Height of the bar corresponding to a bin is equal to

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height of the bar corresponding to a bin is equal to the number of data points in the bin (that is the number of data points with a value within the range of the interval). In an Excel histogram, each bin is labeled by the value of the upper boundary of the bin’s range. For example, in a histogram with three bins (each of width 1), labeled 1, 2, and 3, the bin labeled 2 contains all observations greater than 1 and less than or equal to 2. See bin. I
independent variableA variable that is presumed to be related to a dependent variable. In a regression model, independent variables can be used to help predict the value of the dependent variable. A regression model seeks to find the best-fit linear relationship between a dependent variable and one or more independent variables. interceptThe intersection of a line or curve with an axis on a graph. The y-intercept of the regression line y = a + bx is a, the value of y when x=0. A straight line is completely described by its y-intercept and its slope. interquartile rangeThe difference between the upper quartile (the 75th percentile) and lower quartile (the 25th percentile). The interquartile range contains the middle 50% of the observations in a given data set. K kurtosisA measure of the flatness or sharpness of a distribution. A flat distribution with thick tails has a low or negative kurtosis; a very sharp distribution, with thin tails and a sharp rise to the peak, has a large, positive kurtosis. L lagged variableA type of independent variable often used in a regression analysis. When data are collected as a time series, a regression analysis is often performed by analyzing values of the dependent with independent variables from the same time period. However, if researchers hypothesize that there is a relationship between the dependent variable and values of an independent variable from a previous time period, may include a “lagged variable”, that is, and independent variable based on data from a previous time period. linear regressionA specific form of regression analysis that examines the linear relationship between a dependent variable and one or more independent variables. Linear regression analysis identifies the “best fit line,” the line that minimizes the sum of squared error terms between the observed values in the sample and the predicted values that lie on the regression line. This best-fit line is called the regression line. M mean
The most common statistic used to describe the center of the values in a data set. The mean is also known as the average. For a distribution that has discrete values, the mean is equal to sum of the values of all the data points in the set, divided by the number of data points. mean square errorThe mean square error is an average of the squared errors. For a regression, the mean square error equals the residual sum of squares (that is, the sum of squared errors) divided by the residual degrees of freedom (that is, the number of observations minus the number of independent variables
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