Question 1: The Following Shows The Temperatures High And Lo

Question 1the Following Shows The Temperatures High Low And Weather

The following shows the temperatures (high, low) and weather conditions in a given Sunday for some selected world cities. The weather conditions are indicated as: c = clear; cl = cloudy; sh = showers; pc = partly cloudy. For example, Montreal is listed with a high of 72, a low of 56, and condition pc.

1. Is “Montreal” an element, variable, or observation?

2. Provide the observation for Rome.

3. Give an example of a categorical variable.

4. Provide the ranges for low and high temperatures.

Paper For Above instruction

The data provided reflects the weather conditions across several international cities, including their respective high and low temperatures and qualitative weather descriptors. Analyzing this kind of data involves understanding its structure and the type of variables involved. Montreal, for instance, is considered an observation within the dataset, representing the recorded weather data for that specific city on the specified Sunday. It is not an element or variable but the unit of observation for that particular data point.

The observation for Rome includes the recorded high temperature of 88°F, the low temperature of 68°F, and the weather condition classified as cloudy (cl). This snapshot captures the specific weather scenario for Rome on that day, serving as an individual data point which can be part of broader statistical analysis.

An example of a categorical variable in this dataset is the weather condition notation such as c, cl, sh, and pc. These categories describe qualitative attributes that classify the type of weather experienced, such as clear, cloudy, showers, or partly cloudy, which are non-numeric attributes used for grouping and comparison.

The temperature ranges for the dataset can be summarized by identifying the minimum and maximum recorded values. The lowest low temperature is 57°F (Mexico City), and the highest high temperature is 99°F (Acapulco). These ranges help in understanding the variability of weather conditions across the cities included in the data, and they can be useful in further statistical analysis or comparisons.

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