use r-studio, please

River	Abundance	Tree.Area
7	0.34	17.2
4	0.6	20.7
3	0.9	24.1
14	1	29.8
5	1.1	25.3
1	1.2	34.4
13	1.4	23
12	1.5	40.2
9	1.8	37.9
10	2	26.4
6	2.1	40.2
11	2.3	34.4
8	2.6	41.3
15	2.9	43.6
2	3.4	47.1

12) Create a scatter plot with salmon abundance on the xaxis and the tree basal area on the y- axis. Label the graph, label each axis appropriately, and make it colorful. (3 points) 13) Find the line of best fit (regression line) for the salmon and tree area data using the equations found in the lab slides 14 and 15. a) First, calculate and interpret r. (Hint: dene n, x, and y rst.) (4 points) b) Next, calculate the slope (b1) for the regression line. (2 points) 0) Now calculate the intercept (b0) for the regression line. (2 points) (1) Finally, write out the regression equation in terms of the variables Abundance and Tree.Area. (2 points). e) Based on the regression equation above, calculate the predicted Tree.Area for salmon abundance values of: (4 points) i) 1.9 ii) 2.7 14) Interpret the slope in the context of the original question. What does it tell you about the potential effect of salmon on tree density? Have you proven this to be true using this analysis? Is there another potential explanation? (3 points) 15) Now we want to calculate the coefcient of determination (r2). Remember that the r2 is calculated as the explained variation divided by the total variation. a) Start by rst calculating the predicted value of y (j!) for each value of x. (2 points) 15) Now we want to calculate the coefficient of determination (r2). Remember that the r2 is calculated as the explained variation divided by the total variation. a) Start by first calculating the predicted value of y (y) for each value of x. (2 points) b) Next calculate the total variation as the sum of the squared differences between each observed y (y;) and the mean of y (y). (2 points) c) Then calculate the explained variation as the sum of the squared differences between each predicted y (y) and the mean of y (y). (2 points) d) Finally calculate the coefficient of determination: (2 points) e) Lastly, let's let RStudio make our lives easier by using the Im() function.i) State your null and alternative hypotheses for the slope (b1). (2 points) ii) Now use the lm() and summary() functions in RStudio to perform a linear regression of the salmon abundance and tree area data. Paste your code and the resulting output. (2 points) 16) You'll see that not only do you get the same estimates for the intercept (b0) and slope (b1), but also the standard errors, tvalues, and P values for those estimates. Is our slope estimate signicant (assume an alpha of 0.05)? How can you tell? What does that mean in terms of our original question? (3 points)