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The importance of SPSS in making statistical analysis is growing day by day. In Dissertation or thesis SPSS sometimes plays an important tool to perform analyses for statistical data. We provide SPSS help to researcher to complete their dissertation. The powerful statistical tool like SPSS helps you to give accurate results for your analysis. Our experts will help you to learn how to use SPSS in analyzing data. SPSS is a very simple yet flexible statistical software and can be learnt easily . Examples will be provided to you to make your understanding better.
Aid to Multivariate Statistics and Multicollinearity in Multiple Regressions:
Multivariate statistics takes into account more than one statistical .Multivariate statistics is an application of multivariate analysis. The statistics which deals with two variables is known as bivariate statistics is a special case of multivariate statistics. Bivariate statistics include simple linear regression and correlation. MANOVA, Multivariate regression analysis, PCA, Factor analysis, Canonical correlation analysis, Correspondence analysis , Multidimensional scaling , Discriminant analysis, Clustering systems, etc. are examples of multivariate statistics. You will be assisted by us to determine the appropriate multiple statistics in your dissertation.
Now coming to Multiple regression we will see that it will include more than one predictor. Including multiple variables in the model helps you to predict the variable of interest. However sometimes results may be inconsistent. Both the independent variables gives the same information if they are highly correlated. Neither of them will be a significant contribution to the model if we include the other one. Both will however contribute a lot. It will be a worse fit if both variables are removed. Thus overall the model will fit the data well , however none of the independent variables will make a significant contribution if added last to the model. If this thing happens the concept of multicollinearity arises where the independent variables are collinear. To evaluate multicollinearity you have to examine how well every independent variable is predicted from the other ones. This is measured in terms of individual R_{2} value and Variance Inflation Factor (VIF). For any independent variable a high R_{2} value and Variance Inflation Factor (VIF) tells us that the fit is affected by multicollinearity We will help you on this aspect.
Determining sample size is to determine or select the number of observations in a sample. It is important to choose a sample size because choosing the correct sample size makes the inference about the population from the sample accurate. Sample size in a study is determined on the basis of the expenditure on collecting data and a sufficient statistical power is required.We will help you to calculate the sample size needed for your thesis.
Hypothesis test gives us an idea of the probability of result when the null hypothesis is true.
Generally when the sample size is large it makes the parameter estimates more accurate and helps us to find the unknown thing we are looking for. If we look harder we will find more information. During the design stage power analysis is performed. Through the study we can make conclusion whether the null hypothesis is true or not. When the value of sample size and effect size is given we can determine the power. It may happen that your power becomes insufficient, in such cases steps should be taken to increase the power .However it should be kept in mind increasing sample size does not increase the power. So, we will assist you in this matter.
In Biostatistics statistical techniques are applied to scientific research based on health. Statistical techniques are applied to health related fields like public health medicines, biology etc. The application of statistical technique helps to study the matters related to health.You will be assisted by our consultants to analyze your medical research projects.
It refers to the size of the effect in the population. The larger the effect size more easier is to find the thing we are looking for. In a statistical population an effect size refers to the measure of the strength of relationship that two variables possesses. It gives us an idea about the magnitude of relationship between two variables without telling us that the relationship in the data reflects true relationship in the population. So effect size complement inferential statistics for example p value. Thus the effect size is very important in your study. Our consultants will help you to calculate the effect size.
