Hi, and welcome!
Please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? Using a reprex, complete with representative data will attract quicker and more answers. Also, please see homework policy
Coming up to speed on R at the same time as assimilating the mysteries of statistics is challenging.
For the R part, two suggestions.
-
R for Data Science is a great introduction to
R. In addition to the free linked version, it's well worth the cost to buy the physical version. -
Think of
Ras school algebra writ large: f(x) = y.Rpresents to the user as functions, f that take one or more arguments, x, and return values, y. (This is also key to understanding how to read thehelppages.) And everything inRis anobject, meaning that g(f(x)) is possible.
The dataset is an object that contains the two other objects, frequency and reaction time.
The first question is how to invoke them:
dataset$frequency
I'll call the two of them x and y. In fact you might even want to create temporary variables
x <- dataset$frequency
(Think of temporary variables as training wheels while coming up to speed.)
Given x and y, what functions are available to produce the three results called for? There are a lot of choices, but here's one using the built-in mtcars dataset.
fit <- lm(mpg ~ wt, data = mtcars)
summary(fit)
#>
#> Call:
#> lm(formula = mpg ~ wt, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -4.5432 -2.3647 -0.1252 1.4096 6.8727
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
#> wt -5.3445 0.5591 -9.559 1.29e-10 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.046 on 30 degrees of freedom
#> Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
#> F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10
Created on 2020-03-30 by the reprex package (v0.3.0)
I have a brief explainer here