R Day #1 - Simple Linear Regression - Slope

UCLA Notes were very useful.

Linear Regression Model - Representing mean of response variable as function using slope and intercept parameters. Can be used for predictions. I have earlier used moving average algorithm for forecasting.

• Simple Linear Regression - Explanatory variable is 1 (Dependent variable is 1)
• Multivariate Linear Regression - Number of Explanatory variables more than 1

Good Summary of Data Quality Issues were summarized

• Data-entry errors
• Missing values
• Outliers
• Unusual (e.g. asymmetric) distributions
• Unexpected patterns

R Cookbook had good step by step examples to try out - link

Basics Maths Again

Slope - lines rate of change in the vertical direction

y = mx + b

• y = dependent variable as y depends on x
• x = independent variable
• m , b = characteristics of line
• b = y intercept where line crosses y axis

Ref - Link

Slope     = Rise / Run

              = Change in y / Change in X

Equation y = x

1 = 1

2 = 2

Slope = y/x = 2/2 = 1

Slope = y2-y1 / x2-x1

Slope > 1 tilt upwards towards y axis

Slope < 1 tilt downwards towards x axis

Ref - Link

Ref - Link

Happy Learning!!!