- x_id – Id of the page
- ismobile – page visited is by mobile or not
Here, we want to know the impact of average server response time, average server connection time, average redirection time, average domain look up time, average page download time and average page load time on the bounce rate. So, we have rearranged the data set and removed x_id, country, page path, page title, entrances, page views, exits and bounces from the data set and appended bouncerate after calculating it. Now data set contains following parameters.
>Model_1 <- lm(bouncerate ~ avgServerResponseTime + avgServerConnectionTime + avgRedirectionTime + avgPageDownloadTime +avgDomainLookupTime + avgPageLoadTime)style="text-align: justify">We have generated the model nicely, but we are interested to know the relationships between bounce rate and and time components. Let’s check summary of the model.
Output Call: lm(formula = bouncerate ~ avgServerResponseTime + avgServerConnectionTime + avgRedirectionTime + avgPageDownloadTime + avgDomainLookupTime + avgPageLoadTime) Residuals: Min 1Q Median 3Q Max -98.276 -19.816 -1.169 19.805 107.705 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 49.10686 0.32862 149.435 < 2e-16 *** avgServerResponseTime -0.85724 0.17154 -4.997 5.93e-07 *** avgServerConnectionTime 2.02335 0.55566 3.641 0.000273 *** avgRedirectionTime -0.37822 0.06368 -5.939 2.97e-09 *** avgPageDownloadTime 0.31975 0.12172 2.627 0.008631 ** avgDomainLookupTime 4.14929 0.88525 4.687 2.81e-06 *** avgPageLoadTime 0.04684 0.01896 2.470 0.013528 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 26.74 on 8481 degrees of freedom Multiple R-squared: 0.01339, Adjusted R-squared: 0.0127 F-statistic: 19.19 on 6 and 8481 DF, p-value: < 2.2e-16style="text-align: justify">Let’s understand the result. In the result, coefficients are shown in the column Estimate std. So, the equation for bounce rate becomes as below. style="text-align: justify">bouncerate = 49.107 + (-0.86)avgServerResponsetime + (2.03)avgServerconnectionTime + (-0.38)avgRedirectionTime + (0.32)avgPageDownloadTime + (4.14)avgDomainLookuptime + (.05)avgpageLoadtime style="text-align: justify">As we can see from the equation, avgDomainLookupTime impacts more on bounce rate . If avgDomainLookupTime increase by 1 unit then bounce rate increase by 4.14. At last, we succeed in identifying the relationship between bounce rate and time components of a web page using regression. style="text-align: justify">Here, we cannot say that the relationships estimated from this regression model(model_1) are perfect, because the model result is generated after model fitted to the data set(i.e. model learns from the data and then estimate coefficients values) and data set may contain some unreliable observations . It is necessary to improve the model, so we can identify the relationships of bounce rate and time components very precisely. In the title="Improving Bounce Rate Prediciton Model for Google Analytics Data" href="http://www.tatvic.com/blog/improving-bounce-rate-prediction-model-for-google-analytics-data/" >next blog, we will discuss about how to improve the model? and summary of the improved model. class="wp-about-author-containter-top" style="background-color:#FFEAA8;"> class="wp-about-author-pic"> src="http://www.tatvic.com/blog/wp-content/uploads/userphoto/14.jpg" alt="Amar Gondaliya" width="60" class="photo" />