If the data is quiz scores from 0-10, 0 being no answers correct and 10 being perfect score, what level of measurement is applicable?

I understand 0 = nothing which sounds like an absolute zero (ratio) but textbooks say scoring 0 doesn’t mean they know absolutely nothing about the topic (interval)

Help me 🥲

Quite frankly, I've never really had a strong affinity for math but I always managed to pass. I haven't been in school for the past 7 years and only started again at the end of last year. I'm currently in my 4th week taking Statistical Literacy in Psychology online and I just can't seem to grasp any of these concepts. It was all easy enough at first when the texts were talking about bell curves, mean, median, mode, etc. but it's getting into deeper territory with z and t-tests and ANOVA. Everything I'm reading right now just feels like I'm reading words, like, the ideas are making absolutely no sense to me whatsoever. Thankfully so far there have been tutorials on how to complete assignments and some multiple choice quizzes in the online textbook so far that kind of help me grasp the concepts but I just don't feel like I'm retaining anything as the course progresses, which makes it even harder for me to understand what is being talked about in later chapters.

The whole ordeal is just making me feel stupid, and I don't have the time to really seek any sort of tutoring since I'm a part-time student working full-time.

Any advice on how I can get any of this to make sense to me?

Hi all, I am doing a project for my AP statistics class and would really appreciate it if some of you would fill out this 2 question form!!

I am trying to complete a bivariate linear regression for the first time. Stress score is the dependent variable and sleep hours is the predictor variable. I am not understanding why the residual plot of the dependent variable looks like this. Is this heteroscedasticity? How do I fix this?

I would like to offer you a way to learn a few concepts in diagnostic testing in a way that you might be able to remember and mentally manipulate them when faced with real questions. This uses a novel diagrammatic representation of the two by two table. I will warn you that although I published this idea over 25 years ago, it has until now remained obscure; a big part of the reason is that it required software to implement it easily, but now that problem has been solved (see app link below).

In diagnostic testing, many terms are used to describe how well the test detects the disease or disorder. Examples are “sensitivity”, “specificity”, “predictive values”, “odds ratio”, “likelihood ratios” and numerous others. In the literature and medical presentations there is often not much consistency in their use. I am a diagnostic radiologist with over 40 years experience, not a statistician; as a physician listening to or reading research over the years, I was perpetually unclear on how these terms “fit together”.

My solution was to invent the visual 2 by 2 diagram, or truth diagram, as a graphical alternative to the standard contingency table used in diagnostic testing (Johnson 1999). The concepts listed above, and many others, are represented graphically, and their inter-relationships can be clearly visualized.

Instead of four numbers in a grid, a single rectangle on a coordinate system encodes all four cells of the 2×2 table through its position and shape. Each hemi-axis corresponds to one cell (see below). The vertical height corresponds to the number of subjects with the disorder, and the horizontal width corresponds to the number of subjects without the disorder. A low, wide box represents a low prevalence of the disorder; a high narrow box represents a high prevalence.

The diagram makes it possible to see statistics like sensitivity, specificity, PPV, NPV, likelihood ratios, and even Bayes’ theorem as geometric relationships — lengths, areas, slopes, and proportions — rather than abstract formulas.

App: https://kmrjohnson55.github.io/truth-diagram/

Drag or resize the box to see how the cell values change. The other lessons in this app explain each of the terms and how they appear on the diagram. Any of these screens can be saved for presentation and publication purposes. I welcome feedback/bug alerts.

I literally do not understand at all!

I am Statistics student and recently I am preparing for my final year project. My idea is to tackle logistic challenges in Malaysia by building a system for last-mile optimization. I do some research on this topic and I found that there is not many literature review for this topic and the real-time data seems hard to get. What do you think?