The least squares regression line ( also known as the least squares regression equation, or simply the regression equation) is usually the first question worth marks on a simple linear regression question. We did most of our calculations when we found the sums in an earlier video, so determining the values (coefficients) of this equation will be quick and easy!
In Part 2 of this question we are expected to first calculate several rates of return ourselves before determining the Geometric Mean. After this, we once again compare our result to the regular (arithmetic) mean, but part (d) presents us with a follow-up question that highlights a tricky rule that determines when the arithmetic mean is a BETTER choice - even when working with rates of return.
What if the distribution that we're being asked about is NOT normally distributed? ... Or what if we don't know what the distribution looks like at all? No worries! Chebyshev's theorem describes ALL distributions - including the normal distribution.
How samples work, how sample information is collected, and problems that can occur when using samples to predict population behavior.
Every decision analysis question requires that you setup three components based on the given information: decision variables, states of nature, and the payoff table. I'll show you how to identify them in the wording, and what to do when you're presented with revenues and costs instead of profits.
We shouldn't have to give up confidence in order to have a smaller (more precise) interval estimate. In this video I cover all of the other factors that we can change to affect the width of the confidence interval, and I discuss why increasing the sample size is by far the best approach.
This is a Chapter 8.1 Uniform Distribution question. To solve it, we'll need to first work out the height of the distribution: f(x), and then calculate the area under the curve over the interval 4,500 gallons - 6,500 gallons.
What good is an equation if you don't know what it means? Here I explain what the intercept and slope of the equation line mean. I do this in the context of the example that we've been following throughout these videos, but I also give you a general version of the interpretations that will work for any simple linear regression questions.