Dissertation Help

Dissertation consulting is very often a necessity in order to help students who face the difficulties of selecting a topic, writing methods, understanding content, understanding complex formulas and understanding techniques. Students can find help if they seek the help of dissertation consulting. Dissertation consulting provides assistance in various statistical topics used in the fields of business, nursing and medicine, and psychology.

Statistics Solutions is the country’s leader in dissertation consulting. Contact Statistics Solutions for a free 30-minute consultation.

Dissertation consulting helps students in the fields of business, nursing and medicine, and psychology as students often face problems in submitting their dissertation on statistical topics. In business, for example, suppose the student is working on a market research project. Dissertation consulting can provide help on the complete overview of market research theory. Dissertation consulting can also provide information on various statistical techniques that need to be used to do the market research. If the student is using cluster analysis in his or her project, then Dissertation consulting can provide information on cluster analysis. Dissertation consulting sheds light on various usages of cluster analysis in market research. This includes why consumers buy certain products and the benefits sought from these products. Dissertation consulting tells students that Cluster Analysis can identify the homogeneous groups of buyers, can formulate a problem, can select a distance measure, can select a clustering procedure, can decide the number of clusters, and can interpret the profile clusters. Additionally, dissertation consulting helps with the validity of clustering done through Cluster Analysis. Clearly, dissertation consulting informs the students on different types of clustering procedures in the analysis. These are hierarchical, non hierarchical, or two step procedures.

In psychology, if a student is working on a project based on partial correlation, dissertation consulting can provide information about the complete overview of partial correlation theory. Dissertation consulting can help students gather information that tells the student that partial correlation is nothing but the measure of association between the two variables— while controlling or adjusting the effect of one or more additional variables. Dissertation consulting provides information on certain terminologies of partial correlation. These terminologies include control variables, order of correlation, spurious correlation, etc. Dissertation consulting provides information about the usefulness of partial correlation. It tells students that partial correlation is quiet common in cases of small models, like models with one control variable or sometimes with two or three control variables. Dissertation consulting gives information about various assumptions of partial correlation. These assumptions include the assumption that in a linear relationship, data should be interval in nature, etc.

In the medical field of nursing, laboratory research and medicine, dissertation consulting provides information that tells the researcher that clinical trials can be done only when a sufficient amount of information has been gathered on the quality of the drug that is to be tested. Dissertation consulting explains that epidemiology is the process of studying the factors affecting the health and illness of the population of a particular region. Dissertation consulting also advises the researcher that this process serves in the interest of public health, and medicines can be taken for prevention.

Dissertation consulting consists of people who have attained their doctorates in respective fields. These people include professors and lecturers. Dissertation consulting does not do the work for the student, however. Rather, dissertation consulting assists the student so that they are equipped with everything they need to succeed.

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If you are going to make your career in the sphere of business, you should be a knowledgeable and serious person. Only people like that can survive in the world of business.

Still, you have some time before you enter this tough world. While studying, you will have to complete a lot of assignments dealing with different business matters.
If you are looking for some information on writing a business research proposal, most likely, you need to write a business research paper.

Your business research proposal can tell a lot about you as a future businessman. Many things can reveal your personal traits: your rationale arguments in the business research proposal, the methods you are going to use for making your research. Still, there is one more thing that tells a lot about your personality and professionalism: it is the language you use in your business research proposals.

Sometimes, students use inappropriate words and phrases in their business research proposals. They are using some general phrases or meaningless statements that do not demonstrate their proficiency at all . So, these students fail to get good grades for their business research proposals, since they do not sound impressive enough.

This is why we would like to give you some phrases that you should avoid in your business research proposal.

• I believe/feel/think – actually, this does not sound strong or convincing enough. Besides, your committee members are waiting for some scientific statements, instead of your personal feelings.
• I hope – this phrase also does not show your strong intention to do something. In your business research proposal, you have to show the confidence in your actions.
• I will try – do not try, but do. Avoid this phrase in your paper!

So, you see that your business research proposal has to demonstrate your proficiency and your confidence in your own abilities.

Logistic regression is the extension of simple linear regression. Simple Linear regression is a statistical technique that is used to learn about the relationship between the dependent and independent variables. In Linear regression, dependent and independent variables are continuous in nature. For example, we could apply it to sale and marketing expenditure, where we want to predict sales based on marketing expenditure. Where the dependent variable(s) is dichotomous or binary in nature, we cannot use simple linear regression. Logistic regression is the statistical technique used to predict the relationship between predictors and predicted variables where the dependent variable is binary. Furthermore, where our dependent variable has two categories, we use binary logistic regression. If our dependent variable has more than two categories, it will be necessary to use multinomial logistic regression, whereas if our dependent variable is ordinal in nature, we use ordinal logistic regression.

In logistic regression, we assume one reference category with which we compare other variables for the probability of the occurrence of specific ‘events’ by fitting a logistic curve.

Like other regression techniques, logistic regression involves the use of two hypotheses:

1. A Null hypothesis: null hypothesis beta coefficient is equal to zero, and,

2. Alternative hypothesis: Alternative hypothesis assumes that beta coefficient is not equal to zero.

Logistic regression does not require that the relationship between the dependent variable and independent variable(s) be linear. Also, logistic regression does not require the error term to be normally distributed. Logistic regression assumes that the independent variables are interval scaled or binary in nature. However, logistic regression does not require the variance between the categorical variables. In logistic regression, normality is also not required. However, logistic regression does assume the absence of outliers.

There are some key differences in the methodologies and processes involved in simple vs. logistic regression. In the case of simple regression, ANOVA is used to evaluate the overall model fitness. Furthermore, R-square is used to evaluate the variance, as explained by the independent variable. Cox and Snell’s R², Nagelkerke’s R², McFadden’s R², Pseudo-R² are alternatives to the R-square in logistic regression. Furthermore, we use the t-test to assess the significance of individual variables where simple regression is concerned. However, in the case of logistic regression, we use the Wald statistic to assess the significance of the independent variables. Instead of simple beta, exponential beta is used in logistic regression as the independent coefficient. Exponential beta provides an odd ratio for the dependent variable based on the independent variables. This essentially is a probability of an event occurring vs. not occurring.

Rule of thumb (Peruzzi et al, 1996) recommends that to estimate the logistic regression function, a minimum of 10 cases per independent variable is required to achieve reliable and meaningful results. For instance, where 10 independent variables are concerned, a minimum sample size of 100 with at least 10 cases per variable (once you take missing values and outliers into account) are permissible.

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SPSS

SPSS is a popular statistical analysis software package, which stands for Statistical Package for Social Sciences. It is one of the more popular tools in contemporary statistical analysis due to its easy to use Graphical user interface, although it offers a wide range of capabilities ranging from add-on modules to add-on packages such as Amos and Clementine. SPSS was first developed in 1968 and has since been used extensively in industry and university research applications.

SPSS, like many other tools out there such as E-views, Stata, MStat and SAS, offers the basic capability of descriptive statistics, regression and other related tools in the base package, to serve its most common applications. In addition, through various add on modules, it can cater to the more advanced needs of high end multivariate analysis, neural networks, conjoint analysis, etc. it offers the convenience of automating several tasks such as data cleansing and organizing, along with creating charts and other types of output. It also allows for automation of tasks such as data coding, missing values analysis and import/export (from tools such as excel).

The SPSS screen offers two viewing modes; one with the data entry screen where entered or imported data are displayed. The labels row, unlike in excel, is displayed separately as a grayed out area. The editing of the variables themselves takes place in the other display called the ‘Variable View’. has two view options one is data view where data entry occur. The second view is variable view where we can see the properties of the variable(s), including name, variable type (string, date, numeric, etc.), variable length (width of the column), label, alignment, etc. as an analyst, one of the key advantages of SPSS is the ability to simply open an excel file in its own screen and edit information, without having to go through a complex import/export process. It also has the inherent windows properties such as cut, copy, paste, find, replace, etc., which makes it easy for a non-SPSS user to gain familiarity with the system, particularly if one has experience using MS Office tools.

The key selling point of SPSS is its expansive data analysis options. A wide range of data analysis functions can be performed using SPSS, including hypothesis testing, frequencies, crosstabs, T-test, ANOVA, correlation, regression analysis (linear as well nonlinear), cluster analysis, factor analysis, discriminant analysis, missing value analysis, time series forecasting etc. however, what makes it even better is how these functions are automated to the point that one need simply select the relevant variables and corresponding applications for output and analysis (where necessary), and SPSS does the rest. In the context of SPSS, it’s important to mention that using AMOS and Clementine, two of its’ most popular add-on packages (not modules), one can access the high end functionality within SPSS. While Amos is used for Structured Equation modeling and Path analysis, Clementine is a high end data mining package. Amos is probably one of the simplest and easiest to use Path analysis softwares available. Each chain of variables can be dynamically ‘graphed’ without going into programming, with the results available near-on-the-fly. Clementine offers

There are several other packages in the market which are strong competition to SPSS in terms of functionality. Despite being a powerful software, SPSS is not without its shortcomings. For instance, when it comes to time series analysis, SPSS offers limited capabilities. Similarly, MATLAB is a powerful mathematical package used where programming needs are extensive.

SPSS vs. SAS

SAS is another leading statistical package with extensive programming capabilities. Unlike SPSS, SAS does not offer the easy to use point-and-click interface as extensively, although for programming needs, SAS is considered a more powerful tool. It was preferred over SPSS historically due to the ease of programming, which in SPSS was considered far more complex and difficult. However, the modern versions of SPSS command a lot more respect in terms of programming capability.

Time series analysis is another function which is much more extensive in SAS. However, SAS offers the flexibility to perform a variety of functions which may or may not be possible through SPSS.

For SPSS assistance click here!