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## Descriptive Statistics Homework help in SAS

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## ANOVA Assignment Help in SAS

The question most students ask themselves when they have an assignment that they don’t understand is, who will do my assignment in SAS? An ANOVA, which is an Analysis of variance determines, if an experiment or survey results are significant. Groups are tested to find out if there is a difference. The student conducting the research need to figure out if they need to accept the null hypothesis or accept the alternate hypothesis. For example, e.g. in psychiatry there are three types or treatment, medication, counselling, and injection. Therefore, if the researcher needs to know which method works best then ANOVA is vital.

Depending on the research design, there are three ways one can use ANOVA.

**The one way**

This method has only one independent variable. The independent variable can have more than two categories to compare from.

**Two way**

The two way method has two independent variables. Interactions indicate that differences are not uniform across all the categories of the independent variables.

**The N-way**

This method allows the researcher to use more than two independent variables

To determine if the results of the analysis of a data sample are reflective of the entire population a hypothesis testing is used.The null hypothesis for ANOVA indicates that there is no significant difference among the groups while the alternative hypothesis tends to assume that at least one significant difference exists among the groups. The T-test through statistical examination analyses the means of two samples. The F test assists in identifying the model that best fits a population from which data samples has been picked from.

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## CHI SQUARE Assignment Help in SAS

Chi square, also known as a “goodness of fit figure”, is a model of nonparametric test that draw comparisons of more than two variables from a random data. It measures how expectations compare to results from random, exclusive, and raw data drawn from a large enough samples.

Developed by Prof. Karl Pearson in 1900, Chi Square enables one to find if experimenteviations are either by chance or due to the inadequacy of the theory to fit the observed data

A chi squared test which is a hypothesis statistical test, is used to test the due chance of an observed distribution. For a chi square test to be used, certain conditions must be met;

- The observations on samples should be independent
- Data should be given in original units
- No theoretical cell frequency should be small
- The cell frequency constraints should be linear

A Chi Square distribution has the following applications;

- Goodness-of- fit test shows if an observed distribution conforms with any distribution
- Test for association widely used for nominal data.

One of the disadvantages of Chi-square test is that the determination of sample size is not readily available because calculation of confidence intervals is not permitted.

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