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.
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.
R as school algebra writ large: f(x) = y.
R presents 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 the
help pages.) And everything in
R is an
object, meaning that g(f(x)) is possible.
The dataset is an object that contains the two other objects,
The first question is how to invoke them:
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
fit <- lm(mpg ~ wt, data = mtcars)
#> lm(formula = mpg ~ wt, data = mtcars)
#> Min 1Q Median 3Q Max
#> -4.5432 -2.3647 -0.1252 1.4096 6.8727
#> 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