Question

Have to make a linear regression graph with this data showing that when Real Madrid's best player is there (2017-18) they have higher attendance than when he left (2018-19):

2018-2019      
Game   Real Madrid Attendance   Real Madrid Stadium Capacity
1   48346   81044
2   59255   81044
3   68034   81044
4   78562   81044
5   63762   81044
6   68120   81044
7   69653   81044
8   55229   81044
9   53412   81044
10   68507   81044
      
2017-2018      
Game   Real Madrid Attendance   Real Madrid Stadium Capacity
1   61739   81044
2   67789   81044
3   61757   81044
4   71205   81044
5   63705   81044
6   63326   81044
7   75671   81044
8   76924   81044
9   80737   81044
10   63477   81044

I found my equation: y=-0.187x+76121 with an R-squared of 0.0208

QUESTION:

I have no idea what this equation tells me in relation to my data though. Doesn't this r-squared mean that there is only a 2% correlation?

Also, is this what the y-intercept and slope mean?:

Slope: With every increase in attendance in 2017-2018, there is a decrease of -0.187 in 2018-2019.

Y-intercept: When Ronaldo is on Real Madrid in 2017-2018, attendance is 76,121.

Answer 1

The fitted equation is y=-0.187x+76121.

In relation to the data it tells that

Y-intercept : it can be interpreted as when the attendance in 2017-2018 is 0, then the attendance in 2018-2019 will be 76121.

Slope : it can be interpreted as for an increase of unit value of attendance in 2017-2018, there is a decrease of 0.187 in 2018-2019.

R-squared = 0.0208

By definition, R-squared is a measure of testing how close the data is to the fitted regression line,

In this case, we can interpret it as 2% of the response variable variation is explained by our fitted linear model.

This in turn explains that there is a very low percentage of explanation or your response variable is not explained well by the model.