Question
Consider the model equation Yij = p. + Pi +Tj + eij which is the equation of an additive model, without interaction between the block and treatment factors.True or False: The reason there is no interaction term is that it has been empirically shown that there is almost never any interaction in data arising from this design:
Consider the model equation Yij = p. + Pi +Tj + eij which is the equation of an additive model, without interaction between the block and treatment factors. True or False: The reason there is no interaction term is that it has been empirically shown that there is almost never any interaction in data arising from this design:
Answers
True or False. A correlation coefficient of 0.96 would indicate that a linear model for the data is certainly appropriate. Justify your answer.
We want to answer true or false for this question. When testing a hypothesis via the P value approach, if the P value is large, then reject the null hypothesis H. Now to determine whether or not the statement is true or false weeds or make sure that we understand the P value approach the hypothesis testing, so remember that we reject the null hypothesis H not when P is essential to alpha. P. Here is a P value and Alpha, as I've noted, is the confidence level. So when you have a P value that's less than or equal to alpha, that is smaller than your alpha, You reject H. nine. So already we see that a large p value is problematic for this assumption, but when we factored to account The alpha is usually some value like .01.05.1. We typically don't go beyond 10 confidence. The answer becomes even clearer. That is this is false. Because if we have a P value that is large, it's probably going to be larger than alpha, since alpha is traditionally small, and therefore we wouldn't want to reject H, not when the P value is large in order to meet the conditions described here.
In the description of two variables. We work with this value known as our, which is called our coefficient of correlation. And this question is asking us if it is true that if this value, this coefficient of correlation is close to zero, is it true to say that there's no relationship relationship between the variables? Well, what we know of this correlation coefficient is that it ranges from negative 12 positive one with zero line in the middle, and what zero tells us is that there is no relationship. So if this value of ours anywhere but zero, we can't say that there's no relationship. So even if it's really close to zero, there's still some relationship. It maybe slightly positive Or slightly negative. And an example of that would be suppose we had a coefficient of correlation which is equal to 0.2. Well, this is obviously a positive number. So we're seeing some sort of positive relationship between the two variables, but it's not quite strong. We could say that there's a weak relationship, but unless that coefficient of correlation is actually equal to zero, we can't say that there's no relations. So the answer to this question would be no, it is not true.
This question tells us that data recharge positively correlated our modern Becca using I mean, you're modeling as compared to quadratic motors. No, You understand what this statement means? Let us first look at what positively correlated data means. So if we draw oh, set off access and if we draws get a plot with different data points positively correlated. Data will be data which appear to lie own or alone. Nine were the data values are constantly increasing, as opposed to this negative correlation would have been If the data values on this point, they big raising no for a function like this, they're saying that the new morning is better. No, this is true because as far as the data given here is concerned, there is no indication off the data reducing all the data values reducing. So if we model it with a linear function, it will keep arising. But if we function if remodel it using a quadratic model, then quadratic model will at some point come down work. And there is no indication off this actually happening with the given data. Therefore, linear model is definitely better for positively correlated data. So this statement is true
Okay, When we look at a galaxy and model, we can see by what the graph looks like that there's not an X intercept. And then when we look at the equation, we realized we could rewrite the equation as why equals a over E to the X minus b quantity squared, divided by C. And we realized that the only way for the Y coordinate to be zero is if a was zero and a being zero means. All we would have is just a flat horizontal line. Y equals zero, and that wouldn't fit with the galaxy and model being a Bel Kher. So that's not possible. Therefore, it's not possible for this to have an X intercept, so the statement was true.