Univariate analysis is one of the simplest methods to analyse data. “Uni” means “one”, thus, in other words, your data only has one variable. It does not like regression where you need to find causes or relationships. The main purpose of the univariate analysis is to summarise, describe and finds patterns of the data.
Understanding the Term Variable in Univariate Analysis
If we talk about the variables in univariate analysis, it can be explained as a subset or condition that the data comes under. You can say it as a category. Let’s take an example - your analysis can be in the form of a variable such as “age”, “height” or “weight”. However, it is just one variable. In case, if there is more than one variable at a time it will be bivariate analysis and if 3 or more variables exist, we call it multivariate analysis.
Our experts offering statistics assignment help have provided a frequency table that shows one variable:
In the above table, there could be more than one variable. For instance - you can include the variable like age, location, sex, etc. Also, make a separate column for all these variables.
Sample for Statistics Assignment: Design & Statistics II - Univariate Results For The Dependent Variable
Approach To Solve Part 1 Of Above Statistics Assignment
Here are the following points that a student can consider to successfully complete Part 1 of Design & Statistics II - Univariate Results For The Dependent Variable assessment;
- As per the statistics assignment experts, you must conduct three individual simple regression analyses for each variable
- Follow the eight steps of regression analysis
- Provide relevant values and statistics in the APA format
From the above points, we come to know that you must have the knowledge of regression analysis such as potential variables/features, collecting data from variables, how to check the relationship between response and predictor variable, knowledge of multi-collinearity test, simple linear regression, ways to best use of non-redundant predictor variables, Analyse one or more models through t-statistics. p-value and f-value and R2 (R squared) or adjusted R2.
The second part of the statistics assignment includes three answers which need to be answered accurately. Approaches to be followed are discussed below:
- You must conduct a multiple regression analysis for all three variables.
- No need to conduct analysis again for the three-predictor model. You can simply re-run the multiple regression analysis based on the results of question 7.
- Also, provide relevant analysis to test the assumptions of the two-predictor model.
So, these were the few approaches or methods to solve the statistics assignment. In case, if you need further help, simply contact Online Assignment Expert.
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