Suppose the correlation coefficient between a baby's weight and the price of the smartphone is 0.87. Which statement must be true?

Who statement it is against all reason to suppose that this continent can long remain subject to any ectemal power?

who statement was "it is against all reason to suppose that this contient can long remain subject to any ectemal power

What are the ideal characteristics of a calibration curve?

The ideal characteristics of a calibration curve include a linear relationship between analyte concentration and response, a high correlation coefficient (R-squared value) close to 1, a wide dynamic range, and low limits of detection and quantification. Additionally, the curve should be reproducible and stable over time.

What does Supongo que no mean?

"Supongo que no" means "I guess not" in English. It implies doubt or uncertainty about a statement or situation.

What is presidential doctrine?

I suppose it is a doctrine articulated by the President. A doctrine is a statement of belief used to define policy, usually foreign policy.

suppose that g(x) = f(x-8). which statement best compares the graph of g(x) with the graph of f(x)?

Why

What do you mean when you say that the coefficient of variation has no units?

Suppose the mean of a sample is 1.72 metres, and the standard deviation of the sample is 3.44 metres. (Notice that the sample mean and the standard deviation will always have the same units.) Then the coefficient of variation will be 1.72 metres / 3.44 metres = 0.5. The units in the mean and standard deviation 'cancel out'-always.

What is multiple and partial correlation?

multiple correlation: Suppose you calculate the linear regression of a single dependent variable on more than one independent variable and that you include a mean in the linear model. The multiple correlation is analogous to the statistic that is obtainable from a linear model that includes just one independent variable. It measures the degree to which the linear model given by the linear regression is valuable as a predictor of the independent variable. For calculation details you might wish to see the wikipedia article for this statistic. partial correlation: Let's say you have a dependent variable Y and a collection of independent variables X1, X2, X3. You might for some reason be interested in the partial correlation of Y and X3. Then you would calculate the linear regression of Y on just X1 and X2. Knowing the coefficients of this linear model you would calculate the so-called residuals which would be the parts of Y unaccounted for by the model or, in other words, the differences between the Y's and the values given by b1X1 + b2X2 where b1 and b2 are the model coefficients from the regression. Now you would calculate the correlation between these residuals and the X3 values to obtain the partial correlation of X3 with Y given X1 and X2. Intuitively, we use the first regression and residual calculation to account for the explanatory power of X1 and X2. Having done that we calculate the correlation coefficient to learn whether any more explanatory power is left for X3 to 'mop up'.

Is autocorrelation function a copy of original function?

No. You probably know what a sample correlation is. This statistic is often used to measure how well a linear function of one variable predicts the value of another variable. The statistic can assume any value from -1 to 1, and the extreme values show the strongest (linear) relationship. Calculating the autocorrelation function for a time series involves doing a series of calculations that are the same as those done to obtain a sample correlation coefficient. Since these values must always be between -1 and 1 they cannot in general form a copy of the original function. Here is where the idea of copying appears. Suppose you want to calculate the 1st autocorrelation coefficient from the series v0, v1, v2, v3, v4, ... . Then calculate the sample correlation for the pairs (v1, v0), (v2, v1), (v3, v2), (v4, v3), ... Notice that it is as if you were to write down the original time series on one line and then copy it on a second line shifting it one item to the right so that the pairs needed to compute the sample correlation could be read from the columns of the two lines. The 2nd autocorrelation would be computed as if by copying the second line shifting it two places to the right and so on.

How can you tell from a scatter plot whether 2 variables have a positive correlation a negative correlation or no correlation?

Suppose the scatter plot is of a variable X on the horizontal scale and Y on the vertical scale.Find the approximate middle of the x values and call it p.Find the approximate middle of the y values and call it q.Draw horizontal and vertical lines through the point with coordinates (p, q).If you know about quadrants, skip this paragraph. The two lines through the point (p,q) divide up the plane into 4 quadrants. Quadrant I is top right. Quadrant II is top left. Quadrant III is bottom left. Quadrant IV is bottom right.If the scatter plot is mostly in quadrants I and III the correlation is positive. If mostly in quadrants II and IV the correlation is negative. Otherwise the correlation is small.Remember, though, that 0 correlation does not mean no relation. y = x2 will have 0 correlation but it is a perfectly well defined relationship!Suppose the scatter plot is of a variable X on the horizontal scale and Y on the vertical scale.Find the approximate middle of the x values and call it p.Find the approximate middle of the y values and call it q.Draw horizontal and vertical lines through the point with coordinates (p, q).If you know about quadrants, skip this paragraph. The two lines through the point (p,q) divide up the plane into 4 quadrants. Quadrant I is top right. Quadrant II is top left. Quadrant III is bottom left. Quadrant IV is bottom right.If the scatter plot is mostly in quadrants I and III the correlation is positive. If mostly in quadrants II and IV the correlation is negative. Otherwise the correlation is small.Remember, though, that 0 correlation does not mean no relation. y = x2 will have 0 correlation but it is a perfectly well defined relationship!Suppose the scatter plot is of a variable X on the horizontal scale and Y on the vertical scale.Find the approximate middle of the x values and call it p.Find the approximate middle of the y values and call it q.Draw horizontal and vertical lines through the point with coordinates (p, q).If you know about quadrants, skip this paragraph. The two lines through the point (p,q) divide up the plane into 4 quadrants. Quadrant I is top right. Quadrant II is top left. Quadrant III is bottom left. Quadrant IV is bottom right.If the scatter plot is mostly in quadrants I and III the correlation is positive. If mostly in quadrants II and IV the correlation is negative. Otherwise the correlation is small.Remember, though, that 0 correlation does not mean no relation. y = x2 will have 0 correlation but it is a perfectly well defined relationship!Suppose the scatter plot is of a variable X on the horizontal scale and Y on the vertical scale.Find the approximate middle of the x values and call it p.Find the approximate middle of the y values and call it q.Draw horizontal and vertical lines through the point with coordinates (p, q).If you know about quadrants, skip this paragraph. The two lines through the point (p,q) divide up the plane into 4 quadrants. Quadrant I is top right. Quadrant II is top left. Quadrant III is bottom left. Quadrant IV is bottom right.If the scatter plot is mostly in quadrants I and III the correlation is positive. If mostly in quadrants II and IV the correlation is negative. Otherwise the correlation is small.Remember, though, that 0 correlation does not mean no relation. y = x2 will have 0 correlation but it is a perfectly well defined relationship!

What Suppose that the allele for blue eyes in cats is recessive and your cat has blue eyes What statement can you make about your cat's genotype for eye color?

it is probably homozygone for the recessive allele.

Suppose that the allele for blue eyes in cats is recessive and your cat has blue eyes. what statement can you make about you cats genotype for eye color?

it is probably homozygous for the recessive allele

Why can one not say that correlation proves causation?

You cannot say it because it is not true.First of all, correlation simple states that two variables change so in such a way that a change in one leads to a change in the other. Changes of the same magnitude in the first variable brings about the consistent changes in the second variable. There is no way to determine whetherthe first causes the second,the second causes the first,they cause one another, orthey are both caused by an unknown third variable.A simplistic example from economics will illustrate the first three. Capital investment (spending on machinery, for example) by a company and the company's profits are positively correlated. But the direction of the causal relationship is not simple to establish. A company needs to be profitable before it can raise the money to invest. On the other hand, by investing well, it becomes more competitive and so is more profitable.As an example of the fourth type, in the UK there is a significant correlation between the sales of ice cream and swimming accidents. This is not because ice cream causes swimming accidents nor that ice cream is caused (?) by swimming accidents. The hidden variable is hot weather. People are more likely to eat ice cream. They are also more likely to go to beaches.The converse of the statement in the question is also untrue: the absence of correlation does not prove that there is no causation. Suppose you have one variable X which is defined on a the interval (-p, p) for some positive number a. And then let Y = X^2. There is clearly a perfect relationship between the two variables. However, if the X-values are symmetric, then the symmetry of the relationship ensures that the correlation coefficient is 0! No correlation but a perfect relationship.