1. ______ _____ _____ is computed by taking the sum of the absolute values of the individual forecast errors and dividing by the numbers of errors (n).
2.our job as quantitative analysts is to develop the best ____ ____ between the variable being forecast and the set of independent variable.
3.the proportion of the variability in y that is explained by the regression equation is called____ _____.
4.in addition to the central tendency of a probability distrubution most people are interested in the _____ or spread of the distribution.
5._____ usually represent probability function.
1. The term that completes the sentence is "Mean Absolute Deviation (MAD)". To compute the MAD, you would follow these steps:
- Calculate the absolute value of each individual forecast error.
- Sum up all the absolute values.
- Divide the sum by the number of errors (n).
2. The term that completes the sentence is "regression model" or "regression equation". As quantitative analysts, our task is to develop the best regression model that captures the relationship between the variable being forecast (dependent variable) and the set of independent variables.
3. The term that completes the sentence is "coefficient of determination" or "R-squared". The coefficient of determination measures the proportion of the variability in the dependent variable (y) that is explained by the regression equation. It ranges from 0 to 1, with higher values indicating a stronger relationship.
4. The term that completes the sentence is "dispersion" or "variability". Although central tendency provides information about the average value, people are often interested in the dispersion or spread of the probability distribution. Common measures of dispersion include variance, standard deviation, and range.
5. The term that completes the sentence is "probability distributions". Probability distributions typically represent the probability function for a random variable. Different types of probability distributions exist, such as normal distribution, binomial distribution, exponential distribution, etc. They describe the likelihood of different outcomes or events occurring.