The 324e 05 extremely low p value indicates that the

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The 3.24e-05 (extremely low) p-value indicates that the new interpretation of the data is statistically significant. The differences in data after taking the log of GDP means that it is not robust (able to retain correct output after changes in data). The slope for the first scatterplot is weakly negatively correlated (before using the log of GDP). After introducing log the scatterplot is weakly positvely correlated, indicating a totally different relationship. ####(k) Regardless of what you actually got in the above analyses, suppose that we find a positive and statistically significant coefficient in these regressions. Does this warrant the conclusion that "being more democratic (having a higher polity score) increases a country's GDP per capita?" Why or why not? Follow the instructions in class for how to address such causal questions, including pointing out potential confounders, non-comparability, and proposing the ideal research design. ### Question 2. Climate change
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####(a) Begin by loading a new dataset (Tempdata.RData) into R. This dataset shows the average temperature of Los Angeles for the month of October from 1944 to 2015. What is the range over the average temperature in October: What year has the highest average temperature? What year has the lowest? ```{r} Tdata <- load("tempdata.RData") range(tempdata$temp) mean(tempdata$temp) ``` 2015 had the highest average temperature with 74 degrees fahrenheit. 1946 had the lowest average temperature with 61.6 degrees fahrenheit. ####(b) Make a scatterplot of temperature over time. Add a trend line. What does it tell us about how the climate is changing over time? ```{r} plot(tempdata$year, tempdata$temp, xlab = "Recorded Years of October", ylab = "Recorded Average Temperatures in Fahrenheit", main = "Average Recorded Temperature of October in Los Angeles") model3 <- lm(tempdata$temp ~ tempdata$year, data = tempdata) abline(model3, col = "blue", lw = 2) ``` ####(c) Next, subset the data into groups by decade. Start with the seventies, and then create subsets for the eighties, nineties, noughts, and 2010-2015. What is the mean temperature for each decade? How is this changing over time? ```{r} seventies <- tempdata[27:36, "temp"] eighties <- tempdata[37:46, "temp"] nineties <- tempdata[47:56, "temp"] noughts <- tempdata [57:66, "temp"] current <- tempdata [67:72, "temp"] mean(seventies) mean(eighties) mean(nineties) mean(noughts) mean(current) ``` The mean has increased every decade (except noughts). ####(d) What is the standard deviation for each decade? How is the standard deviation change over time? What do the changes in standard deviation mean? ```{r} sd(seventies) sd(eighties) sd(nineties) sd(noughts)
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sd(current) ``` The overall positive trend in standard deviation indicates that the variation of weather is increasing. The temperatures in October are fluctuating more throughout the decades because of the higher max temperatures. ####(e) Why is understanding changes in temperature variation, not just average temperature, important for understanding the consequences of climate change?
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