Quiz Belum Berjudul

How many types that regression have ???

• 1

• 2

• 3

• 4

before we do the regression test, we must do............

• assumption

• Box Plot Test

• Correlation

• Sobel Test

of several assumption tests below, which ones are not used in simple regression tests.......

• Linearity

• Normality

• heteroscedasticity

• Multicollinearity

The meaning of dummy variable

• categorical variables with two characteristics can be used as independent variables (predictors)

• numerical variables can be used as independent variables (predictors)

• two or more independent variables that are highly correlated

• categorical variables with more than two characteristics can be used as independent variables (predictors)

Required assumptions of pearson's product moment

• 1. 1. The continuous variables should be paired
     2. Should be a linear relationship between your the continuous variables
     3. No significant outliers
     4. Should had a normal distribution

• 1. Two continuous variables should be non-paired
  2. Should be a homogeneity relationship between your two continuous variables
  3. Significant outliers
  4. Should had a bivariate linear distribution

• 1. Two continuous variables should be paired
  2. Should be a linear relationship between your two continuous variables
  3. No significant outliers
  4. Should had a bivariate normal distribution

• 1. Two ordinal variables should be paired
  2. Should be a linear relationship between your two ordinal variables
  3. No significant outliers
  4. Should had a bivariate normal distribution

Three regression analyses are needed

• 1. 1. as the predictor variable and X as the outcome variable.
     2. as the predictor variable and Y as the outcome variable.
     3. X and Y as the predictor variables and M as the outcome variable.

• 1. 1. as the predictor variable and Y as the outcome variable.
     2. as the predictor variable and M as the outcome variable.
     3. X and M as the predictor variables and Y as the outcome variable.

• 1. 1. as the predictor variable and M as the outcome variable.
     2. as the predictor variable and Y as the outcome variable.
     3. Y and M as the predictor variables and X as the outcome variable.

• 1. 1. as the predictor variable and Y as the outcome variable.
     2. as the predictor variable and Y as the outcome variable.
     3. Y and M as the predictor variables and X as the outcome variable.

Dion wants to predict bicycle purchases based on price and a consumer's healthy lifestyle. Perform multiple regression analysis and interpret the results

• Var X = price and consumer's healthy lifestyle
  Var Y = bicycle purchases

• Var X = bicycle purchases
  Var Y = price and consumer's healthy lifestyle

• Var X = price
  Var Y = consumer's healthy lifestyle

• Var X = price and bicycle purchases
  Var Y = consumer's healthy lifestyle

Full mediation is

• when the mediating variable is only responsible for a part of the relationship between independent & dependent variables.

• there will still be a relationship between the independent and dependent variables

• when the entire relationship between the independent & dependent variables is through the mediator variable.

• when the entire relationship between the independent & dependent variables is through the mediation variable.

R = 0,932 and R square = 0,868. So, we can indicate that

• R 0,932 is a very strong association between variables and R square 0,868 is the dependent variables into a regression model explain 86,6% of the variability of the independent variable

• R 0,932 is a very small association between variables and R square 0,868 is the independent variables into a correlation model explain 86,6% of the variability of the dependent variable

• R 0,868 is a very medium association between variables and R square 0,932 is the independent variables into a regression model explain 93,2% of the variability of the dependent variable

• R 0,932 is a very strong association between variables and R square 0,868 is the independent variables into a regression model explain 86,6% of the variability of the dependent variable

MULTICOLLINEARITY DIAGNOSIS:

• VIF > 10
  TOLERANCE > 0,1

• VIF < 10
  TOLERANCE > 0,1

• VIF < 10
  TOLERANCE < 0,1

• VIF < 0,1
  TOLERANCE > 10