Final Project
Bryant/Smith Manual Case 7:
Tom’s Used Mustangs

Applied Managerial Statistics

I. Executive Summary

The data file named “MUSTANGS”, contains observations on 35 used Mustangs with a variation of 10 different characteristics[1]. This file was used to prepare a report on the influence of various options on asking price and to relay how this information could be used to set prices on used Mustangs. Statistical analysis by Hypothesis Testing and Multiple Regression Analysis was performed on the asking prices for used Mustangs and it was found that there are five independent variables that affect the selling price of used Mustangs:

• If the car is a convertible or not
• If the car is a GT model or not
• Age of the car in years
• Odometer reading in miles
• Number of cylinders in the engine

II. Introduction

Research suggests that pricing strategies can have a huge influence on company profits.[2] Several customers of Tom’s Used Mustangs have mentioned that asking prices are way out of line with the rest of the market. These prices may be too high or too low, and are never close to the going rate. It had been determined that asking prices for the used Mustangs were based on an informal scheme and has not proved to be as effective as anticipated.

In researching competitors, and because the physical characteristics are many, there has been some difficulty pinpointing a useful pricing strategy. To effectively find a pricing scheme, statistical analysis of the given data involved hypothesis testing and multiple-regression analysis. Hypothesis testing is the rational agenda for applying statistical tests. The main question we usually wish to dig up from a test such as this is whether the sample data is important or not. There are two hypotheses that are possible:

• H0: the null hypothesis. The number is from a standard normal distribution with μ = 0. • HA: the...

...and Posttest with dependent (paired) samples
Hypothesis Testing in Regression-Null hypothesis: β=0/ t- statistic: t= b-b/sb
Average standard error test statistics DF
Sample to pop | | | | |
Sample mean with independent sample | | | | |
Pre and post test | | | | |
Interpretations:
Accept: the relationship/ answer is not statistically significant
Reject: the relationship/ answer is statistically significant.
Hypothesis testing is a statistical technique for evaluating whether a statement is more likely true or false.
EXAMPLE
Two hundred recipients are selected; 120 are randomly assigned to a workfare program, and 80 are assigned to a control group. By follow-up interviews, the state finds out how much income per week is earned by each individual, with the following results: Present a hypothesis and a null hypothesis, and evaluate them at the 1% and 0.1% level of type I error. State a conclusion in plain English. (30 points) mean for workfare 242.5 std 137. Control, mean 197.3 std 95.
Step 1: Formulate Hypothesis. Null is “no effect”
Ho workfare has no effect on wether an individual earns extra money
Ha workfare increases incentives for an individual to make extra money
Step 2: Calculate Sample statistics
Step 3: Decide Type I error
1% and .1%
Step 4: Calculate Test statistics
Step 5: Find P-value
if...

...BRYANT/SMITH CASE 32
WHAT FACTORS INFLUENCE PAYING BILLS ON TIME???
Ernest G. Hamilton III
Keller Graduate School of Management
GM 533 – AppliedManagerialStatistics
December 14, 2010
INTRODUCTION
As you requested, the following information below illustrates my analysis in determining whether the dollar of a bill has an effect of the number of days the bill is late. Additionally, I have obtained information that determines if the bill is from a residential or commercial customer has any effect on the number of days the bill is late. The statistical analysis of the data includes regression analysis.
Data
The collection agency supplied a sample of 96 randomly selected customer bills. These bills demonstrate the following information:
* One dependent variable (y), which is the number of days to collect the payment
* Two independent variables (x1 & x2), which are the amount of the overdue bills and the specific type of customer accounts
Results
* The model depicts an average time of 50 days late for the bill
* The average amount per bill was approximately $174
* According to the model, it suggests there is a correlation between the number of days the bill is late, the size of the bill, and the kind of customer account.
* Analysis shows that when the size of the bill increases, the number of days the bill is late decreases, thus proving the relationship between the two variables is inversely...

...of 1000 flights and proportions of three routes in the sample. He divides them into different sub-groups such as satisfaction, refreshments and departure time and then selects proportionally to highlight specific subgroup within the population. The reasons why Mr Kwok used this sampling method are that the cost per observation in the survey may be reduced and it also enables to increase the accuracy at a given cost.
TABLE 1: Data Summaries of Three Routes
Route 1
Route 2
Route 3
Normal(88.532,5.07943)
Normal(97.1033,5.04488)
Normal(107.15,5.15367)
Summary Statistics
Mean
88.532
Std Dev
5.0794269
Std Err Mean
0.2271589
Upper 95% Mean
88.978306
Lower 95% Mean
88.085694
N
500
Sum
44266
Summary Statistics
Mean
97.103333
Std Dev
5.0448811
Std Err Mean
0.2912663
Upper 95% Mean
97.676525
Lower 95% Mean
96.530142
N
300
Sum
29131
Summary Statistics
Mean
107.15
Std Dev
5.1536687
Std Err Mean
0.3644194
Upper 95% Mean
107.86862
Lower 95% Mean
106.43138
N
200
Sum
21430
From the table above, the total number of passengers for route 1 is 44,266, route 2 is 29,131 and route 3 is 21,430 and the total numbers of passengers for 3 routes are 94,827.
Although route 1 has the highest number of passengers and flights but it has the lowest means of passengers among the 3 routes. From...

...AppliedManagerial Decision Making
MGMT600-1301B-03
Phase 3 individual project
Rocklyn Kee
Colorado Technical University Online
Professor Donald Pratl
March 11, 2013
There are 500 employees in the sales force of Company W that are spread out over Southeast, Northeast, West, and Central regions. The company has recently incorporated a new software program in and attempt to monitor how many sales are generated by each employee. It is expected that each month each region should sell the same aamount of products. It has been noted that over the last three months however that this expectation has only been reached by half of the employees in each region. Before a decision can be made on possible theories as to why this is, some statistical testing must be done. Company W knows that there are different techniques that can be used to statistically analyze this issue. The one that we will be discussing here will be non-parametic statistics and hypoyhesis testing along with chi-square distribution testing of data. Let us begin by first defining these terms for a better understanding.
* Hypothesis Testing
This is a technique that is applied sequentially by businesses in order to obyain concluions in regard to population utilizing information obtained from a sample. This information is gathered so as to enable a decision to be made as to the acceptance or rejection of the hypothesis by the researcher. The...

...B6014 MANAGERIALSTATISTICS
Course Description: This course introduces students to basic concepts in probability and statistics of relevance to managerial decision making. Topics include basic data analysis, random variables and probability distributions, sampling distributions, interval estimation, hypothesis testing and regression. Numerous examples are chosen from quality-control applications, finance, marketing and management. Type and Length of Exam: Open book, 3 hours, calculator such as HP-12C or HP-21S required. Laptop not allowed. Specific Topics Covered: Descriptive statistics, including mean, median, mode, standard deviation and variance, and the ability to calculate and interpret these. Elementary probability theory, understanding basics including the ability to calculate the probabilities of unions and intersections of simple events. Understand the idea of independence of events. Conditional probabilities, Bayes rule. Distributions, including the binomial and the normal distribution. Sampling and sampling distributions. Understand the concept of the standard error. Be able to build confidence intervals on the means or proportions or the difference in means or proportions based on sample data. Understand the testing of hypotheses for means, proportions, and the difference of means and proportions. Linear regression analysis. This includes understanding the idea of the regression equation,...

...ManagerialStatistics
Distinguish between primary data and secondary data?
OBJECTIVE
The main objective of this topic is to measure the degree of relationship between the variables under consideration.The correlation analysis refers to the techniques used in measuring the closeness of the relationship between the variables.
DEFINITION
Some important definitions of correlation are given below:
1. “Correlation analysis deals with the association between two or more variables”. ---- Simpson & kafka.
2. “When the relationship is of quantitative nature, the appropriate statistical tool for discovering and measuring the relationship and expressing it in brief formula is known as correlation”.----- Croxton & Cowden.
3.Correlation analysis attempts to determine the “degree of relationship between variables”.----- Ya Lun Chou.
Thus correlation is a statistical device which helps us in analyzing the covariation of two or more variables.
TYPES OF CORRELATION
Correlation is described or classified in several different ways.Three of the most important ways of classifying correlation are:
1.Positive or negative 2.Simple, partial and multiple 3. Linear and non-linear
The various methods of studying correlation are
1.Scatter Diagram Method
2.Karl Pearson’s Coefficient of correlation.
3.Method of Least Squares [Of these , the first two methods shall be discussed as follows. ]
SCATTER DIAGRAM
What it is: A scatter diagram is a tool...

...Statistics 1
Business Statistics
LaSaundra H. – Lancaster
BUS 308 Statistics for Managers
Instructor Nicole Rodieck
3/2/2014
Statistics 2
When we hear about business statistics, when think about the decisions that a manager makes to help make his/her business successful. But do we really know what it takes to run a business on a statistical level? While some may think that businessstatistics is too much work because it entails a detailed decision making process that includes calculations, I feel that without educating yourself on the processes first you wouldn’t know how to imply statistics. This is a tool managers will need in order to run a successful business. In this paper I will review types of statistical elements like: Descriptive, Inferential, hypothesis development and testing and the evaluation of the results. Also I will discuss what I have learned from business statistics.
My description of Descriptive statistics is that they are the numerical elements that make up a data that can refer to an amount of a categorized description of an item such as the percentage that asks the question, “How many or how much does it take to “ and the outcome numerical amount. According to “Dr. Ashram’s Statistics site” “The quantities most commonly used to measure the dispersion of the values about...

...making. These developments were so successful that after World War II many companies used similar techniques in
managerial decision making and planning.
The decision making task of modern management is more demanding and more important
than ever. Many organisations employ operations research or management science personnel or
consultants to apply the principles of scientiﬁc management to problems and decision making.
In this module we focus on a number of useful models and techniques that can be used in the
decision making process. Two important themes run through the study guide: data analysis and
decision making techniques.
Firstly we look at data analysis. This approach starts with data that are manipulated or processed
into information that is valuable to decision making. The processing and manipulation of raw
data into meaningful information are the heart of data analysis. Data analysis includes data
description, data inference, the search for relationships in data and dealing with uncertainty
which in turn includes measuring uncertainty and modelling uncertainty explicitly.
In addition to data analysis, other decision making techniques are discussed. These techniques
include decision analysis, project scheduling and network models.
Chapter 1 illustrates a number of ways to summarise the information in data sets, also known as
descriptive statistics. It includes graphical and tabular summaries, as well as summary measures...