The purpose of this experiment is to determine whether a player’s statistics in baseball are related to the player’s salary. The sample set was taken out of 30 players who were randomly selected from the top 100 fantasy baseball players in 2007. We displayed the information with a scatter plot, and then determined with a linear equation the line of best fit. Along with the line of best fit we are going to analyze the Pearson Correlation Coefficient. This value is represented as an “r-value”. The closer this number is to 1 the better the relationship between the two variables being compared. The three statistics that we compared to the player’s salaries are; Homeruns, RBI, (runs batted in), and batting Average.

The line of best fit for a players home runs to salary using linear regression is .0453029808x+6.586733375. The Pearson Correlation Coefficient, (r-value) is .0811721504. Based on how the graph looks and the distance of the r-value to 1, it is pretty safe to say that there is not a good relationship between the number of homeruns a player hits and their salary. This means that a person’s salary is not based on the number of homeruns that they hit. Next we’ll discuss the relationship between RBI’s and salary.

The line of best fit for a players RBI to salary is .0299088213x+5.00741382. The r-value is .1429247937. While this line of best fit is slightly better than homeruns vs. salary based on the r-value it is still not enough to be considered a good relationship between the two. The lack of relationship between RBI and salary means that a player’s salary is not based upon the number of runs batted in. The last stat we’ll discuss is batting average vs. salary.

The line of best fit for batting average to salary is 93.29024715x-19.57391786. The r-value for this line is .4644363458. Based on this r-value we are 99% confident in our line of best fit. Looking at the scatter plot and the line of best fit it is not nearly as...

...Generalized Linear Models
We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the response variable is discrete and the error terms do not follow a normal distribution, namely logistic regression and Poisson regression. Both belong to a family of regression models called generalized linear models. Generalized linear models are extensions of traditional regression models that allow the mean to depend on the explanatory variables through a link function, and the response variable to be any member of a set of distributions called the exponential family (e.g., Normal, Poisson, Binomial). We can use the function glm() to work with generalized linear models in R. It’s usage is similar to that of the function lm() which we previously used for multiple linear regression. The main difference is that we need to include an additional argument family to describe the error distribution and link function to be used in the model. In this tutorial we show how glm() can be used to fit logistic regression and Poisson regression models.
A. Logistic Regression Logistic regression is appropriate when the response variable is categorical with two possible outcomes (i.e., binary outcomes). Binary variables can be represented using an indicator variable Yi, taking on values 0 or 1, and modeled using a...

...1. Discuss why and how you would use a liner programming model for a project of your choice, either from your own work or as a hypothetical situation. Be sure that you stae your situation first, before you develpp the LP model
Linear programming is a modeling technique that is used to help managers make logical and informed decisions. All date and input factors are known with certainty. Linear program models are developed in three different steps:
Formulation
Solution
Interpretation
The formulation step deals with displaying the problem in a mathematical form. Once that is developed the solution stage solves the problem and finds the variable values. During the interpretation stage the sensitivity analysis gives managers the opportunity to answer hypothetical questions regarding the solutions that are generated.
There are four basic assumptions of linear programming and they are as follows:
Certainty
Proportionality
Additivity
Divisibility
Linear programming is the development of modeling and solution procedures which employ mathematical techniques to optimize the goals and objectives of the decision-maker. Programming problems determine the optimal allocation of scarce resources to meet certain objectives. Linear Programming Problems are mathematical programming problems where all of the relationships amongst the...

...ANNEXTURE
Questionnaire
Dear respondent,
I m a student of “Bhagwan mahavir college of business administration, surat” conducting a survey for my project preparation, as the requirement of partial fulfilment of subject project in third year(semester-VI) BBA in surat city of a study on “A COMPARATIVE STUDY ON BRITANNIA AND PARLE COMPANY IN SURAT CITY (A SURVEY ON BISCUIT )” I assure that the information given by you are strictly used for academic purpose only. I request you to help me in gathering information by filling up yhe following information.
Thank you,
Abhishek sojitra
Bhagwan mahavir business administration
Top of Form
1) Do you eat biscuit?
Yes
No
2) Select your likely tastes for biscuit?
Sweet
Salty
Sweet & Salty
Cream biscuit
Others
3) What type of biscuit you normally prefer?
Branded
Bakery product
4) How often do you eat biscuit?
Once in a week
Once in a month
Once in a fortnight
Alternate days
Every day
5) When do you have biscuit?
At breakfast time
At evening
Any time
6) Which brand you normally buy?
Britannia
Parle
Both
Other:
7) From where do you buy biscuit?
Provisional store
Hawkers
Convenience store
Other:
8) Out of the following brand which...

...UNIVERSITY OF KARACHI
Maximizing profit for the company and
Minimize transportation cost
Aunshehe Nawazi Course In charge:
MCS(1st Semester) Dr. Syed Jamal Hussain
Seat # 03
Course# 507
MAIN OBJECTIVE OF PROBLEM:
Main objective of the problem is to maximize profit of the company, that produces milk made products and minimize shipping cost of these products to supply at different stores in the city. The main objective is achieve by using linear programming optimization methods.
EXACT PROBLEM DEFINITION:
A company produces milk made products such as, Cream, Skim milk, Full Cream Milk, butter and ghee. The company uses 3000000 liters of milk monthly for producing milk made goods. From 3000000 liters of milk company uses 25% of milk for producing cream and skim milk, 25% for producing Full Cream milk, 25% for producing butter and 25% for ghee. According to company policies, company produces only 500 gram pack of cream, 1 liter pack of skim milk, 1 liter pack of full cream milk, 500 gram pack of butter and 1 liter pack of ghee.
750000 liters of milk is 25% of 3000000 liters. From 750000 liters 30% of milk use to extract cream and remaining 70% sales as skim...

...Chapter 2: Linear Functions
Chapter one was a window that gave us a peek into the entire course. Our goal was to understand the basic structure of functions and function notation, the toolkit functions, domain and range, how to recognize and understand composition and transformations of functions and how to understand and utilize inverse functions. With these basic components in hand we will further research the specific details and intricacies of each type of function in our toolkit and use them to model the world around us.
Mathematical Modeling
As we approach day to day life we often need to quantify the things around us, giving structure and numeric value to various situations. This ability to add structure enables us to make choices based on patterns we see that are weighted and systematic. With this structure in place we can model and even predict behavior to make decisions. Adding a numerical structure to a real world situation is called Mathematical Modeling.
When modeling real world scenarios, there are some common growth patterns that are regularly observed. We will devote this chapter and the rest of the book to the study of the functions used to model these growth patterns.
Section 2.1 Linear Functions 99
Section 2.2 Graphs of Linear Functions 111
Section 2.3 Modeling with Linear Functions 126
Section 2.4 Fitting...

...Project Management, 2e (Pinto)
Chapter 3 Project Selection and Portfolio Management
3.1 True/False
1) Numeric project selection models, by their very nature, employ objective values.
Answer: FALSE
Diff: 2
Section: 3.1 Project Selection
Skill: Definition
AACSB Tag: Reflective
2) Every decision model contains both objective and subjective factors.
Answer: TRUE
Diff: 3
Section: 3.1 Project Selection
Skill: Factual
AACSB Tag: Reflective
3) A simplified scoring model addresses all the weakness of a checklist model for project screening.
Answer: TRUE
Diff: 1
Section: 3.2 Approaches to Project Screening and Selection
Skill: Conceptual
AACSB Tag: Reflective
4) The Analytical Hierarchy Process elegantly addresses scaling issues in criteria and negative utility in alternative scores.
Answer: FALSE
Diff: 1
Section: 3.2 Approaches to Project Screening and Selection
Skill: Conceptual
AACSB Tag: Analytic Skills
5) The efficient frontier in a profile model is the set of options that offers a maximum return for a given level of risk or a minimum risk for every level of return.
Answer: TRUE
Diff: 2
Section: 3.2 Approaches to Project Screening and Selection
Skill: Factual
AACSB Tag: Analytic Skills
6) The present value of money is lower the further out in the future I expect to spend it.
Answer:...

...
INTRODUCTION
The Project assigned to me was “A STUDY ON CUSTOMER SATISFACTIOIN REGARDING AFTER SALES SERVICES OF MAHINDRA&MAHINDRA AT SUTARIA AUTOMOBILES SERVICE CENTRE, IN BELGAUM DIST”.
This study will help me to find and customer satisfaction level of the customer for authorized M&M service station in Belgaum dist, To know the reason for decline of customers at service station, To know the perception of customers regarding the charges or rates offered by the service station and To know any suggestion from customers to improve the service station.
The study is scheduled through primary data and other information thereby preparing Questionnaire, which focus of various variables, and attributes that are important to know the satisfaction level and the factors affecting the purchase decision.
The survey caused in the Belgaum Dist with the sample size of 100.The collected data are tabulated and analyzed data and all suggestions are given according to the analyzed data graphs and charts are used to illustrate the statistical data and findings.
INTRODUCTION ABOUT AUTOMOBILES INDUSTRY:
History and development of Automobiles also marks the dynamism in technological growth men have achieved. From the days of horseless carriages to the modern-age self-guided automobiles that are designed meticulously using cutting-edge...

...1. Problems Identified
1.1 Structural Strategy
1. No proper project structure - this made the execution and management of the project very ineffective.
2. Project over budget – The project estimated cost overrun of atleast 20%
3. Poor leadership/no leadership commitment - the customer requested the Divisional GM and his team to present the status of the project. However the DGM instructed Reichart to go with any other 3 or 4 functional managers
4. No Balanced Scorecard with critical success factors, including continuous review and appraisal processes.
5. Lack of a sufficient Guiding Coalition - no support from other functional managers
6. There is no control over functional managers - Reichart did all the work himself due to the lack of communication and the imbalance of power between the project managers and functional managers.
7. There are no policies and procedures in place - Top management failed to implement processes and policies based on programme and project management principles. This indicate a poor leadership approach
8. No proper resource allocation - Reichart did not have adequate resources(people) that could have stay at required pace or make up for the time already lost in the project management
9. There was a lack of project management in the organization - Reichart planning was in isolation not involving functional managers...