.60
Maximum 5481.70
Sum 46191.80
Count 10.00
The above three tables presents the descriptive statistics of all the given variables. Mode of all
the variables cannot be computed or determined. However, mean and median of all the variables
can be easily determined (Loeb & McFarland, 2017).
It is difficult to state the nature or characteristics in one statement. In the case of ‘fixed case and
consumer durable goods’, there is slight difference between mean and median. On the other
hand, variable, ‘real personal income’, there is wide fluctuation between mean and median. Thus,
it is better to state that all the three variables are performing in the different direction
independently.
SUMMARY OUTPUT of real personal
income and fixed assets consumer
durable goods
Regression Statistics
Multiple R 0.99
R Square 0.97
Adjusted R Square -1.25
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Standard Error 919.43
Observations 1.00
ANOVA
df SS MS F Significance F
Regression 10.00 255859354.28 25585935.43 302.67 #NUM!
Residual 8.00 6762769.34 845346.17
Total 18.00 262622123.62
The above table aims to explain the regression analysis of two variables. R square of real
personal income and fixed assets consumer durable goods is almost equal to 1. This has led to
higher value of F statistics indicating that there is a strong relationship between personal income
and fixed assets goods intended for consumers. As such, increase in real personal income will
increase the expenditure towards the fixed assets consumer durable goods.
SUMMARY OUTPUT of fixed assets
consumer durable goods and current
receipts
Regression Statistics
Multiple R 0.97
R Square 0.95
Adjusted R Square -1.25
Standard Error 1286.70
Observations 1.00
ANOVA
df SS MS F Significance F
Regression 10.00 249377314.18 24937731.42 150.63 #NUM!
Residual 8.00 13244809.44 1655601.18
Total 18.00 262622123.62
The R square value of fixed assets consumer durable goods and current receipts is also equal to
1. In other words, there is a positive relationship between current receipts and fixed assets
consumer durable goods and so is the case with real personal income and fixed asset consumer
durable goods.
Intercept between real person income and fixed
assets and consumer durable goods
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-15222.86
Intercept between current receipts and fixed assets
and consumer durable goods
18757.54
An attempt has been made towards the determination of intercept between the real person
income and fixed assets and consumer durable goods. In the same manner the intercept between
the current receipts and fixed assets and consumer durable goods. While the intercept between
the real personal income and fixed assets and consumer durable goods comes out to be negative
value, the intercept between the current receipts and fixed assets and consumer durable goods
(Waltenburg, 2012).
However, the both the independent variables are positively related to the dependent variable and
as such will impact the dependent variable in the same direction. It is important to note that the
proportion of increase in dependent variable due to increase in independent variable will not
remain the same.
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References
 Harding, B., Tremblay, C., Cousineau, D., 2014. Standard Errors. School of Psychology,
Universitéd’ Ottawa, vol. 10, no. 2, 1-17.
 Loeb, S., McFarland, D., 2017. Descriptive analysis in education: A guide for researchers.
Institute of Education Sciences. 12(7). Pp. 1-40.
 Waltenburg, E., 2012. Exploratory Data Analysis: A Primer for Undergraduates. Department of
Political Science Faculty Publications. 6(4).Pp.1-69.
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