Question

Explain the difference between a variable and a statistic in the context of a regression Define the terms "causal effect" and "ideal experiment". Explain the difference betwe descriptive statistics, inferential statistics, and causal inference.

Answer 1

Variable is a data or quantity or factor on which regression is done.dependent variable is the response or outcome variable.Independent variables are denoted by X and dependent variable are denoted by Y in regression.Regression deals with the phenomena of change in the dependent variable correlating with the change in independent variable.variables can be of 3rd type, called as controlled variable.statistics is a method used by regression in different arenas like financing and investing and other attempts which establishes relations between X(independent variables) and Y(dependent variable). statistics is a entire gamuet where regression analysis is used to find different trends in data collected.
something which has happened or is now happening, something which has occurred or is now occuring based on something is coined as Casual Effect.casual effect can be defined in simple terms as C being an outcome of D and D has occurred because of C, depending on how it has worked.
Ideal experiment refers to a type of experiments which are controlled or uncontrolled are none.experiments works as per initiative and result are because of the effects of a variable tested.Ideal experiments maximize the validity of Internal factors and external factors.
Descriptive statistics gives description of data.
Inferential statistics allows for predicting results from data.
Casual inferences comprises of a process which draws a conclusion about a connection which is casual and which is base on the conditions of an effect that has occurred.Descriptive statistics generally provide description about population by tables or graphs. Inferential statistics owes prediction or inferences based on collected data.
when There is a change in cause the variable effect response is analysed by the casual inferences.