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Remember, this involves three things:\n+\n+a) Looking at the raw data values.\n+a) Computing summary statistics.\n+a) Creating data visualizations.\n+\n+What can you say about the relationship between age and teaching scores based on this exploration?\n+\n+**Solution**: \n+\n+- Looking at the raw data values: \n+```{r}\n+glimpse(evals_ch5)\n+```\n+\n+- Computing summary statistics:\n+```{r}\n+skim_with(numeric = list(hist = NULL), integer = list(hist = NULL))\n+evals_ch5 %>%\n+  select(score, age) %>%\n+  skim()\n+```\n+(Note that for formatting purposes, the inline histogram that is usually printed with skim() has been removed. This can be done by running `skim_with(numeric = list(hist = NULL), integer = list(hist = NULL))` prior to using the `skim()` function as well.)\n+\n+- Creating data visualizations:\n+```{r}\n+ggplot(evals_ch5, aes(x = age, y = score)) +\n+  geom_point() +\n+  labs(x = \"Age\", y = \"Teaching Score\",\n+       title = \"Scatterplot of relationship of teaching score and age\")\n+```\n+\n+\n+<!--\n+TODO: Albert needs to double check interpretation:\n+-->\n+\n+\n+Basid on the scatterplot visualization, there seem to have a weak negative relationship between age and teaching score. As age increases, the teaching score see, to decrease slightly. 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