Which is better binary data or continuous data?
In general, binary data provide less information than an equivalent amount of continuous data. If you can collect continuous data, it’s the better route to take! Poisson Hypothesis Tests for Count Data Count data can have only non-negative integers (e.g., 0, 1, 2, etc.).
When to use hypothesis test with continuous data?
For example, when you measure height, weight, and temperature, you have continuous data. With continuous variables, you can use hypothesis tests to assess the mean, median, and standard deviation. When you collect continuous data, you usually get more bang for your data buck compared to discrete data.
Can a 2 proportions test be used for binary data?
Yes, you can do as you suggest assuming the respondents are different in the two quarters and assuming that the data are binary (satisfied/not satisfied). The 2 proportions test is designed for independent groups and binary data. I hope that helps even belatedly!
What are the advantages of collecting continuous data?
When you collect continuous data, you usually get more bang for your data buck compared to discrete data. The two key advantages of continuous data are that you can: Draw conclusions with a smaller sample size. Use a wider variety of analyses, which allows you to learn more.
How are binary independent variables used in regression?
Binary independent variables are also known as indicator variables and analyst frequently use them in linear regression. Typically, the 1s and 0s of an indicator variable represent the presence or absence of a characteristic.
Is it bad to use linear regression to model binary outcomes?
In conclusion, although there may be settings where using linear regression to model a binary outcome may not lead to ruin, in general it is not a good idea. Essentially doing so (usually) amounts to using the wrong tool for the job.
What happens if the outcome variable is binary?
If the outcome variable is binary this assumption is clearly violated, and so in general we might expect our inferences to be invalid. Actually things might not (necessarily) be too bad.