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Mathematics

1 answer:

Ket [755]3 years ago

3 0

If we are to write this equation in slope-intercept form, it will be in y = mx + b, where m is the slope of the line and b is the y intercept. We need then to find the slope of the line using 2 points on the line and filling in the slope formula to find the slope. One of the points we can use is (0, 3) which is also the y intercept. The y-intercept is found where x = 0. Where x = 0, y = 3. So b = 3. Now for the slope we will use (0,3) and (4,4): $m= \frac{4-3}{4-0} = \frac{1}{4}$ . Using that m value and that b value we have the equation $y= \frac{1}{4}x+3$ . There you go!

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Answers are a. 10^-6 b. 10^-5 c.10^5 d.10^6

Harman [31]

Answer:

the answer is 10^-6 power would be the answer based off the pattern.

Step-by-step explanation:

8 0

4 years ago

The coefficient of determination is mathematically equivalent to the correlation coefficient omega-squared eta-squared both omeg

kvv77 [185]

Answer:

The equivalent expression is $\eta^2$ , because respect to $\omega^2$ the formula is different.

Step-by-step explanation:

The equivalent expression is $\eta^2$ , because respect to $\omega^2$ the formula is different.

$\eta^2$ "describes the ratio of variance explained in the dependent variable by a predictor while controlling for other predictors", similar to the determination coefficient.

One property is that "is a biased estimator of the variance explained by the model in the population" when we are just estimating the size effect.

And other porty is that "measures the variance explained of the sample and not the population" and many times overestimate the effect size.

The formula for is given by:

$\eta^2 = \frac{SSR}{SST}$

Where SST represent the total sum of squares and SSR the sum squares for the regression.

$\omega^2$

This one is a similar estimator to the determination coefficient, but is a estimator with lower bias. And analyze again the variance explained like the determination coefficient and the $\eta^2$

Since have lower bias than $\eta^2$ is prefered, but sometimes is not easy to calculate it. The formula to calculate is given by:

$\omega^2=\frac{SS_{treatment}-(df_{treatment}*MS_{error})}{SS_{total}+MS_{error}}$