Magic Case Study
Dear Writer, You will need to use Minitab software to solve to conduct the a regression analysis on the case study data._ In Minitab Program you dont have to use the type command ,only use the icons._ for example to make a regression analysis you have to go to STAT>>REGRESSION>>>REGRESSION and make the analysis to find the coefficients._ Then Apply ANOVA TEST , T-test to recognize which vriables affect the company sales and give your comments. After that, If you have any other methodology to get to the same result then use it as well and include the results in the paper._ In both cases you will have to show and explain your work. Please ask if clarification is needed. Thank you Mohammad Details: 1- I am sorry but the use of Minitab software is mandatory. Usage of other softwares as back up to Minitab is recommended. The main answer should be through Minitab. Simon the need for Minitab competency was mentioned to Amanda on the Live chat prior to submitting the order, and she confirmed the availability of such requirement. 2- After the writer comments on the Findings and provide his/her Recommendations, the writer may use another program (Gretl,SPSS or Excel) as well to back up the initial findings from Minitab software. Simon this issue was discussed with Ashley 2 days ago. I need an urgent reply whether you will be able to prepare my order by the deadline or not as I am getting critical with dead line timing. Please advise.
Magic Case Study
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Magic Case Study
Regression Analysis
The number of sales acted as the response variables while the market potential of the territory, the number of shops, dealers, popular brands and the size of the population as the predictor variables.
The factors that affect sales are the number of dealers and the number of popular brands because they had p values of 0.012 and 0.011 respectively, which are less than 0.05 (Downing and Clark, 2010).
The market potential of each territory, the y intercept, and the number of shops had p values of 0.134, 0.758, and 0.359 (greater than 0.05) respectively hence, are insignificant predictors.
We cannot determine whether the size of the population influences the number of sales because it had a p value of 0.51, which is almost equal to 0.05.
The R squared from the regression summary is 98.13 percent meaning 98 percent of the variation in sales is explained by the response variables used.
Considering the level of significance in the regression model, sales can be predicted by the following equation. Sales = 3.39 + 0.590 No of Dealers - 0.805 No of Popular Brands + 0.1764 Population.
Regression Analysis: sales versus Market Potential, No of Shops, No of Dealer, No of Popular Brands, and Population Size.
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 5 8825.99 1765.20 252.16 0.000
Market Potential of The Territory 1 16.87 16.87 2.41 0.134
No of Shops 1 6.11 6.11 0.87 0.359
No of Dealers 1 51.62 51.62 7.37 0.012
No of Popular Brands 1 53.18 53.18 7.60 0.011
Population 1 29.57 29.57 4.22 0.051
Error 24 168.01 7.00
Total 29 8994.00
Model Summary
S R-sq R-sq(adj) R-sq(pred)
2.64583 98.13% 97.74% 96.96%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 2.03 6.50 0.31 0.758
Market Potential of the Territory 0.262 0.169 1.55 0.134 8.90
No of Shops 0.288 0.308 0.93 0.359 10.17
No of Dealers 0.428 0.158 2.72 0.012 19.88
No of Popular Brands -0.808 0.293 -2.76 0.011 6.93
Population 0.1393 0.0678 2.06 0.051 22.90
Regression Equation
Sales = 2.03 + 0.262 Market Potential of the Territory + 0.288 No of Shops
+ 0.428 No of Dealers - 0.808 No of Popular Brands + 0.1393 Population
...
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