7 Please refer to the information below for the questions in this section. Various studies have found that the slower the speed at which a web page takes to load, the more likely that someone will "bounce" and leave the page. Google has also made it public that page speed is an important factor in determining where a page is ranked in Google's search results (the faster the better). A tool called the Google PageSpeed Insights API can be used programmatically to obtain page speed-related data for a given URL/website. Some of the variables available from the API include: • performance score: A score between 0 and 100, where high scores indicate: faster page load speeds, use of best practices for web performance, and smoother user experiences. • speed_score: A measure of how long it takes, in seconds, for the contents of a web page to be visually displayed to the user. To explore the speed and performance of pages on the University of Auckland website, a random sample of 90 pages was taken from all 12747 pages available. For each web page in the sample, the URL of the page was recorded and used to obtain the performance_score and speed_score from the Google PageSpeed Insights API. This sample data was then used to carry out the analysis shown below. Inference of performance score versus speed score: Linear Trend Coefficients with 95% Confidence Intervals - Intercept Estimate 97.465 Lower 83.761 Upper 111.17 -11.559 p-value < 2e -16 - Speed_score Estimate -15.575 Lower -19.592. Upper -11.559. P-value 1.8e-11 p-values for the null hypothesis of no association, He: beta = 0 the linear correlation is -0.63 7(e) The analysis provided includes output from iNZight Lite for a test for no association (linear relationship) between performance_score and speed_score. In no more than two sentences, explain why it doesn't really make sense to conduct this test, making sure you specifically refer to information provided about the data/variables and the null hypothesis for this test.