Week 8

Running head: MARKET RESEARCH 1

MARKET RESEARCH 2

MARKET RESEARCH REPORT

Miguel Figueroa

February 2nd, 2021

Columbia Southern University

Hypothesis 1

Employees whose working hours which are flexible will always report greater job satisfaction than employees who work for fixed hours.

The hypothesis looked at 200 number of workers who had flexible working hours and 200 others who had fixed working hours to research on the same hypothesis and the results gotten were tabulated in a frequency table as follows:

	Satisfied	Not satisfied
Number of employees with flexible working hours	200	50
Number of employees with fixed working hours	50	150

Therefore we can analyses this information using the statistical analysis tool available. From the data above its approves the hypothesis to be true where the number of workers who are satisfied by their jobs are those who work in flexible hours and those who work with fixed hours are not satisfied. From the information and data above we can therefore say that using the regression available we can say that the two variables have a linear relationship (Daoud 2017, December). This is because if the data above was to be drawn on a graph then a straight line would be formed and therefore making the same a linear relationship, however, this is a negative linear relationship that is as the number of working hours increase the job satisfaction rate also reduces, the graph, in this case, would have a zero slope. The conclusion to this data is therefore that flexibility in working hours should be given to workers to enhance productivity in organizations, shifts should be introduced to enhance this aspect.

Hypothesis 2

Another hypothesis would therefore be sleep-deprived people do perform worse on the job than individuals who are not sleep-deprived.

The research, therefore, took a total number of individuals who are both sleep-deprived and others who are not sleep-deprived, the number of people or workers who were deprived of sleep were 300 and those who were not deprived of sleep were also 300 and the results got from the research tabulated in the below table;

	Performed good	Performed worse
Workers deprived of sleep in the company.	50	250
Workers who were not deprived of sleep in the company	250	50

Form the data collected, analyzed and recorded in the table above the paper can conclude that the hypothesis that workers who are sleep deprived performed worse than workers who were not sleep-deprived, the number of workers who were deprived of their sleep and performed well were 50 compared to 250 of those who were not deprived their sleep. Therefore we can say that the variables in the hypothesis in this research is that just like in the other hypothesis the relationship is a linear, but in this case, the linear relationship is negative, this states that when one variable increases the other one decreases (Kumari & Yadav 2018), and in this case as the number of sleep of workers is being reduced the amount and the productivity level decreases and therefore we call this a positive regression because also as we increase the number of sleeping hours of the workers the productivity rate of the same workers also increases. The hypothesis is therefore approved. To conclude, we can say that the sleeping hours of the workers in an organization should be added, workers should be allowed to rest to increase their productivity rate.

Hypothesis 3

Another example of a hypothesis that we can test is that the bright colored products are preferred by customers in the market than dull-colored products.

In testing this hypothesis the paper can therefore take commodities of the same product that is, for example cars of the same type and company and color a number of them for example 100 brightly colored cars and 100 dull-colored cars and then present these cars to a market with the price and all other factors constant and see which cars will be bought more and which ones will be bought less. The results of the research can be tabulated in a frequency table as follows.

	Number of cars bought	Number of cars not bought
Bright colored cars	90	10
Dull colored cars	20	80

The data in the frequency table above therefore approves the hypothesis that is the bright colored products are more preferred than the dull-colored ones, 70 more colored cars were bought compared to the dull-colored cars. The relationship between these variables is therefore linear (Kumari & Yadav 2018), that is the brighter the number the more demand of the same, this also positive linear relationship. The conclusion to this is therefore that the color of the product should be considered while making a product or if you want to present it to the market since demand is directly related to the color.

References

Daoud, J. I. (2017, December). Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing. Retrieved from: https://iopscience.iop.org/article/10.1088/1742-6596/949/1/012009/meta

Kumari, K., & Yadav, S. (2018). Linear regression analysis study. Journal of the practice of Cardiovascular Sciences, 4(1), 33.retrieved from: https://www.j-pcs.org/article.asp?issn=2395-5414;year=2018;volume=4;issue=1;spage=33;epage=36;aulast=Kumari