Descriptive and Inferential Statistics for Strategic Business Decision-Making
Overview
Business data determination defines the evaluation of the data parameters that are applicable for the execution. This describes the functional evaluation of the data parts which are applicable for the determination. The executional evaluation defines the functional section which is applicable for the determination of the evaluation. The execution approach defines the implementation of descriptive determination which is applicable for the execution. On the other hand, the inferential determination defines the functional section of the implementation of the model/prototype construction such as linear regression. The determining approach derives the functional elements which are useful for the determination of the parameter. The execution approach also provides the functional evaluation of the data-determining approaches that are usable for the determination. The business data execution defines the analytical approach to understand the functional determining factors. In this case, the air quality data is implemented to check the air quality data. This assists in determining the air quality checking factors with respect to month and day. This defines the involvement of Ozone, Temperature, and other factors.
Aim
The aim of the study describes the impact of the inferential, and descriptive statistic determination on the decision-making approaches of a business which assist in business strategical planning.
Objective
To construct the statistical determination approach to determine the decision-making approaches for the strategic business plan.
To evaluate the impact of the statistical determination process that is applicable to evaluate the decision-making approach for a business.
To evaluate the determination of the impact of Descriptive and Inferential Statistics on the business data evaluation which assists in the decision-making process.
Method
The method defines the execution approach that is implemented for the functional evaluation of overall research. The research approach defines the functional execution which is implemented to derive the functional process of execution. This highlights the data analysis approach by using the R-Software. R-Software tool is applicable to investigate the data that are applicable for the determination. The functional section describes the usability of the elements of the R-coding. This highlights the implementation of the cleaning approach, missing data-finding techniques, and others. This describes the functional determination of the evaluated data portion which highlights the usability of the data execution process with the assistance of statistical determination, and normalization approach. The determination defines the implementation of the linear execution approach. The regression method determines the involvement of inferential statistics. This determines the business data handling strategical planning approaches which assist in the decision-making process. The impact of the business evaluation defines the decision-making approaches for the evaluation.
Figure 1: Libraries and Data Read Section
(Source: R-Studio)
The libraries are the main point of execution portion which are applicable for the determination. This portion derives the usability of the data read functionality. In this case, ‘airquality.csv’ data is read with the assistance of the R-coding.
Figure 2: Structure of the data
(Source: R-Studio)
The structure of the data provides information on the necessary data parameters such as ozone values, wind data values, temperature, and solar values. This also highlights the month, and day data which is usable for the month, and day-wise execution of the data parameters. The functional part provides information about the application of the data factors that are usable for the determination (Özemre, and Kabadurmus, 2020). This provides the information about the application of the functional evaluation that is necessary for the evaluation.
Figure 3: Check Null/Blank
(Source: R-Studio)
The checking of blank/null is applicable to check the number of null/blank data present in each column of the data. As per the execution, the columns, ‘Ozone’, and ‘Solar.R’ contain 37, and 7 null/blank values.
Figure 4: Clean data
(Source: R-Studio)
The cleaning approach is applicable to remove those null/blank data sections. This describes the functional determination of the ‘is.na’ checking approach. The replace approach is implemented to replace the null/blank portion with the mean value of those data parameters. After execution of the null removal approach, there are no blank/null values present in the final data section.
Figure 5: Structure of cleaned data
(Source: R-Studio)
The final structure of the cleaned data is evaluated at this point of execution. This derives the functional data values that are usable for the execution. This cleaned data is applicable for the determination of the necessary data values.
Figure 6: Data summary
(Source: R-Studio)
The summary of the data provides the information of the min, 1st qu, median, 3rd qu, mean, and max value of each data column. This describes the evaluation of each data part which are applicable for the execution (Rahimnia, and Molavi, 2021). The functional part provides information about the handling of the data.
Figure 7: Setting of outliers
(Source: R-Studio)
The outliner setting approach is implemented on the ‘Ozone’ data column. This defines the setting of the lower, and upper limit of the data. The outcome determines the execution of the Ozone outliers with the assistance of the upper, and lower levels of the ozone zone.
Figure 8: Handle of outlier
(Source: R-Studio)
The handling approach of the outlier defines the evaluation of the data factors with the help of lower, and upper ozone levels. The functional part also provides the changing of the final data frame with the new outlier for the Ozone column data.
Figure 9: Descriptive determination
(Source: R-Studio)
The descriptive determination defines the construction of the statistical functionality. This describes the construction of the statistical value determination approach which highlights the mean, 1st qu, meidan, 3rd qu, std dev, range, max, variance, and min value. This function is applicable to the overall data.
Figure 10: Statistical Determination
(Source: R-Studio)
The statistical determination functionality is implemented on the four columns such as ‘Ozone’, ‘Solar’, ‘Temp’, and ‘Wind’. This describes the determination of the evaluated parameters of those data factors.
Figure 11: Statistical Outcome
(Source: R-Studio)
The statistical outcome defines the details of the evaluated value points. As per the execution, the mean value of ozone is 39.85807, solar is 185.93151, wind is 9.957516, and temperature is 77.88235. As per execution, the minimum values of ozone, solar, wind, and temperature are 1, 7, 1.7, and 56 respectively. On the other hand, the maximum values of ozone, solar, wind, and temperature are 83.5, 334, 20.7, and 97. The std dev values for ozone, solar, wind, and temperature are 22.97445, 87.96027, 3.523001, and 9.46527.
Figure 12: Normalization of the data
(Source: R-Studio)
The normalization approach is implemented to make a normalization of the data columns. This highlights the construction of a new data frame that contains the normalized data form which is implemented for the determination (Albejaidi et al., 2020).
Figure 13: Normalized data structure
(Source: R-Studio)
The normalized data structure is evaluated at this point of action. This highlights the change in the data structure due to the implementation of the normalization process. This describes the changes in the main data structure.
Figure 14: Correlation Evaluation
(Source: R-Studio)
The correlation evaluated the determination of the correlation value between various data columns. The data columns are implemented for the execution of the data. The plot describes the heat map section which highlights this correlation data values.
Figure 15: Heatmap (Correlation plot)
(Source: R-Studio)
The correlation defines the plot of the heat map which highlights the value of 1 between two similar data columns. The other columns highlight the relation between various elements. In this case, the Ozone-Solar.R correlation value is 0.32. The Ozone-wind is -0.52, whereas the Ozone-Temp value is 0.67. The Solar.R-Wind defines the correlation whose value is -0.06, and Solar.R-Temp is 0.26. The value between Win-Temp is -0.46.
Figure 16: Bar plot 1
(Source: R-Studio)
The visualization plot determines the plot of the bar which displays the relation between ozone level value, and monthly data. The month-wise execution of the type of the relational determination (Eneh, 2022).
Figure 17: Month-wise Ozone level determination
(Source: R-Studio)
The executable approach determines the month-wise ozone level determination. As per the plot, the maximum ozone level is found in the month of ‘7’ (July) which is around 53.
Figure 18: Bar plot 2
(Source: R-Studio)
The box plot defines the plot of the distributed data values. This defines the usability of the bar design which assists in the visualization approach. This highlights the relation between month-wise temperature determination.
Figure 19: Month-wise Temperature level determination
(Source: R-Studio)
The month-wise execution of the temperature level is evaluated in this point of visualization. As per the execution, the maximum temperature is determined in the month of ‘8’ (‘August’), which is around 83.97.
Figure 20: Normalized distribution of data
(Source: R-Studio)
The normalized data plot also defines the construction of the histogram distribution data plot. This provides the plot of all the data columns.
Figure 21: Normalized Box plot of the data
(Source: R-Studio)
The second plot of determination describes the evaluation of the box plot. This determines the function of the bar plot for those attributes,
Figure 22: Normalized scatter plot of the data
(Source: R-Studio)
The distributed scatter plot for the collected data is evaluated in this visualization point. This demonstrates the implementation of the relational evaluation between various data elements. The first plot defines the relational execution between ozone-temperature. It also highlights the solar.r-temperature, wind-ozone, and wind-temperature. The distributed plot determines there is no good correlation between those elements.
The inferential statistical determination defines the implementation of the data analysis model/prototype. This describes the functional sections that are applicable for the determination. This defines the implementation of the testing approaches such as ANOVA, T-test, Linear Regression, and so on. In this section, the Linear Regression is applicable for the determination of the inferential statistics (Weerasekara, and Bhanugopan, 2023). This describes the determination of the executable parameters which is applicable for the execution.
Figure 23: LR model execution
(Source: R-Studio)
The model/prototype defines the configuration of the linear model/prototype. This describes the construction approach for the LR prototype. In this case, the dependent and independent variables are set for the execution. The execution is evaluated for the ozone with respect to temperature, wind, and solar.r. The summary of the model/prototype describes the necessary parameter values that are implemented for the execution of the determination. The determination also describes the evaluated value points that are applicable for the execution of the data factors (Ontita, and Kinyua, 2020). The evaluated data part provides the necessary factors that are usable for the determination of the execution.
Figure 24: Summary of the prototype/model
(Source: R-Studio)
The summary of the prototype/model describes the evaluated data value point. This highlights the residual value which evaluates the min 1st qu, median, 3rd qu, and max value. In this case, the min value is -33.741, the median is -2.518, 1st qu is -12.961, 3rd que is 11.723, and the max value is 45.186. The coefficients provide the details of the p-value for temperature, wind, and solar.r. As per the execution the p-value for the determination of < 2.2e-16. The F-statistic value is 57.76. The significant value is determined in the case of executable parameters. This describes the functional part of the execution.
Through conducting this study, it has been concluded that both descriptive and inferential statistics play vital role in informing business decisions, on the other hand, it mainly impacts decision-making approaches in different ways. It has been recognized that descriptive statistics mainly help this business to understand the characteristics and also patterns within a specific data set. This process mainly involves measures such as central tendency, variability, and distribution. Also identified that descriptive statistics is mainly used to analyze financial performance, assess profitability, and make data-driven decisions about resource and investment allocation. When utilized together, descriptive and inferential statistics deliver a comprehensive knowledge of data, enabling companies to make more knowledgeable and data-driven decisions. Descriptive statistics deliver the foundation for comprehending what has transpired, while inferential statistics allow businesses to anticipate future “challenges and opportunities”. The overall evaluation provides the evaluation of the air quality checking data which highlights the implementation of the statistical determination process. The execution provides the details of the constructive data model that is applicable for the business data evaluation. This assists in the decision-making process.
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Eneh, E.O., 2022. Effect of employees involvement in management decision making on organizational efficiency of pharmaceutical manufacturing firms in Enugu state. European Journal of Marketing and Management Sciences, 5(2), pp.23-35.
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Özemre, M. and Kabadurmus, O., 2020. A big data analytics based methodology for strategic decision making. Journal of Enterprise Information Management, 33(6), pp.1467-1490.
Rahimnia, F. and Molavi, H., 2021. A model for examining the effects of communication on innovation performance: emphasis on the intermediary role of strategic decision-making speed. European Journal of Innovation Management, 24(3), pp.1035-1056.
Weerasekara, S. and Bhanugopan, R., 2023. The impact of entrepreneurs’ decision-making style on SMEs’ financial performance. Journal of Entrepreneurship in Emerging Economies, 15(5), pp.861-884.
Figure: Correlation Matrix
Figure: Month-wise Ozone data table
Figure: Month-wise Temperature data table
