# Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualisation - Regression Modelling for Inferential Statistics

## 6 important questions on Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualisation - Regression Modelling for Inferential Statistics

### What is regression and what statistical purpose does it serve?

It can be used for:

- hypothesis testing (theory building): investigating potential relationships between different variables. It can reveal the strength and directions of relationships between a number of explanatory variables and the respons variable.
- Prediction/forecasting: estimating values of a response variable based on 1 or more explanatory variables. The equation is used to predict.

### What are the commonalities and differences between regression and correlation?

**Correlation**: is not concerned with te relationship between variables. It gives an estimate on the degree of association between the variables?

**regression**: attempts to describe the dependence of a respons var. on 1 (or more) explanatory vars. Implicit assumption that there is a 1-way causal effect.

### What is OLS? How does OLS determine the linear regression line?

**OLS**: Ordinary Least Squares: is a method/algorithm to identify the regression line. It leads to the mathematical expression for the estimated value of the regression line.

### List and describe the main steps to follow in developing a linear regression model?

### What are the most commonly pronounced assumptions for linear regression?

**Linearity**: linear relationship between vars.

**Independence**(of errors): the errors of the response variable are uncorrelated of each other.

**Normality**(of errors): the errors of the response variable are normally distributed

**Constant variance**(of errors): the errors of the response variable have the same variance. Assumption is invalid if resp.vars. over a wide enough range.

**Multicollinearity**: the explanatory variables are not correlated.

### What is time series? What are the main forecasting techniques for time series data?

**naïve forecast**: today's forecast is the same as yesterday's actual

**ARIMA**: very complex: combination of AutoRegressIve and Moving Average patterns

**Averaging methods**: simple average, moving average, weighted moving average,...

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