M9 Discussion Assignment Example | Topics and Well Written Essays - 500 words

M9 Discussion - Assignment ExampleEverything we learned close innocent elongated regression is a special case of multiple regression. Multiple regression is involve when a single-predictor model is inadequate to describe the true relationship between the chemical reaction variable y and its potential predictors (x1, x2, x3 . . .). Adding predictors is more than a matter of improving the fit.A multiple regression is used to place linear relationship between a response variable y and more than one explanatory variable x. In multiple regression, more than one explanatory variable are used to inform or predict a single response variable. The multiple regression model assumes that the mean of the response variable y depends on p explanatory variables according to a linear function y = 0 + 1x1 + 1x2 ++ 1xp. In this case, the mean response is not observed, as the observed values of y transfigure about their means. However, we can think of sub populations of responses, each correspon ding to a particular set of values for all in all of the explanatory variables, and in each subpopulation, y varies normally with a mean given by the population regression equation. The regression model assumes that the standard deviation of the responses is same in all subpopulations.A put downistic regression is used when the response variable has only two possible values such as success or failure, live or die, acceptable or not. Logistic regressions work with odds kinda than proportions. The odds are simply the ratio of the proportions for the two possible outcomes. The logistic regression model relates the log of the odds to the explanatory variable. A logistic regression models the log odds as a linear function of the explanatory variable, which is given by the equation log odds = 0 + 1x.A simple linear regression is a flexible way of analyzing linear relationships between two quantitative variables. A key assumption for simple linear regression model is that the deviation s from the model fit