# Student having issues with p-value and t-value for relationship between two variables

Good evening everyone! I'm a student working on a project and I am hitting a wall with one of the questions.

I don't imagine it is a difficult problem, I just have no experience in either RStudio or statistics in general, so I've managed to struggle.

I have a dataset that contains information about a lexical decision task in which people were timed in their responses to certain words. Frequency in this data set represents the number of times a word occurs.

The task at hand is : Calculate the correlation coefficient, t-value, and p-value for the relationship between frequency and reaction time in this dataset.

The issue I am having is, due to my inexperience across the board, I do not know where to start working towards this.

I apologise for my question formatting, this is my first post into this community and there may be significant information missing. Let me know if more information is needed and I can provide it

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.

1. 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.

2. Think of `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, `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

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