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Correlation analysis is the relationship of two values. When two items are similar, they will have a high correlation. Should they differ, they will be much lower in variables.
The measure of the amount of variation in the observed values of the response variable explained by the regression is known as the coefficient of determination, denoted as ( R^2 ). This statistic quantifies the proportion of the total variability in the response variable that can be attributed to the predictor variables in the model. An ( R^2 ) value closer to 1 indicates a better fit, meaning that a larger proportion of the variance is explained by the regression model. Conversely, an ( R^2 ) value near 0 suggests that the model does not explain much of the variation.
In this context, ( s^2 ) would refer to the sample variance of the salaries of the 66 employees taken from the population of 820 employees. It is a measure of how much the salaries of these sampled employees deviate from their average salary. This sample variance provides an estimate of the variance of the population, assuming that the sample is representative.
Variance is basically the raw material of statistics. If you don't have variance (differences in scores) you don't have much to work with or for that matter you don't have much to talk or think about. Consider a test where everyone gets the same score. What does that tell you? You might have some measurement problem, wherein the test is so easy everyone aces it. Still it might be so hard that everyone gets a zero. Now consider two tests. On each everyone gets the same score. That is on test one everyone gets a 15 and on the second test everyone gets a 10. That isn't telling you much is it? Now these are extreme cases, but in general, more variance is better and less variance isn't so good.
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false- it is best to isolate them once you know how much of the material has been used. If you don't know how much is used, you can't get a variance to evaluate efficiency of how materials were used.
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Correlation analysis is the relationship of two values. When two items are similar, they will have a high correlation. Should they differ, they will be much lower in variables.
Volume is a change in how many products you sell Price is a change in how much you charge for the product
No, it is an integer number. Correlations happen among various different things. A non-zero correlation means that the things interact or depend on each other. A zero correlation means they don't. Examples: There is a positive correlation between how much you eat and how much you weigh. There is a zero correlation between the color of your car and its gas mileage. There is a positive correlation between how far the volume control is turned up and the sound pressure level that you hear. There is a negative correlation between the air temperature and the sales of sweaters.
It will be invaluable if (when) you need to calculate sample correlation coefficient, but otherwise, it has pretty much no value.
The budget for it in 2008 has been explained in the below link click it to see the results /Download/file/YWY0Nzg4ODIt
Engine size is not a direct correlation to the horsepower.
Variance is a measure of "relative to the mean, how far away does the other data fall" - it is a measure of dispersion. A high variance would indicate that your data is very much spread out over a large area (random), whereas a low variance would indicate that all your data is very similar.Standard deviation (the square root of the variance) is a measure of "on average, how far away does the data fall from the mean". It can be interpreted in a similar way to the variance, but since it is square rooted, it is less susceptible to outliers.
"Strong" is very much a subjective term. Not only that, but it depends on expectations. In economics I would consider 70% to be a strong correlation, but for physics I would want more than 95% before I called the correlation strong!
A positive correlation between two variables, say X and Y, means that if one increases, the other will too. No correlation means that they are not related. A negative correlation means that as one increases, the other decreases. Normally you will see this in studies as "Recent studies demonstrated a positive correlation between eating too much and obesity." Or, "recent studies demonstrate a negative correlation between a healthy, balanced diet and obesity".