0
What else can I help you with?
An author is most likely to defend her choice of multiple regression statistical techniques in which section of a proposal?
An author is most likely to defend her choice of multiple regression statistical techniques in which section of a proposal?
What is the importance of correlation and regression analysis in business decision making?
Correlation and regression analysis can help business to investigate the determinants of key variables such as their sales. Variations in a companies sales are likely to be related to variation in product prices,consumers,incomes,tastes and preference's multiple regression analysis can be used to investigate the nature of this relationship and correlation analysis can be used to test the goodness of fit. Regression can also be used to estimate the trend in a time series to make forecast
What is the process of extending a graph beyond given data to make a prediction?
The first step would be to determine the nature of the relationship between the variables using the graph and any other relevant informaton. The next stage would be to use regression techniques to calculate a curve of best fit to the observations. This curve may be extended beyond the data but, the further the distance from the actual observations, the greater the likely error in the extrapolation
What do the terms frequent likely occasional seldom and unlikely used in the risk assessment matrix refer to?
They are used to determine how likely it is that something will occur. If it occurs frequently then it is a high risk situation.
What is the normal probability plot of residuals?
When you use linear regression to model the data, there will typically be some amount of error between the predicted value as calculated from your model, and each data point. These differences are called "residuals". If those residuals appear to be essentially random noise (i.e. they resemble a normal (a.k.a. "Gaussian") distribution), then that offers support that your linear model is a good one for the data. However, if your errors are not normally distributed, then they are likely correlated in some way which indicates that your model is not adequately taking into consideration some factor in your data. It could mean that your data is non-linear and that linear regression is not the appropriate modeling technique.
