Evaluate the Process of Statistical Application

Business Case for Statistical Purposes 2

Create a Business Case for Statistical Purposes

Lawrence Baiden

Northcentral University

TIM-7101 v1: Statistics with Technology Applications

Dr. Alfred Basta

March 13, 2022

Introduction

The specific organizational problem that I think could be addressed through statistical applications is determining whether the ability to execute or business fitness depends on economic fitness or attractiveness. The standard physical system of business consists of the business attractiveness, conversion, and delivering ability. These three disciplines are very important in the current world of digital business. Economic fitness refers to the country’s ability the production of more complex goods in a globally competitive market. A country’s ability to produce products that have diverse portfolios depends on its level of economic fitness. Starting and running a business in an economy that one is not more familiar with has remained to be a problem to many business people in the world (Khraisha, 2020).

Solving this problem is good because it will help the entrepreneur and business people in making the right decision before starting a business. Businessmen and women will also know the importance of studying economic fitness before investing within that economic block. Solving this problem will bring some sigh of relief to the business people as they will be able to know what they should expect from their business in the economic. Solving the problem of the relationship between business fitness and economic fitness also helps the investors to wisely choose where and what to invest in. A solution to this problem will enable the investors to maximize their profits as they will have a good understanding of the economy.

Statistical application

Hypothesis

The first step in solving this problem using statistical applications is by formulating the hypothesis of the analysis. A hypothesis is an untested statement of any statistical analysis that states the relationship between the variables involved in the analysis. The hypothesis is always phrased from the research question. From my problem, the research question is whether the ability to execute/business fitness depends on economic fitness/ attractiveness.

The research question gives two hypotheses. The first hypothesis is the null hypothesis which is constructed under the assumption that the variables involved have no relationship. From my research question, the null hypothesis is that there is no relationship between the ability to execute/business fitness and economic fitness/ attractiveness.

The second hypothesis is the alternative hypothesis which is simply the opposite of the null hypothesis. It is constructed under the assumption that the variables involved have a significant relationship. From my research question, the alternative hypothesis is that there is a relationship between the ability to execute/business fitness and economic fitness/ attractiveness.

Statistical errors

The other very important thing to consider when solving this problem using the statistical application is the possible error that can occur in decision making. One of the possible errors is the type 1 error. The type 1 error occurs if the conclusion of the research shows the presence of a relationship between the involved variables when in fact the variables have no relationship.

The other type of error that can be committed in the decision-making process is the type II error which occurs if the conclusion of the research shows ab absence of a relationship between the involved variables when in fact the variables have a relationship (Kaur & Stoltzfus, 2017).

Independent and dependent variables.

The constructed hypotheses help us to determine the independent variables and those that are dependent. The independent variable is the predictor of the variable that is being measured while the dependent variable is the variable that is being measured. In this study, the dependent variable is the ability to execute/business fitness because it is assumed to depend on the economic fitness/ attractiveness and it is the variable that is being measured. Economic fitness/ attractiveness is the independent variable because the ability to execute/business fitness will be assumed to depend on it. That is, it’s the predictors of the variable that’s being measured (Dinga et al., 2020).

Sample and sampling

To make experiments or a statistical analysis feasible, a sample is always extracted from the entire population or the given data set. The results gotten by analyzing the sample are always generalized for the whole data set. A sample is always chosen wisely to avoid biases in the study. In this problem, a sample of twenty-three initiatives was randomly chosen (Fei et al., 2020).

Analysis

The first step in the analysis stage is to compute the descriptive statistics. The descriptive statistics summarize the given data set. Descriptive statistics include central tendency measures, variability measures. Central measures include mode, mean, and median while the variability measures include the standard deviation, range, variance, maximum and minimum. The descriptive statistics of these variables can be coded in excel by first clicking the data analysis on the Data tab, selecting descriptive statistics, input range, output range, checking the Summary statistics, and then clicking Ok (Haden, 2019).

Economic Fit/ Attractiveness (70)
Mean	49.91304
Standard Error	1.260392
Median	50
Mode	50
Standard Deviation	6.04463
Sample Variance	36.53755
Kurtosis	-0.2799
Skewness	-0.34404
Range	22
Minimum	38
Maximum	60
Sum	1148
Count	23
Confidence Level (95.0%)	2.613894

Ability To Execute / Business Fit (30)
Mean	23.47826
Standard Error	1.258755
Median	26
Mode	28
Standard Deviation	6.036778
Sample Variance	36.44269
Kurtosis	0.188534
Skewness	-1.24684
Range	18
Minimum	10
Maximum	28
Sum	540
Count	23
Confidence Level (95.0%)	2.610499

To statistically solve the problem, a regression analysis should be conducted to show whether the relationship exists and whether it’s significant. Regression analysis estimates the relationship between the independent variable and the dependent variable. The regression analysis can be conducted by first arranging the columns of the data and then clicking the Data tab, Data analysis, regression analysis, and then clicking Ok (Ghosal et al., 2020).

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.257799178
R Square	0.066460416
Adjusted R Square	0.02200615
Standard Error	5.9699853
Observations	23
ANOVA
	df	SS	MS	F	Significance F
Regression	1	53.28391624	53.28391624	1.495028988	0.234983395
Residual	21	748.4552142	35.64072449
Total	22	801.7391304
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	10.62743401	10.58354867	1.004146562	0.326738896	-11.38226032	32.63713
Economic Fit/ Attractiveness (70)	0.257464301	0.210567923	1.22271378	0.234983395	-0.180435667	0.695364

Summary

The descriptive analysis shows that the economic fit/attractiveness score has a mean of 49.91. The median and mode scores of the economic fitness/attractiveness are 50. The standard deviation and the sample variance of the economic fit/attractiveness score are 6.04 and 36.54 respectively. The range of the economic fit/attractiveness score is 22 while minimum and maximum scores are 38 and 60 respectively.

The descriptive analysis also shows that the ability to execute/business fit score has a mean of 23.48. The median and mode scores of the ability to execute/business fit scores are 26 and 28 respectively. The standard deviation and the sample variance of the ability to execute/business fit score are 6.04 and 36.44 respectively. The range of the ability to execute/business fit score is 18 while minimum and maximum scores are 10 and 28 respectively.

The regression analysis carried out at a 95% level of significance has a P-value of 0.23. Since the P-value is greater than alpha 0.05, we fail to reject the null hypothesis and conclude that there is no relationship between the ability to execute/business fitness and the economic fitness/ attractiveness.

References

Dinga, R., Schmaal, L., Penninx, B. W., Veltman, D. J., & Marquand, A. F. (2020). Controlling for effects of confounding variables on machine learning predictions. BioRxiv.

Fei, H., Tan, S., Guo, P., Zhang, W., Zhang, H., & Li, P. (2020, October). Sample optimization for display advertising. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 2017-2020)

Ghosal, S., Sengupta, S., Majumder, M., & Sinha, B. (2020). Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases-March 14th 2020). Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 311-315.

Haden, P. (2019). Descriptive statistics. The Cambridge Handbook of Computing Education Research, 102-131.

Kaur, P., & Stoltzfus, J. (2017). Type I, II, and III statistical errors: A brief overview. International Journal of Academic Medicine, 3(2), 268.

Khraisha, T. (2020). Complex economic problems and fitness landscapes: Assessment and methodological perspectives. Structural Change and Economic Dynamics, 52, 390-407.