Business Decision Making Correlation between Independent Variable and Dependent Variables Outcome - Statistics Project Example

Business Decision Making January 23, Introduction Statistical findings enhance management decisions. The research delves on the use of statistics to aid management decisions. Management has the prerogative to use different references to enhance its decision making process (DuBrin, 2009). Management can use the financial statements as basis for deciding whether to continue the current marketing activities or expand its business sphere. Other management officers use economic forecasts as basis for setting up a new branch in another uncharted business territory. The current research will aid management make decisions. The current research used statistical tools to aid management’s plans to hire employees during certain business conditions. The research focused on the problem of hiring more employees during certain weather conditions. As the weather increases, an increase in customer visits persuades management to hire more sales workers. Plan for the gathering of primary and secondary data to resolve the hiring business problem A plan was crafted to determine whether temperature influences the sales output of business establishments (Kotler, 2006). The survey will answer the business problem: At what weather condition should the research respondents hire part time sales persons? The researcher will include references from refutable book authors. Likewise, the researcher will use online journals to complement the book references. In addition, the research will include primary resource. The primary resource is the sales output survey. Further, the secondary references will complement the primary resource. Survey methodology and sampling frame Six individuals were asked to sell a food product for 20 days. There were four male respondents and two female respondents. The individuals were asked to roam the streets to sell to people crossing an assigned street corner. The results of their sales outputs were recorded. The research included the two variables. The independent variable is the weather. The dependent variable is the quantity of products sold on each temperature occurrence (Kotler, 2006). In term of sampling frame, the results of all four respondents were taken. The respondents represent 60 percent of the total individuals selling food products along the busy street corner. The sales data of each day’s total sales was collected. The respondents were taught the basic techniques of selling the food products. The respondents were told to be persistent in their selling activities. Consequently, the respondents were able to sell the products. The respondents were trained to advertise the many advantages of the buying the food products. The researchers were instructed to report their daily sales to the main researcher, this business research reporter (Johnson, 2010). The business research reporter recorded the temperature during each sales day. The research reporter observed that the temperature fluctuated on different days. The 20 business days did not necessarily indicate that the sales activities were conducted consecutively, without break. Instead, there were breaks between some sales activities. The reason for the day breaks is to allow the respondents to sell on days when they are available (Brigham, 2011). For example, the respondents mentioned that they were busy on some business days. Consequently, the group agreed to sell only on days when they are free. The research focuses on one aspect of the marketing mix (Hartline, 2011). The marketing mix is composed of four P’s. The first P is product. The company must sell products that fill the company’s current and future customers’ needs. The customers will only buy products that they want or need. No amount of forcible sales presentation will convince a customer to buy a product that he feels he or she does not need. For example, selling a paperbound book to a blind current and future customer will not persuade the blind person to buy the book. Blind persons cannot see. Consequently, the blind person will not see any benefit of buying a paperbound book because he or she cannot read. Likewise, the male customer will find it insulting for a store sales person to convince the male customer to buy a female person’s clothes (Ferrell, 2010). The customer may be insulted by the store person because males normally do not need some female products for their personal use. The customer may think the sales person thinks the customer is gay. Similarly, selling guns to a Catholic priest may not be a good marketing strategy. The priests normally do not need guns. Especially in a peaceful environment, the Catholic priest may find it very insulting for the sales person to sell a gun to a priest. The priest’s main function is to fill the people’s need for spiritual peace, not enter a shooting war with the neighborhood gangs. The second marketing mix P is the price (Nutt, 2010). The current and future customers prefer to buy their products at prices that are reasonable. Reasonable is not the same as lowest price. Some products are sold at lower prices because they lack the necessary quality raw materials and services that are evident of the higher priced products and services. For the financially handicapped or poor customers, they have no other recourse but the buy the lowest priced product. Doing so will permit the poor or financially handicapped current and future customers to use the save purchase money to buy other goods and services. On the other hand, the rich or affluent members of society can afford to buy their preferred products and services at higher prices. The rich and famous normally do not prioritize the price of the products and services. The rich and famous have the money to purchase the expensive products and services. The third marketing mix P is promotion (Daft, 2009). The company will advertise the benefits of selling the store’s products and services. The focus of promotion is advertising. Advertising entails displaying the products many features in the four media outlets. The television is a good media advertising choice. Television advertising targets prospect customers who are watching their favorite television shows. Other television advertising promotional strategies include targeting people who are hearing the news or sports events. Second, the promotion includes selling the company’s products and services in the newspaper spaces (Hartline, 2011). The company can pay for a line advertisement. Another company can afford to pay for a higher priced ¼ page newspaper advertising space. The company can hire popular actors and actresses to promote the company’s products and services. The company can also hire popular singers to spread the many advantages of purchasing the company’s products and services. Third, the company can advertise the company’s products and services using the radio medium (Ferrell, 2010). The company pays for several minutes of advertising time slots. The radio accepts advertisements for a fee. The radio advertisements are often placed within a radio news time schedule. Other radio slots include advertisements within a musical number’s time. Lastly, the third marketing mix P is place (Kotler, 2006). Some current and future customers prefer to buy their product and service requirements from one preferred place. Some current and future customers may prefer to buy from the nearby grocery store. Doing so will save the customers both time and money. Travelling to the grocery entails paying for transportation expenses. The current and future customers will pay for more gasoline expenses to travel to another county just to buy a product that is $ 0.10 lower than the nearby grocery store’s product or service. The $ 0.10 savings is unfavorably offset by a higher $50 gasoline expense payment. In terms of place, some stores are better managed (Phillips, 2012). The store managers determine the target market’s preferred product and service demands. Next, the store purchases the desired products and services. Next, the store fills the eager current and future customers’ needs and wants. Some small corner stores do not have the financial capacity to set up a bigger grocery outlet. The small stores do not have the financial ability to sell more product choices. Consequently, the current and future customers are frustrated to discover that the small corner store does not have the current and future customers’ preferred product on display. Consequently, the individuals have no other recourse but to buy their products and services from the nearest store that has what the customers need. The current research focuses on the place aspect of the marketing mix (Ferrell, 2010). Some countries in the world have freezing temperatures all year round. The countries include those countries located near the North Pole and South Pole. There are countries that are beset with snowy weather during certain times of the year. Russia, U.S. and Japan are among the countries that have snow during winter time. On the other hand, other countries are blessed with the absence of snow during many months of the year. The countries include many African nations. The current research shows that one place’s temperature influences the current and future customers’ desire to buy the company’s products and services. Questionnaire for hiring business problem The questionnaire asks only one question: How many food products did you sell today? The different sales output answers to the question will aid management in its desire to determine when to hire additional part time workers. Management decides to use the weather as the only basis for hiring part time workers. Often, customers would shift to the competitors’ stores because of one or more reasons. One of the reasons is that some current and future customers hate waiting for several minutes to buy their preferred product or service. Some customers abhor falling in line behind a long queue of customers. When there is only one cashier, the customer has no other choice but to fall in line. Consequently, many busy customers drop out of the cashier line and walk away from the store disgruntled (DuBrin, 2009). Section 2: Technique ranges to analyse data business effectively There is a range of techniques used to analyse business-related data more effectively (Clauss, 2010). The techniques include several statistics tools. The mean or average statistical tool will be used. The mean is the average sales output. The figure is arrived at by dividing the total of all the individual sales outputs by the total number of data collected. The mean is one of the most common statistical tools used for interpreting gathered research data. In the current research, the data gathered represents the different sales outputs cropping up during each research day. The current research shows that the research focused on 20 days. The mode statistical is also included (Johnson, 2010). The mode is used to determine the number of occurrences during the entire research period. The mode represents the most number of occurrences that occur within the entire research process. For example, the mode of 47 indicates that 47 cropped up the most number of times, when compared to the cropping up of other alternative data. The current research focuses on the generation of sales output data. The median statistical tool aids the decision making process (Donnelly, 2007). The median amount is the figure that appears in the middle of all the data gathered. Arranging the sales output from the lowest sales output figure to the highest sales output figure, the research can easily pick the figure that stands at the midpoint of the long list of sales output figures. Another popular tool is standard deviation (Salkind, 2009). The standard deviation shows the deviation from the normal or more popular sales output figure. The deviation tool is used to compare the variance between two data sets. The research may indicate whether there a deviation or not (Salkind, 2009). The scatter plot graph tool contributes to the creation of the trend line (Johnson, 2010). The scatter shows the different meeting points between each pair of X data and Y data. In the current research, the pair of points includes the temperature points and the sales output points. The scatter plot graph shows an overall bird’s eye view of the relationship between the two variables (the temperature variable and the sales output variable). The trend line statistical tool shows whether there is a positive or negative correlation between the independent variable (temperature) and the dependent variable (sales output). The scatter plot precipitates to the creation of the trend line. The trend line helps the scatter plot graph reader predict the future outcome (dependent variable) when a certain condition (independent variable) crops up (Johnson, 2010). There are advantages in using the trend line statistical tool (Johnson, 2010). The trend line tool prepares the scatter plot reader to prepare for the successful management of future events. In the current research helps the scatter plot user ensure that all future needs and wants are effectively met. In the current research the scatter plot user can ensure the increased needs of the customers are successfully met. Likewise, the scatter plot graph persuades the scatter plot graph user to accept that there are times when sales outputs will decline. Consequently, the graph user can implement acts or non-acts to remedy the said business or activity decline. The quartile and percentile statistical tools show other aspects of the current research. The quartile shows the sales output on a four different quartile levels. Likewise, the percentile statistical tool aids the decision maker manage an activity or business at different activity levels. Likewise, the percentile statistical tool helps management discover the minimum or maximum activity level outputs at different situations or situation (Salkind, 2009). Summary information using representative values to enhance management’s decision making process Table 1 Measures of Dispersion and other Excel 2007 software statistical tool outputs Statistical Tool Output Mean 25.45 Median 25 Mode 23 Standard Deviation 5.13 Range 17 Variance 26.37 The above table is based on the following day research data (Johnson, 2010). The table shows initial findings of the research. The mean statistical tool indicates that the research respondents’ average sales production is 25.45 units. The median statistical tool shows clear evidence that the research respondents’ sales is 25 units. The mode statistical tool indicates that the research respondents’ most frequent temperature-based sales production is 23 units. The standard deviation statistical tool offers evidence the research respondents’ deviation standard is 5.13 units. The range statistical tool indicates that the difference between the research respondents’ highest sales during one temperature situation (34 units sold) and the lowest sales during one temperature situation (17 units sold) is 17 units. The variance statistical tool indicates that the research respondents’ variance outcome is 26.37 units The research was conducted on 20 business days as follows: Table 2 Sales outputs at different Temperature levels Temperature Sales units 64 19 69 23 74 21 80 26 77 28 80 31 89 32 96 34 91 30 81 27 81 24 74 23 72 19 71 20 79 21 64 17 92 32 58 23 84 28 84 31 The above data shows different sales outputs (Johnson, 2010). Notable in the above table is the emphasis that sales output fluctuates during different temperature levels. Similarly, sales outputs at similar sales levels indicate that there variances in the sales production. For example, the table shows that there were 28 units sold when the temperature was 28 degrees Fahrenheit. However, a higher volume of product units, 31 units, were sold during another 84 degrees Fahrenheit situation. The above table also shows that a lower 23 product units were sold when the day’s temperature dropped to only 58 degrees Fahrenheit. Another peculiar output of the above table indicates that the sales units was lower, 17 product units, when the temperature was pegged at the higher 64 degrees Fahrenheit temperature level. Compounding the issue, the sales output rose to the higher 32 output units when the temperature rose from 58 degrees Fahrenheit to 92 degrees Fahrenheit. Effect of Quartiles, Percentiles, and Correlation coefficient on business related conclusions Table 3 Quartile Statistics Quartile Output First Quartile 21 Second Quartile 25 Third Quartile 30 Fourth Quartile 34 The above quartile statistic table shows how the research respondents’ sales outputs were generated under four different quartiles. The above table indicates the first quartile generated 21 units of food products. The above table shows the second quartile generated 25 units of food products. The same table indicates that the third quartile generated 30 units of food products. The same table proves that the fourth quartile produced 34 food product units. The table clearly shows that as the quartile progresses to the next level, the quantity of food product sales also increases (Johnson, 2010). The quartile is divided into four quartiles (Salkind, 2009). The first quartile is 25 percent of the research respondents’ total sales. The second quartile represents sales at the 50 percent level. The third quartile represents sales at the 75 percent environment. The fourth t quartile is 100 percent portion of the research respondents’ total sales. Table 4 Percentile Statistics Percentile Area Output 90 Percentile 32.00 80 Percentile 31.00 70 Percentile 28.00 60 Percentile 27.40 50 Percent 25.00 Using percentile statistics tool, the research showed the above percentile outputs (Johnson, 2010). Using the 90 percent percentile, the respondents generated 32.00 sales production units performance. Using the 80 percent percentile, the respondents produced 31.00 sales production units performance. Using the 70 percent percentile, the respondents sold 28.00 sales production units performance. Using the 60 percent percentile, the research respondents sold 27.40 sales production units performance. Using the 50 percent percentile, the respondents sold 25.00 sales production units performance. The table clearly shows that as the percentile increases, the quantity of food product sales should be increased. Section 3: Decision making data in organizational context Formal Business Report To affected individuals and entities: The following is a graph indicating the results of research conducted. The research focused on the effect of temperature on the sales output. The research included both primary resources and secondary resources. The primary resource is the actual sales activity for 20 different days. The different days showed the temperature fluctuated during the time of the selling activities. The research findings included the sales outputs of each sales day. Similarly the research included the temperature data of each business sales day. The research findings include the scatter plot graph. Next, the scatter plot graph is used to create the expected trend line. Excel 2007 software was used to compute all the statistical tool outputs (Clauss, 2010). Likewise, the same software was used to generate the scatter plot graph, the trend line generation, and the trend line formula. Finally, the same software was used to compute for the coefficient correlation. The Excel 2007 software was used to arrive at the mean, median, mode, range, variance, percentile, quartile, and standard deviation outputs. Statistical Tools The Excel 2007 software generated several research findings. Rounding the sales outputs to the nearest ones data, eliminating the decimal numbers, the mean is 25 units. The median stands at 25 units. The mode sales outcome is 23 units. The standard deviation is 5 units. The range between the highest sales output figure and the lowest sales output figure is 17 units. Finally, the variance is 25 units (Clauss, 2010). Table 5 Statistical Tools’ outputs Statistical Tool Output Mean 25.45 Median 25 Mode 23 Standard Deviation 5.13 Range 17 Variance 26.37 Graphs: Graph 1 Bar Chart: The above graph clearly shows the relationship between the temperature and the sales units (Johnson, 2010). The above graph shows that the highest temperature is 96 degrees Fahrenheit. Likewise, the same graph indicates that the lowest temperature is 58 degrees Fahrenheit. In terms of sales outputs, the research respondents’ highest sales output during one temperature situation is 34 units. Similarly, the lowest sales production of the research respondents during one temperature environment is 17 units. The above graph indicates that as the temperature of the day increases, the research respondents’ sales performance improves. Improvement equates to higher sales figures. The above graph indicates that there is a relationship or correlation between the temperature and the sales units output. There is a direct relationship between the temperature and the sales unit output. A direct relationship occurs when the sales increases as the temperature or product time increases. On the other hand, an indirect relationship occurs when the sales units decrease as the temperature increases (Salkind, 2009). Scatter Plot: Graph 2 Sales in relation to Temperature The above scatter plot shows that the relationship between the independent variable and the dependent variable (Salkind, 2009). The independent variable is the temperature. The dependent variable is the sales outputs. The above scatter plot indicates that there is a relationship between the temperature and the sales unit sales. Using the above graph as analytical tool, the plot clearly indicates that the sales units increase as the temperature increases. The temperature exerts influential or significant influence on the research respondent’s sales outputs. Trend line to assist forecasting for certain business situations Graph 2 Trend line: Sales Output The above scatter plot clearly shows that there is a trend (Johnson, 2010). The trend indicates that as the temperature increase, the sales output also increases. The above Y formula to arrive at the forecasted future sales is shown on the upper right portion of the graph. Using the above formula, the sales output will be 43 output units when the temperature is 82 degrees. However, the sales output will be lower (33 output units) when the temperature is 60 degrees. Using linear trend line approach the coefficient correlation is 0.697 (Salkind, 2009). The coefficient correlation indicates that the company will sell more goods when the temperature rises. There are times when the customers prefer to buy their goods and services. Some current and future customers prefer to buy their goods during cold weather. Other current and future customers choose to go out and buy their personal and other store necessities during warm weather. There are some advantages of preferring warmer weather to purchase home, school, and office needs. One such advantage is that the current and future customers do not have to suffer from freezing temperatures just to buy shampoo or food items from the nearby grocery store. In terms of forecasting, the above trend line graph indicates that the business entity should hire more employees or store personnel during warm temperature (Clauss, 2010). The graph persuades management to hire more employees to sell during the warm or higher temperatures in order to immediately fill the needs of the increased customer visits. On the other hand, the graph also convinces the company to retrench or not hire part time employees during times when the temperature has dropped to snow levels. The above trend line indicates there is a strong positive relationship between the temperature (independent variable) and the sales output (dependent variable). When the trend line direction is slanting upwards towards the right, the trend line indicates a positive correlation (Johnson, 2010). The current trend line persuades any entity or individual that the sales output increases when the temperature also increases (Johnson, 2010). Conclusion: Based on the above discussion, the independent variable influences the dependent variable’s outcome. The graphs indicate that as the temperature rises, the sales output increases, in direct proportion. The scatter plot graph’s trend line shows the scatter plot users that sales will increase as the temperature increases. Consequently, the graph prods the graph users to fill the needs of the increased needs of the entity’s or sales person’s products. Thus, the graph persuades management or any individual to hire more sales persons during times when the temperature is high. References: Brigham, E. 2011, Financial Management, Cengage Learning, New York. Clauss, F. 2010, Financial Statement Analysis with Microsoft Excel, McGraw-Hill, New York. Daft, R. 2009, Management, Cengage Learning, New York. Donnelly, R. 2007, Statistics,Penguin, New York. DuBrin, A. 2009, Essentials of Management, SouthWestern Press, New York. Ferrell, O. 2010, Marketing Strategy, Cengage Learning, New York. Hartline, M. 2011, Marketing Strategy, SouthWestern Press, New York. Johnson, R. 2010, Statistics, Pirnciples and Methods, J. Wiley & Sons, New York. Kotler, P. 2006, Marketing Management, Pearson, New York. Nutt, P. 2010, Handbook of Decision Making, J. Wiley & Sons, New York. Phillips, J. 2012, Project Management, J. Wiley & Sons, New York. Salkind, N. 2009, Statistics for People Who Hate Statistics,Sage, New York. Read More