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Melihat Dunia Dengan Data Sebagai Sebuah Investasi

# LINEAR REGRESSION REVIEWS MATERIAL FOR APPLIED R APPLICATION

Good morning,..
How are you, guys...??

Current jendela statistik would like to learn english, therefore this time writing about linear regression in english.
Okay, let's us learn together bro.

this morning, jendela statistik would like to invite learning together about  linear regression.
Okay...

First :

"What is linear regression????"

JD :
"Linear regression analysis is one of the basic and commonly used in the prediction data.
Estimation of linear regression is used to describe and explain the relationship between
two continuous (quantitative) variabel by fitting a linear equation to observed data".

Variabel X is regarded as the predictor, explanatory or independent variabel.
Variabel y is regarded as the response, outcome or dependent variabel.

Second :

Types of relationships

Before proceeding, we must clarify what types of relationships deterministic (or functional) and relationships statistical from data observed.

Relationships deterministic (or functional) is a relationship that has a definitely relationship.
Here are some examples of other deterministic relationships:

Boyle's Law: For a constant temperature, P = a/V, where P = pressure, a = constant for each gas, and V = volume of gas.

Relationships statistical is a relationship containing uncertainty (probabilistic), in which the relationship between the variables is not perfect.
Here are some examples of other statistical relationships:

Height and weight — as height increases, you'd expect weight to increase, but not perfectly.

and then, to detect early the strength of the relationship between two variables, We can use scatterplot.

Third :

Model a linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

as for the several stages to note in linear regression analysis:

Stages 1

analyzing the correlation and directionality of the data.

Stages 2

estimating the model.

Stages 3

evaluating the validity and usefulness of the model (satisfy the assumption best linear unbiased estimator (BLUE))

Four :

Three things resulting from regression analysis

1. used to identify the strength of the effect that the independent variable(s) have on a dependent variable.
2. used to forecast effects or impacts of changes.  That is regression analysis helps us to understand how much will the dependent variable change, when we change one or more independent variables.
3. regression analysis predicts trends and future values.  The regression analysis can be used to get point estimates.

There are saveral linear regression anlysis based on type of data:

A. Simple linear regression
1 dependent variable (interval or ratio), 1 independent variable (interval or ratio or dichotomous)

B. Multiple linear regression
1 dependent variable (interval or ratio) , 2 or more independent variables (interval or ratio or dichotomous)

C. Logistic regression
1 dependent variable (binary), 2or more independent variable(s) (interval or ratio or dichotomous)

D. Ordinal regression
1 dependent variable (ordinal), 1or more independent variable(s) (nominal or dichotomous)

E. Multinominal regression
1 dependent variable (nominal), 1or more independent variable(s) (interval or ratio or dichotomous)

Some assumptions for several linear regression analysis:

A. Simple and Multiple linear regression

Linear relationship
Multivariate normality
No or little multicollinearity
No auto-correlation
Homoscedasticity

B. Logistic regression

Logistic regression does not make many of the key assumptions of linear regression and general linear models that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, and measurement level.

Okay, so let's study statistical relationships between response variable y and predictor variable x!