5.1 Activity: Crazy in love (part 2): Sample size and population size.4.8 Activity: Comparing statistical estimates.4.6.1 Quantification of Uncertainty: Margin of Error.4.6 Compatibility intervals and margin of error.4.5 Activity: Monday breakups revisited.4.4 Activity: Validity evidence: Studies of Peanut Allergies.4.2 External Validity Evidence and Random Sampling.4 Random sampling, generalization, and statistical estimation.3.11 Review activity: Gender coding and expectations.3.9.2 Drawing causal inferences from observational studies.3.9.1 Hypothesis tests for observational studies.3.9 Observational Studies vs. Experiments.3.7 Activity: Internal validity evidence.3.6.1 Example: Does sleep deprivation cause an decrease in performance?.3.5 Activity: Stereotypes and creativity.3.2 Random assignment and experimental variation.3 Comparing two groups: Experiments, observational studies, and causation (internal validity).2.16 Review activity: Racial disparities in police stops.2.14.3 Other things that statistical significance can’t tell us.2.14.2 Statistical significance vs. practical significance.
2.12.1 Adjustment for Simulation Results.2.10 A closer look at statistical hypothesis testing.2.7.1 Simulation Process for Evaluating Hypotheses.
2.7 Introduction to statistical hypothesis testing.2.6 Activity: Montana political parties.2.5.1 How many trials do you need to run?.2.4.3 Automating the simulation process.2.4.2 Monte Carlo Simulation 2: Generating a Sample of Students.2.4 Activity: Building Monte Carlo simulations.2.3.3 Monte Carlo Simulation in Practice.2.3.2 Monte Carlo Simulation Assumptions.2.3.1 Example of a Monte Carlo Simulation Study.2 Introduction to modeling, simulation, and hypothesis testing.1.8.2 Summarizing a liner assocation in a statistical world.1.8.1 Summarizing a liner assocation in a mathematical world.1.7.3 Describing statistical association.1 Introduction to data, variation, and distributions.Statistical Thinking: A Simulation Approach to Modeling Uncertainty (STAT 216 edition).In the image there is the 'best' fit line and a line that just barely fits the points. In my example, uncertainty of k is 1.5 and in n it's 6. I'm trying to find how much k and n in y=kx + n can change but still fit the data if we know uncertainty in y values. TL DR: In the picture, there is a line y=2x that's calculated using least square fit and it fits the data perfectly. So uncertainty of k is 1,5 and of n is 6. (8, 10) still fall in the uncertainty range, so they might be the right points and the line that connects those points has an equation: y = x/2 + 6, while the equation we get from not factoring in the uncertainties has equation: y=2x + 0. I'm asking if there is an algorithm in any of the popular problems that takes this in account. For example the first point (0,0) could actually be (0,6) or (0,-6) or anything in between. It's an exaggerated example, but I hope it shows what I need.ĮDIT: If I try to explain a bit more, while every point in example has a certain value of y, we pretend we don't know if it's true. But, as shown on the picture, y=x/2 match the points as well. Most functions I found would calculate the uncertainty as 0, as the points perfectly match the function y=2x.
(8, 16), but each y value has an uncertainty of 4.
How to calculate uncertainty of linear regression slope based on data uncertainty (possibly in Excel/Mathematica)?