Business Statistics Report

This Business Statistics report analyzes the performance of a business entity using various statistical tools. It includes an analysis of warmer and drier globe, hot countries and dry countries, sample covariance and correlation, simple regression, multiple regression, and more.

BUSINESS STATISTICS
REPORT

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
1.) Analysis of warmer and drier globe on the basis of Descriptive Statistics.....................3
2.) Analysis of hot countries and dry countries with growth rate to identify their poor
category.......................................................................................................................................4
. 3. Calculating the sample covariance and correlation of the two relationships identified
in above question........................................................................................................................7
. 4. Simple regression to explore relation between the following...........................................8
. 5. Multiple regression..............................................................................................................9
. 6. Illustrating the below points-............................................................................................10
7.) Analysis of R-squared in the multiple regression............................................................11
8.) Two variables that influence the annual growth of per capita GDP.............................11
CONCLUSION..............................................................................................................................12
REFERENCES................................................................................................................................1

INTRODUCTION
Business statistics report is the projection about various records and statistics that can
provide an overview about the performance of the business entity. This project is all about
analysing and interpreting the information in such way that overall performance assessment
could have been done. This is demonstrated that the different tools such as mean, mode, medium
and such other parameter are assessed to understand the overall performance of the company.
Analysis about the different factors related to GDP growth rate and many such would be done so
that better assessment could have been projected under this project. The review will be done over
the statistics so that proper judgement could have been provided to understand about the
significance of the data and statistics.
MAIN BODY
1.) Analysis of warmer and drier globe on the basis of Descriptive Statistics
Particulars tem1950_1960 pre1950_1960 tem1990_2000 pre1990_2000
Mean 19.46235443 13.0102116 19.90237043 11.72430008
Mode #N/A #N/A #N/A #N/A
Median 21.56305 11.074925 22.31359 10.63715
Sum 2063.00957 1379.082429 2109.651266 1242.775808
Count Number 106 106 106 106
Minimum 2.96588 0.3591493 2.467539 0.6710021
Maximum 28.07941 37.24714 28.8736 39.86776
Interpretations:
The above tools of central tendency indicate that the globe is warmer than the drier
because of the high mean temperature of the countries. The average hot temperature of the mean
is 19.46 for the period of 1950 to 1960 and 19.90 for the period of 1990 to 2000. The mean of the
hot and dry temperature of the globe is indicating 13.01 and 11.72 temperature in a period of
1950-1960 and 1990-2000 respectively. While on the other hand, the maximum hot temperature
for the period of 1950 to 1960 and 1990 to 2000 is 27.07 and 28.87 respectively. The minimum

tool of the data statistics reflects that the dry temperature of the globe between the period 1950 to
1960 is .03 and between 1990 to 2000 is 0.67. The above analysis interpretate that the globe is
get warm and dry over the period of time but most of the time globe remains warmers (Khan-
Malek and Wang, 2017).
2.) Analysis of hot countries and dry countries with growth rate to identify their poor
category
Hot Country tem1990_2000 growth1990_2000
Nigeria 26.85172 0.017646708
Panama 25.11478 3.343584973
United Arab Emirates 26.91433 0.351553339
Sierra Leone 26.19973 2.487687259
Brunei 27.06742 2.300388065
Ghana 26.67841 0.582056958
Cote d'Ivoire 26.26308 1.483790792
India 25.26296 8.655843283
Suriname 26.69649 1.356308293
Senegal 27.44442 -0.034229888
Togo 26.32782 -0.437058263
Philippines 25.64058 1.899129864
Mali 28.47546 1.460227831
Oman 25.76736 2.050550258
Mauritania 28.8736 2.508794623
Trinidad and Tobago 26.05961 0.486085514
Indonesia 25.76939 1.171336005
Belize 25.85874 0.422032291
Cuba 25.33854 2.135885605
Dominican Republic 25.66094 -2.469349752
Nicaragua 26.53195 -0.428450843
Assumption: It is assumed that the average category of hot countries is above 25 temperature
and average category of dry country is below 6 temperatures.
Hot countries relation with its growth rate

Dry Country pre1990_2000 growth1990_2000
United Arab Emirates 1.263214 0.351553339
Spain 6.297207 2.487687259
Austria 9.221741 2.300388065
Canada 9.385453 0.582056958
Australia 7.964385 2.135885605
Egypt 0.6710021 2.512322923
Turkey 6.115713 2.602163169
Saudi Arabia 0.8200489 1.22463352
Luxembourg 7.092182 3.626749501
Tunisia 4.316459 3.286240995
Mozambique 9.191278 2.790436047
Poland 6.146651 3.769896707
Dry countries relation with its growth rate

Interpretation:
From the data and graph of the relation between the hot countries and its growth rate it is
clearly indicatable that the above listed hot countries are tend to be poor. It is because the hot
countries growth rate is not too much high. But on the other hand, there is many countries such
as Nigeria that temperature is above 26 but their growth rate is below nil. The number of hot
countries having high growth rate is minimum and less as compared to the hot countries having
low growth rate. So, from this analysis it is clear that the hot countries are tend to be poor with
lower growth rate and also per capita GDP (Alonso and et.al., 2019).
From the above data and graph reflecting the relation between the dry countries and its
growth rate indicate that the above listed dry countries are tend to be poor. As from the above
data such as UAE country having 1.29 temperature with the growth rate of below zero and on the
other hand the Luxembourg country having the below average temperature i.e., 7.09 but their
growth rate is 3.28. But the number of dry countries having poor growth rate is high than the
number of dry countries having better growth rate. So, from the above analysis it is interpretate
that it is not necessary that all dry countries tend to be poor but between the period of 1990 to
2000 maximum dry countries are tend to be poor.

.3. Calculating the sample covariance and correlation of the two relationships identified in
above question
.Correlation and covariance between hot countries tend to be poor
.correlation
tem1990_2000 growth1990_2000
tem1990_2000 1
growth1990_2000 -0.198878299 1
Covariance
tem1990_2000 growth1990_2000
tem1990_2000 0.908164905
growth1990_2000 -0.39267131 4.292584327
With the correlation between the hot countries tend to be poor it was evaluated that correlation is
a negative which simply means that there is no relation between both these variables. In addition
to this, in logical sense as well there is not any correlation between both these variables because
the hotness of the country has nothing to do with the GDP of the country.
.Correlation and covariance between dry countries tend to be poor
Correlation
pre1990_2000 growth1990_2000
pre1990_2000 1
growth1990_2000 0.234407722 1
Covariance
pre1990_2000 growth1990_2000
pre1990_2000 9.722684819
growth1990_2000 0.767642507 1.103031703
Further with the valuation of the covariance and correlation between the drive countries tend to
be poor the correlation is 0.23 which simply means that the correlation between both this

variable is very low. Hence it can be stated that the dry country and the country being poor has
no correlation at all.
.4. Simple regression to explore relation between the following
Growth of per capita GDP with mean temperature
Regression Statistics
Multiple R 65535
R Square -2.06647E-16
Adjusted R Square -0.00952381
Standard Error 2.289001528
Observations 106
ANOVA
df SS MS F
Significance
F
Regression 1
-1.13687E-
13
-1.13687E-
13 0 1
Residual 105 550.1504397 5.239527997
Total 106 550.1504397
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 1.403 0.222 6.309 0.000 0.962 1.843 0.962 1.843
X
Variable
1 0 0 65535 #NUM! 0 0 0 0
With the analysis of the regression between the growths of per capita GDP with mean
temperature it was identified that there is no significant relationship between both the variables.
The reason underlying this path is that the significance value is one which is greater than 0.05
and this means that the null hypothesis is being accepted rejecting the alternate hypothesis.
Growth of per capita GDP with mean precipitation

Regression Statistics
Multiple R 65535
R Square -2.06647E-16
Adjusted R Square -0.00952381
Standard Error 2.289001528
Observations 106
ANOVA
df SS MS F
Significance
F
Regression 1 -1.1E-13 -1.1E-13 0 1
Residual 105 550.150 5.2395
Total 106 550.150
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
0.22 6.31 0.00 0.96 1.84 0.96 1.84
0 65535 0 0 0 0
Further with the valuation of the regression analysis of growth of per capita GDP with the mean
precipitation it was evaluated that there is no significant relationship between these variables
(Skordi and Fraser, 2019). The reason underlying this fact is that the significance value of one
does not meet the standard criteria of significance value to be 0.05. Hence there is no significant
relationship between precipitation and the growth of per capita GDP.
.5. Multiple regression
Regression Statistics
Multiple R 65535
R Square -2.1E-16
Adjusted R Square -1.9E-02
Standard Error 2.3E+00
Observations 106

.
ANOVA
df SS MS F
Significance
F
Regression 2 -1.1E-13 -5.7E-14 0 1
Residual 105 550.150 5.240
Total 107 550.150
.
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
0.222 6.309 0.000 0.962 1.843 0.962 1.843
0 65535 #NUM! 0 0 0 0
0 65535 #NUM! 0 0 0 0
With the evaluation of the multiple regressions applied over per capita GDP temperature and
precipitation it was evaluated that there is no significant relationship among these variables
(Nielsen, 2018). Logically as well there is no relationship between both the three variables
because the temperature or precipitation does not affect the GDP of the country. In addition to
this statistical analysis also there is no relationship between these three factors as the significance
value is 1. This does not meet the standard criteria of P value being 0.05 which means that the
null hypothesis is accepted rejecting the alternate. This simply means that is the p value will be
less than 0.05 then only it can be stated that there is relationship present between both these
variables.
.6. Illustrating the below points-
The reason why the hypothesis test is required in the above regression analysis
For undertaking any of the statistical application of the truly it is very essential that the
hypotheses are being set effectively. This part is that the hypotheses are the basis on which the
whole of the calculation is based (Black, 2019). The reason underlying the fact is that is the
hypothesis is set on aim which needs to be proved. Hence the statistical tools are being applied in
order to prove the aim of the study. Like in the present study the aim is to find that do hot

countries tend to be poor or do drive countries tend to be poor. Hence the hypotheses are being
set in accordance to the objective which needs to be finding out.
The underlying mechanism of the two tailed hypotheses in context of the regression
analysis
The two tailed hypothesis test is a type of test which is designed to show that whether the
sample mean is significantly greater than or significantly less than the mean of the total
population. This is a two tailed test as it test the area under both tales of the normal distribution
for stock this simply means that the both the sides are one side the sample mean and the other
side the population mean and it is compared with help of two tailed hypothesis (Liu, 2017). In
addition to this a two tailed test is a type of statistical procedure which is used in order to
compare the null hypothesis against the alternative hypothesis. The evidence relating to the null
hypothesis is being obtained from the test statistics which is said to be two tailed because its
alternative hypothesis does not specify whether the parameter is greater than or less than the
value specified.
7.) Analysis of R-squared in the multiple regression
R-squared is a statistical variable which indicates the extent to which variation of
dependent variable is explained by the independent variable. Basically, there are two adjusted R-
squared available that is lowered and higher. The lowered squared indicate that the additional
input or variable do not provide any extra value to the regression model. While on the other
hand, the higher squared indicate that the additional value will provide extra value to the
regression model but to some extent only. From the above calculation, it is clear that the R-
square is negative which clearly indicate that the any addition of the variable to not provide any
value to the complete regression model (Lupenko, Lutsyk and Yasniy, 2018).
8.) Two variables that influence the annual growth of per capita GDP
GDP per capita of any country means the respective country’s economic output divided by
its population. It basically indicates the benefits the citizen of a country receives from its
economic growth and also represent the standard living of that country. It helps to know the
purchasing power of the individual of a particular country and indicate how much economic

production is get assigned to each individual and citizen of country. The two variable that
influence the per capita GDP of the country are as follow:
 Unemployment rate: This is a variable which influence the GDP per capita of a country.
This principle indicates that for every two percent increase in employment the one
percent of GDP get increases. So, to improve the GDP of the country, the government
have to put their focus towards the low unemployment rate. And this is only possible
when each individual of the country gets proper education with the help of proper
education system (Rigas, Friedman and Komogortsev, 2018).
 Poverty Line: This is also one of the variables which influences the per capita GDP of a
country. Around 450 million people in a globe lived below poverty line and have no
income source so that they can feed themselves and their family. If the poverty
percentage of a country will high its GDP growth rate will get low. And on the other
hand, if poverty of a country is low then it leads to growth in the per capita GDP of a
country (Liu and et.al., 2019).
CONCLUSION
Data analysis is a statistical practice that is about to interpret and analysis the information
and data to form a valid justification. This process is about to interpret the information in such
manner that valid justifications could have been designed as a part of this entire study. The gdp
demonstrate about the growth rate of the country economic situation. This demonstrate about the
level of economic growth country has entertained against taking up the various decision making
and the policy formation. The statistics are designed that developed countries economic growth
are more stagnant as compare to the developing countries and economies. This can indicate that
the progress of the country gross domestic product is more significant as it certainly demonstrate
the performance advancement of the country.

REFERENCES
Books and journals
Khan-Malek, R. and Wang, Y., 2017. Statistical analysis of quantitative RT-PCR results.
In Drug Safety Evaluation (pp. 281-296). Humana Press, New York, NY.
Alonso, B. and et.al., 2019. Effects of traffic control regulation on Network Macroscopic
Fundamental Diagram: A statistical analysis of real data. Transportation Research Part
A: Policy and Practice. 126. pp.136-151.
Lupenko, S., Lutsyk, N. and Yasniy, O., 2018. Statistical analysis of human heart rhythm with
increased informativeness. Acta mechanica et automatica. 12(4).
Rigas, I., Friedman, L. and Komogortsev, O., 2018. Study of an extensive set of eye movement
features: Extraction methods and statistical analysis. Journal of Eye Movement
Research. 11(1). p.3.
Liu, L. and et.al., 2019. Statistical analysis of zero-inflated nonnegative continuous data: a
review. Statistical Science. 34(2). pp.253-279.
Black, K., 2019. Business statistics: for contemporary decision making. John Wiley & Sons.
Liu, Z., 2017. Teaching reform of business statistics in college and university. EURASIA Journal
of Mathematics, Science and Technology Education, 13(10), pp.6901-6907.
Nielsen, P.B., 2018. The puzzle of measuring global value chains–The business statistics
perspective. International economics, 153, pp.69-79.
Skordi, P. and Fraser, B.J., 2019. Validity and use of the What Is Happening In this Class?
(WIHIC) questionnaire in university business statistics classrooms. Learning
Environments Research, 22(2), pp.275-295.
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