Case Study: ANALYSIS

ANALYSIS:

Using the table below, select the city that you will analyze based on the second letter of your last name.

Second Letter of Last Name Cities
a – f Dallas
g – n Chicago
o – z Denver

For example, since the 2nd letter in my last name, Steiner, is a t, I would analyze Denver.  However, Amy Adams, who’s 2nd letter in her last name is d, would analyze Dallas.

For your selected city, complete the following steps:

  • Create a simple scatterplot (without the regression line). Use Price as the predictor (x) variable and Volume as the response (y) variable.  Copy/ paste the scatterplot below.  Discuss if the linearity condition is satisfied. Also discuss the direction and strength of the association and any other observations you may have made.

The above scatter plot shows a linear relationship between price and volume. Therefore, the linearity condition is satisfied. The slope of the scatter points show a negative correlation between the two variables. The correlation between the variables may be weak because the data points are widely scattered with probable outliers.

  • Calculate the correlation coefficient, r, for Volume and Price. Copy/paste the Minitab results below.  Based on the correlation value, what do you conclude about the strength of the linear correlation between the Volume and Price of the frozen pizzas in this city?  Explain your answer.

Pearson correlation of Volume and Price = -0.531

P-Value = 0.000

The above results show that the correlation between volume and price is negative. The correlation coefficient is -0.531. This shows that price and volume have a medium correlation.

  • Find the value of R2 (using Minitab) between the Volume and Price. Explain what R2 means in this context.

R2  =  0.2820

The coefficient of determination is 0.2820. This implies that 28.20% of the variation in volume is attributed to the price.

  • Using Minitab, find the linear regression equation for Volume and Price for this city. Copy/paste the equation below.  Explain what the coefficient (slope) means in this context.

Regression Equation

Volume = 139547 – 33527 Price

The slope of the equaltion is -33527. This implies that when the price increases by one dollar, the volume decreases by 33,527.

  • Perform a hypothesis test to determine if the linear relationship between Volume and Price is significant. State your hypothesis and give the t-statistic and P-value (found using Minitab).  Based on the P-value, do you reject or fail to reject H0?  (Use a significance level of 0.05.) What does this mean for your final conclusion about the linear relationship?

Null hypothesis: There is no significant relationship between volume and price.

Alternative hypothesis: There is a significant relationship between volume and price.

The correlation between volume and price is significant because the p-value, 0.000 is less than the significance level, 0.05. Therefore, we reject the null hypothesis and conclude that the relationship between volume and price is significant.

  • Using your regression equation from Step 4, predict the volume when the price of the pizza is $2.50. Show your work below.  (Round to the nearest whole number.)

We substitute the value of $2.50 to the price.

Volume = 139547 – 33527(2.50) = 139547 – 83817.5 = 55,729.5 or 55,730

  • Using Minitab, find a 95% confidence interval for the mean volume for frozen pizza prices of $2.50 and find a 95% prediction interval for the volume when a frozen pizza is priced at $2.50. Copy/paste the Minitab results below.

Regression Equation

Volume = 139547 – 33527 Price

Variable  Setting

Price         2.5

Fit   SE Fit        95% CI              95% PI

55729.5  843.500  (54063.1, 57395.8)  (39121.0, 72338.0)

 

World of Innovation: Case Study

Introduction  

Business organizations today regard innovation as critical to competitiveness. Globally, 83% of participants in an innovation survey responded that innovation was highly decisive factor in relation to their organization’s success while 88% of the participants responded that this will be for the coming five years (Shelton & Percival, 2013). Although sometimes innovation happens by accident, innovations of products or processes mainly arise out of deliberate effort in research and development (R&D). (Artz, Norman, Hatfield & Cardinal, 2010). Even though R&D spending has become a very important aspect in the competitiveness strategy of organizations as well as becoming a key point of focus for managers, intense debate still continues on the extent to which R&D is benefits business organizations from an economic performance perspective. (Artz, Norman, Hatfield & Cardinal, 2010). This matter has been the subject of serious research however; it has not been conclusively ascertained. (Artz, Norman, Hatfield & Cardinal, 2010) Research still continues on the direction, extent, as well as if the link between R&D spending and firms’ financial performance is consistently positive. (Artz, Norman, Hatfield & Cardinal, 2010).

Traditional methods of measuring of innovation using R&D spending and the number of patents granted have long been considered as indication of economic performance both in organizations as well as nationally. (Bessant & Tidd, 2011). Even though this largely true, R & D spending and the number patents are however not predictive for most of the sectors while also not being reliable performance indicators at the organizational level. (Bessant & Tidd, 2011). More comprehensive indicators of innovation include the percentage of revenue from innovation and new products as well as the level of differentiation. (Bessant & Tidd, 2011).  These represent more wide-ranging, robust and reliable indicators of economic performance. (Bessant & Tidd, 2011).  More critically, organizational, process, and managerial innovations have greater potential of resulting into economic and social gains. The relationship between R&D spending, number of patents and economic performance can therefore not conclusively be considered as always being linear and positive because adopting innovations can also result in a greater impact compared to generating innovations. (Pandit, Wasley & Zach, 2011)

Existing network problems

The technical aspects include defining of the system, and this involves system analysis, design, coding, and then testing and system installation. It also includes training, data conversion, operations support such as problem management as well as definition of releases, evaluation of alternatives, reconciliation of information across various stages and the overview, which is definition of the technical strategy of the project. The management aspects involves the establishing of priorities, definition of goals, tracking of the project and status reports, change management, assessment of risk, analysis of cost versus benefit, user interaction, vendor management, post implementation analysis, as well as quality assurance evaluations. Meeting each of the objectives and the requirements of the SDLC necessitates that specific design approaches must be followed. The SDLC must be representative of a system developed through the use of the techniques it stand for This means that it should employ a layered approach for analysis, design, installation support as well as production support. This also implies that the specific project operations and how they are implemented must be kept distinct in relation to carrying out the tasks and producing the outputs. The SDLC must also organize its information must also be organized in a hierarchical method such that users with different level of knowledge will locate whatever they need easily and quickly.