Public Lab Wiki documentation

This week we're focusing on data analysis using math concepts you guys are already familiar with. Last week we focused on just 1 day of data, this week we'll identify patterns over one full week of values.

Activity 1: Identifying the range and mean of 1 week of data

The range, context and size of data sets is important to data analysis / data science; the number of datapoints we have to analyze helps scientists and researchers eliminate bias and gives us a higher resolution picture of what is happening. for example how using a larger data set can be more meaningful when identifying patterns and trends.

Here, we're looking at Egg 10's temperature an humidity. Where are the lowest and highest points in the week for both Temperature and Humidity? What about when we average an entire day of data and compare entire days?

Drawing conclusions from our data

Why does the highest point of humidity coincide with one of the lowest points of temperature? Around the 5th day of this chart, we see the humidity has a much higher 24-hour average than any other day. We can also see that the temperature did not rise as high as the other days, and it stayed relatively low the entire period. How do we think the weather was behaving at these points? What do high relative humidity values mean in terms weather? How does the temperature usually feel when it???s raining?

Activity 2: Calculating Slope

We calculate slope by dividing rise over run. Calculating slope can be very useful when trying to compare and understand patterns in our data. Below, you???ll see a line graph of the dataset we used, showing 48 hours of Temperature data from the Air Quality Egg installed in the school???s greenhouse.

Drawing conclusions from our data

Where do we see the fastest increase and decrease of the change in temperature? Students found that sharpest slopes are in the rise of temperature in the morning-time. The slowest rate of change happens in the evening, as the greenhouse cools down for the night. What can we conclude about the design of the greenhouse in terms of heat preservation? Why would we want a greenhouse to absorb heat fast and release it slow?

This week we're focusing on data analysis using math concepts you guys are already familiar with. Last week we focused on just 1 day of data, this week we'll identify patterns over one full week of values.

Activity 1: Identifying the range and mean of 1 week of data

The range, context and size of data sets is important to data analysis / data science; the number of datapoints we have to analyze helps scientists and researchers eliminate bias and gives us a higher resolution picture of what is happening. for example how using a larger data set can be more meaningful when identifying patterns and trends.

Here, we're looking at Egg 10's temperature an humidity. Where are the lowest and highest points in the week for both Temperature and Humidity? What about when we average an entire day of data and compare entire days?

Drawing conclusions from our data

Why does the highest point of humidity coincide with one of the lowest points of temperature? Around the 5th day of this chart, we see the humidity has a much higher 24-hour average than any other day. We can also see that the temperature did not rise as high as the other days, and it stayed relatively low the entire period. How do we think the weather was behaving at these points? What do high relative humidity values mean in terms weather? How does the temperature usually feel when it???s raining?

Activity 2: Calculating Slope

We calculate slope by dividing rise over run. Calculating slope can be very useful when trying to compare and understand patterns in our data. Below, you???ll see a line graph of the dataset we used, showing 48 hours of Temperature data from the Air Quality Egg installed in the school???s greenhouse.

Drawing conclusions from our data

Where do we see the fastest increase and decrease of the change in temperature? Students found that sharpest slopes are in the rise of temperature in the morning-time. The slowest rate of change happens in the evening, as the greenhouse cools down for the night. What can we conclude about the design of the greenhouse in terms of heat preservation? Why would we want a greenhouse to absorb heat fast and release it slow?

This week we're focusing on data analysis using math concepts you guys are already familiar with. Last week we focused on just 1 day of data, this week we'll identify patterns over one full week of values.

Activity 1: Identifying the range and mean of 1 week of data

The range, context and size of data sets is important to data analysis / data science; the number of datapoints we have to analyze helps scientists and researchers eliminate bias and gives us a higher resolution picture of what is happening. for example how using a larger data set can be more meaningful when identifying patterns and trends.

Here, we're looking at Egg 10's temperature an humidity. Where are the lowest and highest points in the week for both Temperature and Humidity? What about when we average an entire day of data and compare entire days?

Drawing conclusions from our data

Why does the highest point of humidity coincide with one of the lowest points of temperature? Around the 5th day of this chart, we see the humidity has a much higher 24-hour average than any other day. We can also see that the temperature did not rise as high as the other days, and it stayed relatively low the entire period. How do we think the weather was behaving at these points? What do high relative humidity values mean in terms weather? How does the temperature usually feel when it???s raining?

Activity 2: Calculating Slope

We calculate slope by dividing rise over run. Calculating slope can be very useful when trying to compare and understand patterns in our data. Below, you???ll see a line graph of the dataset we used, showing 48 hours of Temperature data from the Air Quality Egg installed in the school???s greenhouse.

Drawing conclusions from our data

Where do we see the fastest increase and decrease of the change in temperature? Students found that sharpest slopes are in the rise of temperature in the morning-time. The slowest rate of change happens in the evening, as the greenhouse cools down for the night. What can we conclude about the design of the greenhouse in terms of heat preservation? Why would we want a greenhouse to absorb heat fast and release it slow?

This week we're focusing on data analysis using math concepts you guys are already familiar with. Last week we focused on just 1 day of data, this week we'll identify patterns over one full week of values.

Activity 1 Identifying the range and mean of 1 week of data The range, context and size of data sets is important to data analysis / data science; the number of datapoints we have to analyze helps scientists and researchers eliminate bias and gives us a higher resolution picture of what is happening. for example how using a larger data set can be more meaningful when identifying patterns and trends.

Here, we're looking at Egg 10's temperature an humidity. Where are the lowest and highest points in the week for both Temperature and Humidity? What about when we average an entire day of data and compare entire days?

Drawing conclusions from our data

Why does the highest point of humidity coincide with one of the lowest points of temperature? Around the 5th day of this chart, we see the humidity has a much higher 24-hour average than any other day. We can also see that the temperature did not rise as high as the other days, and it stayed relatively low the entire period. How do we think the weather was behaving at these points? What do high relative humidity values mean in terms weather? How does the temperature usually feel when it???s raining?

Activity 2 We calculate slope by dividing ???rise??? over ???run???. Calculating slope can be very useful when trying to compare and understand patterns in our data. Below, you???ll see a line graph of the dataset we used, showing 48 hours of Temperature data from the Air Quality Egg installed in the school???s greenhouse.

Drawing conclusions from our data

Where do we see the fastest increase and decrease of the change in temperature? Students found that sharpest slopes are in the rise of temperature in the morning-time. The slowest rate of change happens in the evening, as the greenhouse cools down for the night. What can we conclude about the design of the greenhouse in terms of heat preservation? Why would we want a greenhouse to absorb heat fast and release it slow?

This week we???re focusing on data analysis using math concepts you guys are already familiar with. Last week we focused on just 1 day of data, this week we???ll identify patterns over one full week of values.

Activity 1 Identifying the range and mean of 1 week of data The range, context and size of data sets is important to data analysis / data science; the number of datapoints we have to analyze helps scientists and researchers eliminate bias and gives us a higher resolution picture of what is happening. for example how using a larger data set can be more meaningful when identifying patterns and trends.

Here, we???re looking at Egg 10???s temperature an humidity. Where are the lowest and highest points in the week for both Temperature and Humidity? What about when we average an entire day of data and compare entire days?

or as an image like this:

Drawing conclusions from our data

Why does the highest point of humidity coincide with one of the lowest points of temperature? Around the 5th day of this chart, we see the humidity has a much higher 24-hour average than any other day. We can also see that the temperature did not rise as high as the other days, and it stayed relatively low the entire period. How do we think the weather was behaving at these points? What do high relative humidity values mean in terms weather? How does the temperature usually feel when it???s raining?

Activity 2 We calculate slope by dividing ???rise??? over ???run???. Calculating slope can be very useful when trying to compare and understand patterns in our data. Below, you???ll see a line graph of the dataset we used, showing 48 hours of Temperature data from the Air Quality Egg installed in the school???s greenhouse.

Drawing conclusions from our data

Where do we see the fastest increase and decrease of the change in temperature? Students found that sharpest slopes are in the rise of temperature in the morning-time. The slowest rate of change happens in the evening, as the greenhouse cools down for the night. What can we conclude about the design of the greenhouse in terms of heat preservation? Why would we want a greenhouse to absorb heat fast and release it slow?