pl find enlcosed Ms word attachment for past exam paper solutions of  a Foreign Universities in UK.Time Series Analysis Using R

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r project

Introduction: This project contain exploratory data analysis using r and Ggobi sofrware. r software is more powerful tools for analysis the data. Using this we can analyze all kind of descriptive and statistical inference. Addition to this also we can use model like regression analysis.

In this we used employee data for analyzing the salary of associates. The salary of associates depends on so many factors. In this data we included variables are education(in years), salary begin, Previous experience (in years), Job time, minority.

The statistical tools used,

1. Mean, median, Mode

2. cross tab analysis

3. Scatter plots

4. Correlation Analysis

5.  Regression Analysis.

The below is the list of r option we used

#To read the text file into R console we used read.table option.

Emp=read.table(“D:/CARS/Project-cars/Ranadeeb/emp.txt”,header=TRUE)

# Attach the tables imported we need to use attach option.

#Once we attach option used, then we can give Individual variable name for analysis

#and plotting the graph.

attach(Emp)

#To find the column names we need to use the colnames() option. This will display

the column names of the attached file.

colnames(Emp)

#The variables prsent in the emp data are

#1. Educ ( this is the number of years of education)

#2. jobcat ( this will measure the category of job they are doing. 1 reprsent

#trainee. 2 will reprsent Team Members. 3rd will reprsent Manaager Level.

#3. Salary

#4.Salbegin (this salary when started job first time)

#5.Jobtime

#6. Prevexp (It is the experience of the candiates)

#7. Minority (0 means no and 1 means yes)

#The summary will give the overall descrptive statistics for all quantitative data.

#The descrptive statistics are given below.

#1. Minimum Value: This will give the minimum valve for the given data.

#2.1st Qu: the first quarter the 25th percentile data.

#3.Median:It is the middle value of the data set when data is arranged in either

#in ascending or descending order.

#4. Mean: It is the sum of the observation divided by total number of observation.

#5. 3rd Qu: this will give the 75% of the data.

#6. Max: It will give the maximum valuve of data set.

summary(Emp)

summary(salary)

# To get the cross tabulation we can use table option. this will give the cross tabunlation of jobcategory versus minority.

table(jobcat,minority)

# To plot the scatter plot we can use the command plot(x1,x2).

plot(salary,salbegin)

# there is a postive correlation between salary and salarybegin.

# AS salarybegin is high then salary of current also be high.

plot(educ,salary)

#when we plot the correlation between educ and salary, there is a posstive correlation

#between these two variable.

plot (educ,salbegin)

# there is a postive correlation between education and salary of begin.

#To combinaly see the all the correaltion  using scater plot, then we can use pair option.

pairs(Emp)

#to calculate the correlation between all variable then we can use cor option.

#the correlation is the degree of association between two variable.

# The correlation is classfied into three types.

#1. Posstive correlation (Where correlation is close to one)

#2. Negative correlation (where correlation is close to – one)

#3. Zero correlation ( where correlation is close to zero)

cor(Emp)

#As the name suggests, in simple terms correlation is the relationship between 2 variables.

#Correlation is defined as the “degree of strength of association between 2 variables”.

# Here 2 variables, does not mean 2 values in the same variable, but two different variables.

# Correlation explains the degree of the relation the 2 variables share among themselves, but does not explain the cause and effect of each other.

#Usually, correlation is required to be found between at least one dependent variable and another independent variable

#and its relation or association on the dependent variable.

#The researcher looks at things that already exist and determines if and in what way those things are related to each other.

#The purpose of doing correlations is to allow us to make a prediction about one variable based on what we know about another variable.

#Correlation co-efficient is denoted by small r . Always co-efficient of correlation value r ranges between -1 and +1.

# To find out the correlation, we usually do a scatter plot of both x values (independent variable) and the y values (dependent variable).

#A correlation tells us that the two variables are related, but we cannot say anything about whether one caused the other.

#This method does not allow us to come to any conclusions about cause and effect. Correlation is not Causation!

#regression Analysis

#Regression is said to be said an extension of Correlation.

#Regression is the “Functional relationship between dependent and independent variables”.

#Regression gives you the cause & effect relationship of the variables which is left incomplete by Correlation.

#We regress the pattern of the future occurrence of the variables based on the actual prior values.

#Regression forms a basis for future estimating & Forecasting while taking into account #the cause & effect of the relationship of the variables.

#Definition:-  Regression analysis is the process of constructing a mathematical #/statistical model or function that can be used to predict or determine one variable by #other variables.

#Regression involves two or more variables in which one variable is predicted (called the dependent variable) and designated as Y,

# The variable/s which help the prediction are called Independent variables  or #Explanatory variables and are designated as x1   x2   x3  …. xn.

#The values of x variables will be known or have actually happened, based on which we will be predicting the Y variable.

#Here, only a straight line relationship between the dependent and independent  variables is assumed.

# regression analysis was carried out for employee data

# dependent variable is salary and Indpendent variable is salary_begin, Education, #Previous_Experience.

lm(salary~educ+salbegin+prevexp)

#The above model used to run the linear regression of salary as the dependent variable #and education, salary begin and previous experiance was considered as a indpendent variable. The model shows that,

#salary=-3.661.51+735.956*educ+1.1749*salbegin-16.730*prevexp

This 9 Step On-Page Search Engine Optimization Guide will help you optimize your website pages, so they will have the best chance to get found by your target market. The Guide outlines the most important factors to consider when optimizing each page on your site. These steps are listed based on importance, so don’t skip a step. Also, remember that creating new optimized content on a weekly basis is critical to achieving long-term success with SEO.
Step 1: Choose Keywords
Read the page’s content and identify two (2) keywords that are most relevant to the overall page content. Choose one (1) primary keyword relevant to the page’s content and one variation of that keyword (e.g. plural variation or two closely related keywords) per page. If you can’t identify one primary keyword for a page, you’ll need to create new website pages to separate the different content. If it’s not clear to you what page is about, then your visitors and the search engines won’t be able to understand the page either.
Step 2: Page Title
The page title appears as the blue, bolded, underlined text on a Google search results page, and also on the top left the browser bar. The page title should follow these guidelines:
 Be under 70 characters with no more than two long-tail keywords per page title
 The primary keyword should appear first
 Each keyword phrase should be separated by pipes (|)
 Each page title on your website should be unique
 Except for your homepage and contact us page, each page title should NOT include your business name
Step 3: Meta Description
The meta description appears on a Google search results page under the Page Title. The meta description helps people decide whether to click on your result, or a result above or below you. Think of it as a call to action. The meta description should follow these guidelines:
 Be under 150 characters (but not under 100 characters; take advantage of the space you have)
 Incorporate the primary keyword and at least one secondary keyword
 Provide a valuable, compelling reason for why someone should visit the page
 Include keywords in a conversational format; don’t just cram in keywords for the sake of listing them
Step 4: URL
The website page’s URL should include the primary keyword. Each word in the URL should be separated using dashes (-). e.g. www.examplesite.com/inbound-marketing-software
Step 5: Heading Tags
The page should have one H1 heading tag that incorporates the primary keyword, and should align with the page title and the URL or the page. This H1 tag should appear at the top of the page and should be the first thing people see when they arrive on a page.
Step 6: Page Content
Use your primary keyword a few times throughout the page’s content. Don’t overthink keyword density or placement, you should mention them naturally. Try to bold or underline the keyword at least once. This has an effect on how relevant the keyword is to the page. Also mention the secondary keywords when you can.
Step 7: Add a Call to Action
Every website page, including your blog, should have at least one call to action above the page’s fold (Don’t make your website visitor scroll down to see the call to action). Calls to action can help SEO by creating an internal link on your website to a specific landing page. Most calls to action are images; therefore you can optimize the image filename and alt text for the primary keyword you’re targeting on the page (see step 9).
Step 8: Internal Links
If you mention the primary keyword of this page on other pages within your site, then link to this page using the primary keyword as the anchor text. For example, you should link to a page about inbound marketing software using the anchor text “inbound marketing software.” To make sure this is completed, take a moment to create one (1) or two (2) links on related pages that link back to the page you’re optimizing.
Step 9: Images
Any images used on the page should be optimized so that search engines can “read” the image. Optimize the most prominent image on the page using the primary keyword, and then use the primary and secondary keywords for any other images. Images can be optimized in two ways:
 File name: Each word should be separated with dashes (-), e.g. inbound-marketing-software.jpg
 ALT text: The alt text should match the file name, without dashes, e.g. Inbound Marketing Software
If you are unable to change image file name or if it’s too time consuming, then only change the most prominent images’ alt text using the primary keyword.
Meta Keywords
They aren’t part of Google’s or Bing’s search algorithm, so I’m not including them as a step. However, still use your primary and secondary keyword in the page’s meta keywords. Smaller search engines still might use them in their algorithm, but major search engines do not use them. HubSpot does not recommend you optimize your meta keywords.
Repeat these 9 steps for each of your website pages.

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Solution_Unit_4_Balanced_score_card

FINANCIAL MODELLING

Financial modelling refers to performing a set of calculations or process to analyse the financial information.  This process is carried with some pre-decided objective and intentions. Usually it is done to assess performance and financial health of the organization or share and business valuation or capital project analysis or portfolio performance and risk assessment.

Financial modelling can be done with the help of developed software or it can be done manually through excel. The process or fix set of process as may be applicable to the desired objective is already designed and interlinked in the software or excel file for unknown variables. Once variables are inserted into to the model it will facilitate the desired output for intended objective.

Process or set of calculation will depend on various situation and objective and accordingly applicable tools will be used for financial modelling. Like for portfolio performance and risk assessment applicable tools will be CAPM model, Market model, intrinsic / fair value method, α, β, SML, SD, Correlation, covariance, probability etc.fcff_3 oct

Pl find enclosed attached solutions of computations finance . for any module help or final project help assistance for code developement in VBA or Matlab, pl do get in touch with us at  http://www.findtutoronline.net/blogAssQF250814AssQF250814

Banks create Credit.Credit creation by commercial banks India & most of the federal reserves in US,UK, & all european countires from a small deposit how Commercials banks & federal reserves create credit  that can be mentioned within the short space of an Essay.

Commercial bank knows that every Investors deposited money in commercials banks cannot be able to withdrawn whole portion of their deposits in a single day. let say Mr.X deposited $10000 in Bank of America. As per rules of Reserve bank of India & federal reserves of US,UK, there is minimum deposits should be kept as reserves with the commercial banks/Federal reserve banks.

Let say 10% of Total deposit of $10000 is equivalent to $1000. So, out of $10000, commercial banks can retain $1000 as reserves & let say that particular investors withdrawn $9000 out of $10000. so, Commercial banks can give loan to others that $1000 to some third party. that third party take this $1000 & put in his personal bank account. In that case, again this $1000 taken by third pary & deposit in some other banks wherein this person can  withdraw $900 &  banks kept $100 as minimum reserves. Again , Banks gives loan to some other person $90 out of $100 & kept $10 as reserves. This process will go on until deposit amout is so small that it cannot generate a fresh loan.

The above example i can set by algebra formulae like below :

Sn=   a/1-r     where  a = first term = $10000, r=10%. where Sn is the summation at the end of n years.

Therefore ,Sn= 10000/1-10%=$11,111

So, from $10000  deposits , it went to $11,111, so there is an increase of $1,111 & so on which commercial banks credit creation.So, from $10000 to $11,111.

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