Business Statistics: Analysis and Interpretation

This report explores the use of statistical tools in business decision making and hypothesis testing. It analyzes the relationship between temperature, precipitation, and per capita GDP to understand the impact of climate on poverty and economic growth. The findings suggest that hot countries do not necessarily tend to be poor, and there is no significant correlation between temperature or precipitation and GDP.

BUSINESS STATISTICS
INDIVIDUAL PROJECT

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
ANALYSIS AND INTERPRETATION.........................................................................................3
1. Globe becoming warmer over 1950- 2000..............................................................................3
2. Hot countries tend to be poor and presenting two relationships.............................................4
3. Calculation of sample covariance and correlation for two relationships identifying above
two relations................................................................................................................................5
4. Simple regression to explore relationship among...................................................................6
5. Multiple regression for exploring relation between Annual growth of per capita GDP and
mean temperature over 1990- 2000 and mean precipitation over 1990- 2000...........................8
6. Illustration of...........................................................................................................................9
7. Interpreting the adjusted R- squared in the multiple regression analysis................................9
8. Illustrating two other variables and reason for choosing them...............................................9
CONCLUSION................................................................................................................................9
REFERENCES..............................................................................................................................11

INTRODUCTION
The business statistics is being defined as the intelligent decision making which the
business undertakes with the help of the different data and numeric facts and figures. For the
effective decision making of the business it is very useful for the company to have undertaken
the use of different statistics. The current report will outline the use of different statistical tools in
order to meet the objective or to prove the hypothesis. For this the use of different statistical tools
like the correlation, covariance, regression that is both linear and multiple regression will be used
in order to meet the objectives of the business.
ANALYSIS AND INTERPRETATION
1. Globe becoming warmer over 1950- 2000
tem1950_1960 tem1990_2000 pre1950_1960 pre1990_2000
Mean 19.46 19.90 13.01 11.72
Standard Error 0.68 0.68 0.75 0.67
Median 21.56 22.31 11.07 10.64
Mode #N/A #N/A #N/A #N/A
Standard Deviation 6.98 6.98 7.77 6.88
Sample Variance 48.68 48.76 60.37 47.40
Kurtosis -0.77 -0.79 0.54 1.71
Skewness -0.70 -0.69 0.91 1.01
Range 25.11 26.41 36.89 39.20
Minimum 2.97 2.47 0.36 0.67
Maximum 28.08 28.87 37.25 39.87
Sum 2063.01 2109.65 1379.08 1242.78
Count 106 106 106 106
With the help of the above interpretation of the descriptive statistics it is clear that the
globe is becoming warmer and drier with the passage of time (Lind, Marchal and Wathen, 2019).
With the analysis of the above data it is clear that the mean warmer within the time frame of
1950 to 1960 was 19.46 temperatures. In addition to this for the year 1990 to 2000 the average
warmth was 19.90. In addition to this the maximum temperature within 1950- 60 was 28.0 8
where is in 1990- 2000 it was 28.8 7. In case of the dryer global it was analysed that the mean
dryness in the year 1950 360 was 13.01. Whereas in the year 1990- 2000 the trial was 11.7 to. In

addition to this the maximum run as which the countries have faced during the time frame of
1950- 60 first 30 7.25 where as in 1990- 2000 it was 30 9.87.
2. Hot countries tend to be poor and presenting two relationships
country per_cap_GDP2000 tem1990_2000 pre1990_2000
Sierra Leone 4.932303899 26.19973 25.13283
Ghana 5.554783345 26.67841 12.75037
Togo 5.711110209 26.32782 11.59565
Rwanda 5.563669533 20.2585 10.81682
Mali 5.597944577 28.47546 6.926775
Uganda 5.56784437 22.31762 11.9865
Laos 5.784400396 23.60845 17.78357
Guinea-Bissau 5.730567593 26.88505 14.93492
Nepal 5.435860707 20.37417 14.7353
Burkina Faso 5.540155202 27.9637 8.097651
Guinea 5.895731095 25.37372 19.88388
Madagascar 5.682242675 21.05817 14.66957
Kenya 5.985151307 20.06081 12.29716
Niger 5.284935254 28.34429 4.570006
Malawi 5.052325511 23.08399 10.28716
Central African Republic 5.526276812 24.49559 14.09322
Chad 5.110558976 28.09092 6.980453
Mozambique 5.766317693 24.48417 9.191278
Cambodia 5.712336331 27.78295 12.87399

With the evaluation of the above data and chart it is clear that there are many countries
which are poor and are hot as well as drive (Williams, 2020). The major hypotheses under this
were that whether the hot countries tend to be poor or not. In addition to this another hypothesis
was that do dry countries tend to be poor or not. Hence with the evaluation of the above table and
chat it was evaluated that the list of countries poor are not much hot. In addition to this it was
also evaluated that the poor countries are not even much dryer. With the descriptive statistics it
was evaluated that the minimum hotness is 2.97. But the poor countries are having more than
that temperature which means that they are not much hot. In the similar manner the dryness that
is precipitation the minimum version 0.67 but in comparison to the poor countries the person
petition was not up to 0.67. This simply means that there is no relationship between the facts that
hot countries tend to be poor or the drive countries tend to be poor.
3. Calculation of sample covariance and correlation for two relationships identifying above two
relations
Correlation between hot countries tend to be poor
per_cap_GDP2000 tem1990_2000
per_cap_GDP2000 1
tem1990_2000 -0.23 1

By evaluating the correlation between the hot countries tend to before it can be seen that
the correlation among them temperature and the per capita GDP is in negative. This simply
means that there is no correlation between both these factors. The reason and the like this factor
is that the GDP is not decided by the temperature (Alaqil and Lugo-Ocando, 2021). Hence it can
be stated that it is not necessary that the hot countries tend to be poor.
Correlation between dry countries tend to be poor
per_cap_GDP2000 pre1990_2000
per_cap_GDP2000 1
pre1990_2000 0.037 1
Furthermore with evaluation of the correlation between the drive countries tend to be
poor it was evaluated that the correlation is 0.037. This simply means that the correlation among
both the variable that is dry country and tends to be poor is very low. This is particularly because
of the reason that the dryness within the weather does not affect the country to be poor or rich.
Hence the correlation is very low (Doane, Seward and Chowdhury, 2020).
Covariance
per_cap_GDP2000 pre1990_2000 tem1990_2000
per_cap_GDP2000 0.075
pre1990_2000 0.048 22.303
tem1990_2000 -0.181 -3.349 8.194
The covariance is a measure of directional relationship between the returns of the two
variables. Hence this covariance outlines that whether there is a direct relationship between the
two variables or not. With the analysis of the covariance applied over all the variables that is per
capita GDP temperature and precipitation it was analysed that there is not much direct
relationship between all the three variables (Mariappan, 2019). The reason underlying this
factors that the GDP of the country is not dependent over the climatic condition that is within the
temperature is high or not for the precipitation is high or low. This is particularly because of the
reason that the GDP of the countries dependent over the businesses and the economic condition.
4. Simple regression to explore relationship among
Annual growth of per capita GDP and mean temperature over 1990- 2000
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 Vari-
able 1 0 0 65535 #NUM! 0 0 0 0
With the application of the regression analysis over the two variables that is annual
growth of per capita GDP and the name temperature it was analysed that there is no significant
relationship between both these variables. The reason underline this fact is that the significance
value of this was 1 which is greater than the standard value of 0.05. Hence when the value is
greater than the significant value then the null hypothesis is been selected that is there is no
significant relationship between the per capita GDP and the mean temperature.
Annual growth of per capita GDP and mean precipitation over 1990- 2000
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 Er-
ror 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
Similarly in case of theme annual growth rate of per capita GDP and the mean
precipitation it was evaluated that the significance value is one which is greater than the standard
p value. Hence the null hypothesis is being accepted rejecting the alternate which states that there
is no relationship (López-Tamayo and Pérez Marín, 2019).
5. Multiple regression for exploring relation between Annual growth of per capita GDP and
mean temperature over 1990- 2000 and mean precipitation over 1990- 2000
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 Er-
ror 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
In the similar manner with help of the multiple regressions for exploring the relation
between annual growth of per capita GDP with mean temperature and main precipitation it was
evaluated that there is no relationship among these three variables. The reason underlying this

fact is that the significance value was one which is greater than the p value of 0.05. Hence this
simply means that all the three variables have no significant relationship between them because
the GDP is not dependent over the mean temperature of the mean precipitation (Opstad, 2020).
6. Illustration of
Why above hypothesis is required in above regression analysis
The above hypothesis is required for the regression analysis because the hypotheses is the
base on which the whole regression analysis tool is based. Without the hypotheses the researcher
will not have any idea that how they have to conduct the research and what is the objective of the
study.
Underlying mechanism of two tailed hypothesis
The two tailed hypothesis test is designed in order to highlight whether the sample mean
is significantly greater than and less than the mean population. This tested gets its name because
that underlies both the sides of the normal distribution.
7. Interpreting the adjusted R- squared in the multiple regression analysis
The lowered adjusted R square indicates the fact that the additional input variables are
not adding any value to the model. On the other hand the higher adjusted R square indicates that
the additional input variables are also adding some value to the model. With respect to the
current calculation it was viewed that the adjusted R square was a negative which simply means
that the additional variables are not adding any value to the whole regression model.
8. Illustrating two other variables and reason for choosing them
The other two factors that can influence the annual growth of per capita GDP taken for
the study can be poverty and human capital. The reason underlying this fact is that these
variables can affect the GDP and both negative and positive (Kitanovski, 2018). Hence the use of
these variables will definitely outline proper and effective analysis of the influence of annual
growth of per capita GDP.
CONCLUSION
In the end it is evaluated that business statistics is very essential for the effective business
decision making. The reason underlying this fact is that the data and numeric information
provides a more logical base in order to take effective business decisions. Hence the use of
different tools like regression and correlation etc. is helpful in taking business decisions.

REFERENCES
Books and Journals
Alaqil, F. and Lugo-Ocando, J., 2021. Using Statistics in Business and Financial News in the
Arabian Gulf: Between Normative Journalistic Professional Aspirations and
‘Real’Practice. Journalism Practice, pp.1-24.
Doane, D.P., Seward, L.E. and Chowdhury, S., 2020. Applied Statistics in Business and
Economics| | SIE. McGraw-Hill Education.
Kitanovski, M., 2018. Study of Business Communication Algorithms, ervice Scenario Design
and Data Valuation Statistics (Innovation, Business Model). Rocznik Administracji
Publicznej, (4), pp.243-254.
Lind, D.A., Marchal, W.G. and Wathen, S.A., 2019. Basic statistics for business and economics.
McGraw-Hill.
López-Tamayo, J. and Pérez Marín, A.M., 2019. Computing Practices in Statistics I and
Statistics II Business and Management Degree Faculty of Economy and Business
University of Barcelona.
Mariappan, P., 2019. Statistics for Business.
Opstad, L., 2020. Attitudes towards Statistics among Business Students: Do Gender,
Mathematical Skills and Personal Traits Matter?. Sustainability, 12(15), p.6104.
Williams, T.A., 2020. Statistics for business and economics. Cengage Learning.