Analysis of Loan data and relationship with various factors
Introduction
As we all know the history of loans as old as the history of money. Earlier there used to be different mechanism of lending money and recovering it. In simple terms it was the process in which the people who have more money than they required used to give money to people who didn’t had enough. Over the years with the evolution of economics the loan process became extremely important for the people who made business out of it. They used to give loans to people who needed it. But there was always a risk of person defaulting on the loan. For this reason before giving the loan the companies analyses various factors such as the credit history of the borrower, loan period, interest rates, income source etc. in order to prevent any default. In this assignment we are trying to find relation between the interest rates and the various factors like amount, loan length, debt to income ratio, monthly income, FICO score etc. Methods

Data Collection
The data was collected from the link https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv provided on the coursera page. The data was downloaded on 14th February 2013 using R software Exploratory Analysis

Exploratory analysis on the data was done by examining table and plotting the data. The exploratory analysis was used to clean the data and determine factors to be used for the linear regression model. The cleaning of data involved removing inconsistent metrics like year/years, removing percentage signs an converting from factors to numeric for the purpose of regression analysis. Statistical Modelling

A standard model of multiple linear regressions was built using the R software to check and determine the relationship between the outcome variable and the various factors. Coefficients were calculated and the significance was checked using the P value. The R square value and ANOVA table construction helped in interpreting the result. The final...

...Running Head: DataAnalysisDataAnalysis via SPSS
Name student
Name of institution
Table of content
Introduction 3
Aim: 4
Objectives 4
Methodology 5
Sampling 5
Survey Method: 5
Dataanalysis 5
Result: 8
Frequency Distribution 8
Cross tabulation 12
ANOVA 14
Regression 15
Conclusion: 19
Recommendation 20
References 22
Introduction
The concepts of sustainability began to form in the 1970s when there was much discussion at various forums concerning economic development and its impact on the environment and humankind. There was widespread acceptance that economic development should be sustainable, that is, having little negative impact on the earth and humans (especially, our future generations). At present, many consider this to be a theoretical ideal. In reality, sustainable development cannot be achieved within a short time frame. More likely, it is a long-term goal.
Corporate sustainability reporting originated in the early 1990s, when some companies, mainly large multinationals in many countries, began to disclose, in their annual reports, the environmental and social impacts of their operations. In the mid- 1990s, it became common for companies to report on their social performance(Dillard 2004).
Today, sustainable development is expected by society. Therefore, society also expects companies, governments, non-government...

...Introduction to DataAnalysisDataAnalysis Home
While working in lab this semester you will collect lots of data. If you are lucky your data will lead to a clear answer to the question you are investigating. At times, however, you will find your data ambiguous or, more interestingly, that something unexpected is hidden within the data. The modules on this site will guide you in exploring several important concepts in dataanalysis; these are:
the uncertainty inherent in any measurement and how that uncertainty affects your results
completing a preliminary analysis of your data by examining it visually and by characterizing it quantitatively
comparing data sets to determine if there are differences between them
modeling data using a linear regression analysis
examining data for possible outliers
Every discipline has its own terminology. If you are unsure about a term's meaning, use the link on the left for the On-Line Glossary. The data sets in these modules are Excel files. The How To... link provides reminders on using Excel. Each of these resources opens in a new browser window so you can keep them open and available while working on a module.
Uncertainty - Introduction
Suppose you are analyzing soil samples from a...

...Introduction
Our goal in this project is to carefully and accurately perform a regression analysis of the Ryanair share price to econometric parameters. We will examine several theoretically hypotheses and personally motivated hypotheses. We also will interpret the results of the study, by running appropriate tests relating to the assumed dataset. We choose Ryanair due to its recent success, especially given the downturn in the economy. It appears to go from strength to strength as its competition falters.
Having researched and consulted the model we should, we choose 4 independent variables to be used as regressors in our model. The variables we choose are as follows:
EasyJet Share Price
This is the daily adjusted closing share price for EasyJet, an airline company and a main competitor of Ryanair’s in the ‘Low Cost Carrier’ sector of the airline industry.
FTSE 100 Index
This is the daily closing price for the Financial Time Stock Exchange 100 Index.
Jet Kerosene
This is the daily closing spot price for U.S. Gulf Coast Kerosene Type Jet Fuel. This is the type of fuel that airlines use and we thought it would be a better indicator of Ryanairs fuel consumption that just a normal crude oil price.
Crude Oil Futures
This is the daily closing futures price for the NYMEX Crude Oil futures. Since Ryanair have in recent years been hedging their fuel costs, we felt that this would be an extremely relevant variable to include in our regression. Each daily...

...DataAnalysis Report
Case Study – Computers R Us
Executive summary
This report aims to figure out two basic questions, current consumers’ satisfaction and strategy that would be most potent to increase overall satisfaction. At the beginning, a survey of three parts was designed and conducted across different age groups.420 samples were collected and coding and editing of a variety of data ensued. By resorting to hypothesis test, regression model, this report found out that current consumers’ satisfaction level is much lower than management expected and two gender groups show significantly different satisfaction .Also, consumer satisfaction was similar across five age groups. In respect of determinants of consumer satisfaction, satisfaction with response time, satisfaction with the level of advice from staff at call centre, satisfaction with the level of communication and satisfaction with loyalty rewards program from consumers are all key contributors while only satisfaction with the level of communication has positive relationship with overall consumer satisfaction. Thus, in order to make effective strategy to improve overall satisfaction, Computer R Us should increase methods of communication.
Introduction
Computer R Us, whose main business focuses on manufacture and retail of computers, recently set up a division called Completecare. This division offers consumers service and repair for its computers. However, in its everyday...

...
DataAnalysis
Descriptive Statistics, Estimation, Regression & Correlation
Treatment Effects of a Drug on Cognitive Functioning in Children with Mental Retardation and ADHD
Hossam Elhowary
MATH-1016-15
Dr. Maria DeLucia
December 09, 2014
Introduction
The purpose of this survey was to investigate the cognitive effects of stimulant medication in children with mental retardation and Attention-Deficit/Hyperactivity Disorder. Twenty four children were given various dosage of a drug a placebo and 0.60mg/kg. Variable descriptions are kind of drug taken and the number of correct responses after taking of the drug. They were on each dose one week before testing. This sample obtained from the preschool delay task of Gordon Diagnostic System (Gordon, 1983). However, does higher dosage lead to higher cognitive performance?
Histogram:
Box-and-whisker plot:
Multi plot:
Summary statistics:
Column
n
Mean
Variance
Std. dev.
Std. err.
Median
Range
Min
Max
Q1
Q3
Placebo
24
39.75
128.02174
11.314669
2.3095972
36
45
26
71
33
47
0.60
24
44.708333
151.7808
12.319935
2.5147962
42.5
48
29
77
35
54
Simple linear regression results:
Dependent Variable: .60 mg/kg
Independent Variable: Placebo
.60 mg/kg = 10.091611 + 0.87086093 Placebo
Sample size: 24
R (correlation coefficient) = 0.79980157
R-sq = 0.63968255
Estimate of error standard deviation: 7.5614248
Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept...

...xxv
Data and Statistics
I
2 Descriptive Statistics: Tabular and Graphical Presentations
3 Descriptive Statistics: Numerical Measures
4 Introduction to Probability
21
67
117
5 Discrete Probability Distributions 153
6 Continuous Probability Distributions
187
7 Sampling and Sampling Distributions 219
8 Interval Estimation
9 Hypothesis Tests
251
283
10 Statistical Inference about Means and Proportions with Two
Populations
335
I I Inferences about Population Variances
373
12 Tests of Goodness of Fit and Independence 399
13 Analysis of Variance and Experimental Design 429
14 Simple linear Regression 489
15 Multiple Regression 555
16 Regression Analysis: Model Building 613
17 Index Numbers 659
18 Forecasting 683
ix
,
BRIEF CONTENTS
19 Non-parametric Methods
727
20 Statistical Methods for Quality Control
21 Decision Analysis
799
22 Sample Surveys (on CD)
Appendix A References and Bibliography 835
Appendix B Tables 837
Appendix C Summation Notation 867
Appendix D Answers to Even-numbered Exercises 870
Glossary 9 I I
Index 920
767
Contents
Preface and Acknowledgments xvii
About the Authors xx
Walk-through Tour xxii
Accompanying Website xxiv
Supplements xxv
J Descriptive Statistics:
Measures 67
Statistics in practice: TV audience measureme~1 69
3.2
1.2
Data sources 8
1.4...

...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...

...DataAnalysis
The first question of the set of 15 questions was about the age limit of the respondents. We collected all data from the age group starting from 15years. Most of the respondents fall into the age limit of 16-25 years which is 54% of the total respondents. 18of the 50 respondents were 26-35 years of age which is 36%.
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Q1: your most preferable Schemes when you are Thinking about a savings account?
This was the question that gives the critical information of the preference and test of consumers. 48% preferred Double Benefit Scheme which is the highest. Monthly Savings scheme is next which is 24%, Millionaire Deposit scheme20%, Others 8%
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Q2: NBL provide clientele services to all the customers alike.
On this particular question we have used four point scales. Out of 50 respondents 32 of them moderately agreed which is 64%, 10 of them agreed which is 20%, 5of them strongly agreed which is 10%, 3 of them disagreed which is 6%
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Q3: Is the Interest Rate Investment potential of the bank Satisfactory?
This is about benefit customers are getting from the Bank in terms of investment in the short them or long term. 37 of the respondents that are 74% of them told that they are satisfied. 10 of the respondents are very satisfied which is 20%. 3 of them are dissatisfied which is 6%.
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Q4:...