Question

Suppose you record the hours of daylight in Tucson, Arizona, each day for a year and find the mean amount. (a) What do you expect for an approximate mean? (b) How would your data compare with a student doing the same project in Anchorage, Alaska? (c) How would your standard deviation compare with a student doing the same project in Anchorage, Alaska?

Short answer

Question: Compare the approximate mean daylight hours and standard deviation between Tucson, Arizona, and Anchorage, Alaska. Answer: The approximate mean daylight hours in Tucson, Arizona, is around 12 hours, while in Anchorage, Alaska, it varies greatly due to extreme seasonal variation. The standard deviation in Anchorage is larger compared to Tucson due to the larger variability of daylight hours throughout the year.

Step-by-step solution

(a) Approximate mean of the daylight hours in Tucson, Arizona

We are not given specific data for Tucson, Arizona, so we cannot compute an exact mean. However, we can approximate it considering that Tucson is located in a temperate zone, with relatively equal day and night times throughout the year. Throughout the year, the length of the day varies between 10 and 14 hours, thus an approximate mean can be found by averaging these numbers. Mean = (Shortest day + Longest day) / 2

(b) Comparing data with Anchorage, Alaska

Anchorage, Alaska is located in a much higher latitude than Tucson, Arizona. This means that daylight hours vary greatly throughout the year. In the summer, Anchorage experiences extremely long days with up to 19 hours of daylight, whereas in the winter, daylight hours can be as short as 5 hours. The approximate mean can be calculated in the same way as before, by averaging the shortest and longest days: Mean = (Shortest day + Longest day) / 2 The mean hours of daylight in Anchorage will be different from that in Tucson due to the extreme seasonal variation at high latitudes.

(c) Comparing standard deviations

The standard deviation is a measure of the variability of the data. Since Tucson's daylight hours have little variation throughout the year, its standard deviation will be relatively small. In contrast, Anchorage's daylight hours have a much larger variation due to the extreme seasonal changes, so its standard deviation will be larger. Without specific data, we cannot calculate the exact standard deviations for both locations. However, based on the differences in seasonal variation in daylight hours, we can conclude that the standard deviation in Anchorage, Alaska will be larger than that in Tucson, Arizona.

Key concepts

Mean Calculation

Calculating the mean for daylight hours gives us an average time the sun is visible during the day over a certain period, usually a year. In Tucson, Arizona, the daylight varies due to its location in the temperate zone.
This means the length of the day can range from about 10 hours to as much as 14 hours throughout the year. To find the mean, you can use this simple method:

• Add the shortest daylight duration to the longest daylight duration. This is your total.
• Now, divide this number by 2 as you are finding the midpoint, which serves as the average.

So, for Tucson, the mean of daylight hours would be: \[\text{Mean} = \frac{(10 + 14)}{2} = 12 \text{ hours}\]By understanding the mean, students can grasp the average concept even if the specific data details are not explicitly provided.

Standard Deviation

Standard deviation is all about understanding the variation or spread of your dataset. In terms of daylight, it tells us how much the hours of daylight differ from the average each day. For Tucson, daylight hours are pretty stable, not swinging too much throughout the year, with only a 4-hour difference between the shortest and longest days. This regularity results in a small standard deviation. On the other hand, up in Anchorage, Alaska, things look vastly different:

• In summer, daylight can last up to 19 hours.
• Again in winter, it can drop to just 5 hours.

This large fluctuation creates a significant variation from the average, leading to a larger standard deviation. Higher standard deviation signifies that the data points, or daylight hours, often fall further from the mean. Even without specific numbers, by understanding geographical impacts and seasonal changes, students can predict the standard deviation trends.

Latitude Effects

Latitude plays a big role in determining the amount of daylight a location receives throughout the year. Locations at higher latitudes, such as Anchorage, Alaska, experience more extreme daylight hours changes. This happens due to the tilt of the Earth's axis. During summer, higher latitudes lean towards the sun resulting in extremely long daylight periods whereas during winter, the same places plunge into extended darkness. Conversely, places like Tucson, with a much lower latitude, escape such drastic differences. They enjoy relatively balanced daylight hours all year round.
This variation is directly tied to how high or low a place is from the equatorial line. The distance from the equator affects not only the length of day but also results in different sun angles, affecting the intensity of the sunlight received. Thus, understanding how latitude affects daylight can help gauge the seasonal daylight patterns in different locations.

Seasonal Variation

Seasonal variation describes how daylight hours change over different seasons. This change is more prominent at higher latitudes.
In places like Anchorage, Alaska, residents experience long summer days thanks to the tilt of Earth's axis. However, come winter, the days become very short, sometimes barely offering any daylight.
Tucson, in contrast, sees a much milder change with its shorter and longer days not varying as drastically. Those fluctuations are due to the Earth's rotation and orbit around the sun, impacting how long and strong the sun's rays reach any given place.
In practical terms, understanding seasonal variations helps explain why people experience the sunrise and sunset at different times through the year, impacting everything from daily schedules to ecological patterns. Recognizing these patterns enables better planning in agriculture, energy consumption, and leisure activities.