Greetings,

Could someone, please, help with this exercise in R programming? The data for this exercise is below. Thank you

## Load the ggplot2 library

library(ggplot2)

## Fit a linear model using the `age` variable as the predictor and `earn` as the outcome

age_lm <- ___

## View the summary of your model using `summary()`

___

## Creating predictions using `predict()`

age_predict_df <- data.frame(earn = predict(___, ___), age=___)

## Plot the predictions against the original data

ggplot(data = ___, aes(y = ___, x = ___)) +

geom_point(color='blue') +

geom_line(color='red',data = ___, aes(y=___, x=___))

mean_earn <- mean(heights_df$earn)

## Corrected Sum of Squares Total

sst <- sum((mean_earn - heights_df$earn)^2)

## Corrected Sum of Squares for Model

ssm <- sum((mean_earn - age_predict_df$earn)^2)

## Residuals

residuals <- heights_df$earn - age_predict_df$earn

## Sum of Squares for Error

sse <- sum(residuals^2)

## R Squared R^2 = SSM\SST

r_squared <- ___

## Number of observations

n <- ___

## Number of regression parameters

p <- 2

## Corrected Degrees of Freedom for Model (p-1)

dfm <- ___

## Degrees of Freedom for Error (n-p)

dfe <- ___

## Corrected Degrees of Freedom Total: DFT = n - 1

dft <- ___

## Mean of Squares for Model: MSM = SSM / DFM

msm <- ___

## Mean of Squares for Error: MSE = SSE / DFE

mse <- ___

## Mean of Squares Total: MST = SST / DFT

mst <- ___

## F Statistic F = MSM/MSE

f_score <- ___

## Adjusted R Squared R2 = 1 - (1 - R2)(n - 1) / (n - p)

adjusted_r_squared <- ___

## Calculate the p-value from the F distribution

p_value <- pf(f_score, dfm, dft, lower.tail=F)

Data: Height.csv

earn	height	sex	ed	age	race
50000	74.42444	male	16	45	white
60000	65.53754	female	16	58	white
30000	63.6292	female	16	29	white
50000	63.10856	female	16	91	other
51000	63.40248	female	17	39	white
9000	64.39951	female	15	26	white
29000	61.65633	female	12	49	white
32000	72.69854	male	17	46	white
2000	72.03947	male	15	21	hispanic
27000	72.23493	male	12	26	white
6530	69.51215	male	16	65	white
30000	68.03161	male	11	34	white
12000	67.55693	male	12	27	white
12000	65.43059	female	12	51	white
22000	65.66285	female	16	35	white
17000	67.75877	male	12	58	white
40000	68.35184	female	14	29	white
44000	69.60957	male	13	44	white
7000	64.18457	female	12	55	black
53000	73.07461	male	13	35	black
5000	62.37553	female	13	51	white
14000	63.02393	female	14	21	white
5500	67.2299	male	14	22	white
40000	65.55111	female	12	41	white
34000	72.07965	male	12	45	white
10000	63.09113	female	12	35	black
27000	64.32355	female	16	60	white
50000	71.64285	male	16	38	white
41000	76.79309	male	16	33	white
15000	63.89391	female	14	25	white
25000	63.80262	female	12	33	white
75000	71.59223	male	17	39	white
27000	67.52196	male	17	31	white
12000	64.39435	female	12	26	white
7500	61.17822	female	14	78	white
30000	66.98388	female	14	31	black
21000	65.31646	female	12	57	white
27000	63.57419	female	14	26	white
3000	66.611	female	15	65	white
25000	64.91176	female	12	30	white
24000	64.78968	female	12	41	white
32000	66.93769	female	18	29	white
10000	68.17281	female	17	30	white
11000	60.45066	female	12	21	hispanic
18700	64.79325	female	13	32	white
20000	61.81492	female	12	29	white
3500	71.57215	male	10	18	white
13000	67.31441	male	8	56	black
25000	69.89987	male	12	65	white
21000	69.7617	male	17	41	white
34000	67.74647	female	17	49	white
6000	60.19022	female	12	65	white
17000	71.0065	male	12	28	white
35000	71.1668	male	12	32	white
4000	72.73563	male	13	18	white
14000	68.13822	female	14	55	white
10000	66.37981	female	12	57	white
25000	69.23278	male	16	29	white
16000	63.27394	female	14	27	white
16000	61.82776	male	14	28	hispanic
16500	64.22121	female	14	43	white
4000	63.84127	female	9	68	white
3840	66.97477	female	9	52	white