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Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to a scientific, industrial, or social problem, it is conventional, to begin with, a statistical population or a statistical model to be studied. Populations can be diverse topics such as “all people living in a country” or “every atom composing a crystal”. Statistics deals with every aspect of data including the planning of data collection in terms of the design of surveys and experiments. When statistical data has been collected, it is often required to present the data in a suitable form for interpretation. An important part of this is the establishment of summary statistics for the data. These summary statistics are generally presented in tabular or graphical form. The main purpose of summary statistics is to reduce the data to a more manageable form, while still retaining as much information as possible.

Statistical data analysis is often used to investigate relationships between variables. In statistics, a relationship between variables has often termed an association. The statistical analysis of associations usually involves the use of contingency tables, correlation analysis, and regression analysis. Contingency tables are used to investigate the association between two categorical variables. In a contingency table, each row represents a different category of one variable, while each column represents a different category of the other variable. The number of items in a particular category is called the marginal total. The proportion of items in a particular category is called the relative frequency. The chi-square statistic is often used to test for the independence of the two variables in a contingency table. Assignment hippo provides best Statistics Assignment Help.

Correlation analysis is used to investigate the relationship between two quantitative variables. In correlation analysis, the variables are not classified into different categories. The relationship between the variables is measured by the correlation coefficient. The correlation coefficient can take values between -1 and +1. A value of 0 indicates that there is no relationship between the variables. A value of +1 indicates a perfect positive relationship, while a value of -1 indicates a perfect negative relationship.

Regression analysis is used to investigate the relationship between a dependent variable and one or more independent variables. In a regression analysis, the dependent variable is called the criterion variable, while the independent variables are called the predictor variables. The coefficients of the independent variables are used to predict the values of the dependent variable. The coefficients can be interpreted as the amount of change in the dependent variable per unit change in the independent variable. Students who are stuck with their math assignments can get math assignment help online. There are many different types of statistical data, and the choice of which type to use depends on the research question. The most common types of data are interval data, ordinal data, and nominal data. Interval data are data that can be ordered from least to greatest. Examples of interval data include temperature, IQ scores, and test scores. Ordinal data are data that can be ordered from least to greatest, but the differences between the data are not equal. Examples of ordinal data include Likert scale data and survey responses. Chi-square tests are used to investigate the relationship between two categorical variables. Chi-square tests are less powerful than t-tests and ANOVA, but they are more widely used because they are easier to understand.

Once the appropriate test has been selected, the next step is to calculate the test statistic. If you are looking for economics assignment help, assignment help offering best academic services all over the globe. A test statistic is a number that is used to decide whether the null hypothesis should be rejected or not. The test statistic is calculated from the data, and it depends on the type of test that is being used. After the test statistic has been calculated, the next step is to compare it to the critical value. The critical value is a number that is used to decide whether the null hypothesis should be rejected or not. The critical value is calculated from the data, and it depends on the type of test that is being used. If the test statistic is greater than the critical value, then the null hypothesis is rejected. If the test statistic is less than the critical value, then the null hypothesis is not rejected. The decision to reject or not reject the null hypothesis is based on the p-value. The p-value is the probability of observing a test statistic that is as extreme as the one that was actually observed, given that the null hypothesis is true. If the p-value is less than the alpha level, then the null hypothesis is rejected. If the p-value is greater than the alpha level, then the null hypothesis is not rejected. The results of a statistical test should be interpreted in the context of the research question. The results of a statistical test can not be used to support a conclusion about causality.