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This lab will introduce you to calculating confidence intervals using a set of data provided.

As discussed in lecture, confidence intervals are often calculated as a way to either assess how representative a sample is of a greater population, or a way to compare datasets.

The standard layout of confidence interval calculations is:

Mean value ± critical value * standard error

Remember though that you need to decide on what parameters you need to use for each of those parts of the formula.As discussed in class, this depends on whether the population standard deviation is known, the size of your sample, and whether the data is proportional (%) or not.

Demonstration Data:

Atmospheric CO2 has been strongly linked with rising global temperatures.This linkage has become increasingly apparent over the last few decades.In spite of efforts to curb the production of greenhouse gases, such as the Kyotyo Protocol, CO2 seems to be on the rise.To assess how much it has increased, you will be working CO2 concentrations (in parts per million [ppm]) measured by the National Oceanic and Atmospheric Adminstration (NOAA) since 1959.

This data was collected in Hawaii.Unfortunately, the sensors used to measure the data are expensive and there are challenges with deploying measurement equipment in remote locations, especially where research facilities are lacking funding. We can use confidence intervals for the available data to provide an assessment of how representative the data are of a greater area (i.e. the region of the south Pacific Ocean).This lab will walk you through how to calculate (95%) confidence intervals.In other words, we will be 95% confident that the true mean for the entire south Pacific region will be within the confidence intervals that you calculate.

If interested, the source of the data can be found here:

You will then apply these steps to the data from Lab 1 to evaluate how representative the temperature data in Inuvik is of the entire region.

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