You may get more ideas from wikipedia under regression analysis. You can do a regression analysis with as little as 2 x,y points- but is it meaningful? Requirements for valid or meaningful relationships can be subjective. However, in my opinion, if meaningful relationships are to be created using regression analysis, the following are important: a) The independent variable should have values that are independent (no relation exists between them). b) There should be a good rational or experimental basis for identifying the independent variables and the resultant dependent variable. c) Sufficient data should be collected in a controlled environment to identify the relationship. d) The validity of the relationship should easy to identify both visually and by numbers (see "goodness of fit" tests).
frequency distribution regression analysis measure of central tendency
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
Alpha is not generally used in regression analysis. Alpha in statistics is the significance level. If you use a TI 83/84 calculator, an "a" will be used for constants, but do not confuse a for alpha. Some may, in derivation formulas for regression, use alpha as a variable so that is the only item I can think of where alpha could be used in regression analysis. Added: Though not generally relevant when using regression for prediction, the significance level is important when using regression for hypothesis testing. Also, alpha is frequently and incorrectly confused with the constant "a" in the regression equation Y = a + bX where a is the intercept of the regression line and the Y axis. By convention, Greek letters in statistics are sometimes used when referring to a population rather than a sample. But unless you are explicitly referring to a population prediction, and your field of study follows this convention, "alpha" is not the correct term here.
A time series is a sequence of data points, measured typically at successive points in time spaced at uniformed time intervals. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics. Regression analysis is a statistical process for estimating the relationship among variables.
In regression analysis , heteroscedasticity means a situation in which the variance of the dependent variable varies across the data. Heteroscedasticity complicates analysis because many methods in regression analysis are based on an assumption of equal variance.
H. L. Koul has written: 'Weighted empiricals and linear models' -- subject(s): Autoregression (Statistics), Linear models (Statistics), Regression analysis, Sampling (Statistics) 'Weighted empirical processes in dynamic nonlinear models' -- subject(s): Autoregression (Statistics), Linear models (Statistics), Regression analysis, Sampling (Statistics)
Dean P. Foster has written: 'Business analysis using regression' -- subject(s): Regression analysis, Statistical methods, Social sciences, Commercial statistics 'Basic business statistics' -- subject(s): Commercial statistics, Case studies
frequency distribution regression analysis measure of central tendency
R. L. Plackett has written: 'Statistical reasoning' -- subject(s): Mathematical statistics 'Principles of regression analysis' -- subject(s): Regression analysis
Frank E. Harrell has written: 'Regression modeling strategies' -- subject(s): Regression analysis, Linear models (Statistics)
The word regression is a noun. It cannot be an adjective. When it is paired with another noun, it is a noun adjunct.Examples:"In statistics, regression analysis refers to techniques for modeling and analyzing several variables""Regression techniques are used by psychologists."
this is for a class in Math-233-statistics
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
Esa I. Uusipaikka has written: 'Confidence intervals in generalized regression models' -- subject(s): Regression analysis, Linear models (Mathematics), Statistics, Confidence intervals
Irwin Guttman has written: 'Magnitudinal effects in the normal multivariate model' -- subject(s): Bayesian statistical decision theory, Multivariate analysis 'Theoretical considerations of the multivariate Von Mises-Fischer distribution' -- subject(s): Mathematical statistics, Multivariate analysis 'Bayesian power' -- subject(s): Bayesian statistical decision theory, Statistical hypothesis testing 'Bayesian assessment of assumptions of regression analysis' -- subject(s): Bayesian statistical decision theory, Linear models (Statistics), Regression analysis 'Linear models' -- subject(s): Linear models (Statistics) 'Bayesian method of detecting change point in regression and growth curve models' -- subject(s): Bayesian statistical decision theory, Regression analysis 'Spuriosity and outliers in circular data' -- subject(s): Outliers (Statistics) 'Introductory engineering statistics' -- subject(s): Engineering, Statistical methods
Before undertaking regression analysis, one must decide on which variables will be analysed. Regression analysis is predicting a variable from a number of other variables.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com