Alan Shawn Feinstein Graduate School
321 Harborside Boulevard

DATA ANALYSIS PROJECT
THE RELATIONSHIP BETWEEN CARBON DIOXIDE AND ANNUAL MEAN TEMPERATURE IN THE U.S.

Date: 05/10/2013
I Background
There is one of the most sensitive global climate issues for years, which is global warming. In general, we know that the origin of global warming is the emission of Carbon Dioxide. It was verified that the relationship between Carbon Dioxide and temperature: when there is more Carbon Dioxide the temperature gets warmer. According to the 2007 Fourth Assessment Report by the Intergovernmental Panel on Climate Change, global surface temperature increased 0.74 ± 0.18 °C (1.33 ± 0.32 °F) during the 20th century (Keppler). Because of the tendency of rising temperature during 20th century, we step forward and collect more data from 1990 to 2009, which is more representative for us to find out the relationship between Carbon Dioxide and Annual Mean Temperature, especially in the U.S. II Questions

* Is there a relationship between Carbon Dioxide Emissions and Annual Mean Temperature in the U.S.? * If there is, whether the relationship is positive or negative? III Analysis
Data collected below is the Carbon Dioxide Emissions and Annual Mean Temperature in the U.S. from 1990 to 2009. Year | U.S. Carbon Dioxide Emissions| U.S. Annual Mean Temperature| 1990| 5040.9| 53.16|

2000| 5900.3| 53.57|
2004| 6031.3| 54.12|
2005| 6055.2| 54.63|
2006| 5961.6| 53.42|
2007| 6059.5| 54.66|
2008| 5865.5| 53.31|
2009| 5446.8| 53.39|
1. Analysis 1: Scatterplots & Correlation
Pair the data into the order of (x, y), which x value represents the value of U.S. Carbon Dioxide Emission and y represents U.S. Annual Mean Temperature. (5040.9, 53.16)| (5900.3, 53.57)| (6031.3, 54.12)| (6055.2, 54.63)| (5961.6, 53.42)| (6059.5, 54.66)| (5865.5, 53.31)| (5446.8, 53.39)| Input all the data into TI-84 Plus...

...Eleven Multivariate Analysis Techniques:
Key Tools In Your Marketing Research Survival Kit
by
Michael Richarme
Situation 1: A harried executive walks into your office with a stack of printouts. She says, “You’re the marketing research whiz—tell me how many of this new red widget we are going to sell next year. Oh, yeah, we don’t know what price we can get for it either.”
Situation 2: Another harried executive (they all seem to be that way) calls you into his office and shows you three proposed advertising campaigns for next year. He asks, “Which one should I use? They all look pretty good to me.”
Situation 3: During the annual budget meeting, the sales manager wants to know why two of his main competitors are gaining share. Do they have better widgets? Do their products appeal to different types of customers? What is going on in the market?
All of these situations are real, and they happen every day across corporate America. Fortunately, all of these questions are ones to which solid, quantifiable answers can be provided.
An astute marketing researcher quickly develops a plan of action to address the situation. The researcher realizes that each question requires a specific type of analysis, and reaches into the analysis tool bag for. . .
Over the past 20 years, the dramatic increase in desktop computing power has resulted in a corresponding increase in the availability of computation intensive statistical software....

...Research methods: Dataanalysis
G
Qualitative analysis of data
Recording experiences and meanings
Distinctions between quantitative and qualitative studies Reason and Rowan’s views Reicher and Potter’s St Paul’s riot study McAdams’ definition of psychobiography Weiskrantz’s study of DB Jourard’s cross-cultural studies Cumberbatch’s TV advertising study A bulimia sufferer’s diary
G
Interpretations of interviews, case studies, and observations
Some of the problems involved in drawing conclusions from non-experimental studies.
G
Content analysis
Studying the messages contained in media and communications.
G
Quantitative analysis: Descriptive statistics
What to do with all those numbers and percentages at the end of the study.
Measures of central tendency: Mean, median, and mode Levels of measurement Measures of dispersion: range, interquartile range, variation ratio, standard deviation Frequency polygon, histogram, and bar chart Types of data: nominal, ordinal, interval, ratio Statistical significance Tests of difference: Mann-Whitney U test, sign test, Wilcoxon test Scattergraphs, Spearman’s rho Test of association: chi-squared Questions to test experimental validity Varied definitions of ecological validity
G
Data presentation and statistical tests
When to use a chart or a graph. Which statistical test to choose and why.
G...

...Introduction:
Dataanalysis is an attempt by the researcher to summarize collected data either quantitative or qualitative. Generally, quantitative analysis is simply a way of measuring things but more specifically it can be considered as a systematic approach to investigations. In this approach numerical data is collected or the researcher transforms collected or observed data into numericaldata. It is ideal for finding out when and where, who and what and any relationships and patterns between variables. This is research which involves measuring or counting attributes (i.e. quantities). It can be defined as:
“The numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect is called quantitative analysis”
Quantitative analysis gives base to quantitative geography and considered as one of important parts of geographical research. As, subject matter of quantitative geography is comprehended by the following key issues:
Collection of empirical dataAnalysis of numerical spatial data
Development of spatial methods for measurements, theories and hypothesis
Construction and testing of mathematical models of spatial theory
Concisely, all above mentioned activities develop understanding of spatial processes. Quantitative...

...ITKM
Analysis of Data Mining
The article Data Mining by Christopher Clifton analyzed how different types of data mining techniques have been applied in crime detection and different outcomes. Moreover, the analysis proposed how the different data mining techniques can be used in detection of different form of frauds. The analysis gave the advantages and disadvantages of usingdata mining in different operation. The major advantage was that data mining enables analysis of large quantities of data. This is important since such data cannot be analyzed manually since the data is often complex for humans to understand. However, data mining techniques have been used for deceitful purposes such as inappropriate disclosure of private information. The article analyzed different data mining techniques. Predictive modeling is one such technique used in estimation of particular target attribute. Descriptive modeling was another technique, which entails dividing data into groups. The other techniques described include pattern mining used in identification of rules relating to different data pattern and anomaly detection, which entails determining the unusual instances that, may arise when using the different data-mining...

...CONTEXT OF REAL
WORLD DATA CLUSTERING
ADEWALE .O . MAKO
DATA MINING
INTRODUCTION:
Data mining is the analysis step of knowledge discovery in databases or a field at the
intersection of computer science and statistics. It is also the analysis of large observational
datasets to find unsuspected relationships. This definition refers to observational data as
opposed to experimentaldata.
Data mining typically deals with data that has already been collected for some purpose or the
other than the data mining analysis. It is often referred to as ‘secondary dataanalysis.
The overall goal of the data mining process is to extract information from a dataset and
transform it into an understandable structure for further use.
SCORE FUNCTIONS IN DATA MINING
A score function is a measure of one’s performance while making decisions under
uncertainty.
The purpose of a score function in data mining is to rank models as a function of how useful
the models are to the data miner. A chosen score function should reflect the overall goals of
the data mining task as far as is possible. Different score functions have different properties
and are useful in different situations which is why one should avoid using a convenient...

...NOISE REDUCTION IN DATA USING POLYNOMIAL REGRESSION
Geetha Mary A, Dinesh Kumar P, Girish Kumar K, Gyanadeep N
School of Computing Science and Engineering, VIT University
dinesh.venkata@yahoo.co.in
Abstract:-Noise is common in data which hinders the dataanalysis. We consider noise as low-level data errors or objects that are irrelevant to dataanalysis. Data cleaning technique reduces the low-level data errors but not irrelevant objects. To reduce both types of noise there are three traditional outlier detection techniques distance-based, clustering-based, and an approach based on the Local Outlier Factor (LOF) of an object. In this paper we introduce a new method for noise reduction using polynomial regression and spearman’s rank correlation coefficient ρ. This approach allows a high recognition of noise with low false rate.
1. Introduction
Database may contain data objects that do not adhere with the general behavior or model of the data. Those data objects can be considered as noise or outliers. Analysis of noise or outlier data is called as outlier mining.
In this paper we explain four noise removal techniques. In which three of them are based on outlier analysis techniques:
1) Distance-based outlier detection
2) Density-based local outlier...

...Qualitative dataanalysis
What Is Qualitative Analysis?
Qualitative modes of dataanalysis provide ways of
discerning, examining, comparing and
contrasting, and interpreting meaningful patterns
or themes.
The varieties of approaches - including
ethnography, narrative analysis, discourse
analysis, and textual analysis - correspond to
different types of data, disciplinary traditions,
objectives, and philosophical orientations.
What Is Qualitative Analysis?
We have few agreed-on canons for
qualitative dataanalysis, in the sense of
shared ground rules for drawing
conclusions and verifying their sturdiness
(Miles and Huberman, 1984).
Dataanalysis tends to be an ongoing and
iterative (nonlinear) process in qualitative
research.
The term we use to describe this process is interim
analysis (i.e., the cyclical process of collecting and
analyzing data during a single research study). Interim
analysis continues until the process or topic the researcher
is interested in is understood (or until you run out of
resources!).
Throughout the entire process of qualitative dataanalysis it
is a good idea to engage in memoing (i.e., recording
reflective notes about what you are learning from the...

...| |
| Rebecca RoyN8586799Tutor: Jenny Houtsma |
[BSB123 - Dataanalysis research report] |
Analyzing the relationships between different variables in relation to one year returns within the superannuation industry. |
Contents
1.0 Introduction 2
2.0 Outliers 3
3.0 Historical Analysis 4
4.0 Current Data (One Variable Analysis 5
5.0 Bivariate and Trivariate Analysis 6
5.1 Impact of Investment Strategy on One Year Returns 6
5.2 Impact of Three Year Returns on One Year Returns 8
5.3 Impact of Investment Strategy and Three Year Returns on One Year Returns 10
6.0 Conclusion 11
7.0 Appendix 12
1.0 Introduction
Superannuation is something that is relevant to all working Australians and making the correct decisions can have enormous effects on the future. From the age of eighteen, each working Australian’s employer begins to make employer contributions to their superannuation fund. Once the employer begins to make contributions, the employee must start making decisions regarding the investment strategy, segment and specific fund that these contributions are being invested into. By analysing data from historical and present standpoints, it will become evident which superannuation funds have the ability to prosper in the future, while other funds may plummet, essentially reviewing the entire Australian superannuation industry.
2.0...