Three basic levels of measurement are nominal, ordinal, and interval/interval-ratio.
It can be though more often it is a variable on the interval scale (when looking for trends over time).
When you have independent events which have a constant probability of occurrence over an interval of space or time.
Independent events with a constant probability of occurrence over a fixed interval of time (or space).
Technically is an ordinal level measurement - because the options imply a hierarchy (i.e low to high levels of your variable of interest), but we cannot say that the difference between each level is precisely the same as you would be able to with an interval measurement. There is some controversy over this though, and it is still often used like an interval measurement in statistical tests, although this might not really be appropriate.
· Dependent variable ( student's academic achievement ) : It depend on the way that we use it to write the score (if we write it as a letter it will be an ordinal ,but if we write it as number it will be an interval). · Independent variable ( intelligent ) : Interval, · Independent variable ( attention ) : Interval,
· Dependent variable ( student's academic achievement ) : It depend on the way that we use it to write the score (if we write it as a letter it will be an ordinal ,but if we write it as number it will be an interval). · Independent variable ( intelligent ) : Interval, · Independent variable ( attention ) : Interval,
Three basic levels of measurement are nominal, ordinal, and interval/interval-ratio.
The independent variable in ANOVA must be categorical (either nominal or ordinal). The dependent variable must be scale (either interval or ratio). However, it is possible to recode scale variables to categorical and vice versa in order to perform ANOVA. While this is a common practice in many social sciences, it is controversial. I have also seen studies where ordinal data is treated as scale in ANOVA. Personally, I do not endorse either practice as they are tailoring the data to fit the test instead of the proper method of selecting a test that fits the data.
Statistical estimates cannot be exact: there is a degree of uncertainty associated with any statistical estimate. A confidence interval is a range such that the estimated value belongs to the confidence interval with the stated probability.
Each time the independent variable is changed, the dependent variable is measured. If the independent variable is time, the experimenter chooses an appropriate interval between measurements. This same methodology is used whenever the independent variable is not something the experimenter actually has in his control.
It can be though more often it is a variable on the interval scale (when looking for trends over time).
Both dependent and independent variables must be either a measure or a count. When you collect the data the independent variable often (but not always) is a whole number. As an example: A plant grew 1.5" the first day, 1.3" the second day... The growth (in inches) is plotted on the Y-axis and is dependent on the time (days) interval over which the growth rate was measured. Days plot on the X-axis as the independent variable.
Both dependent and independent variables must be either a measure or a count. When you collect the data the independent variable often (but not always) is a whole number. As an example: A plant grew 1.5" the first day, 1.3" the second day... The growth (in inches) is plotted on the Y-axis and is dependent on the time (days) interval over which the growth rate was measured. Days plot on the X-axis as the independent variable.
A statistical estimate is an estimation of population based on one or many data samples of a group. There are two types of estimates: point and interval.
No. The width of the confidence interval depends on the confidence level. The width of the confidence interval increases as the degree of confidence demanded from the statistical test increases.
Answers.com says it is: A statistical range with a specified probability that a given parameter lies within the range. I think that means, just how confident you are that your statistical analysis is correct.