Answer :
To tackle this problem, let's break down the tasks step by step.
### Step 1: Tabulate the Data
The data provided in the table can be summarized as follows:
| Temperature (°C) | Sweat Production (unit) | Urine Production (unit) |
|------------------|-------------------------|-------------------------|
| 10 | 28 | 70 |
| 15 | 45 | 60 |
| 20 | 70 | 47 |
| 25 | 115 | 35 |
| 30 | 190 | 21 |
| 35 | | |
### Step 2: Interpolate Missing Values
The values for temperature 35°C and 40°C are missing in the given data set. We can use linear interpolation to estimate these values.
#### Step 2.1: Estimate for 35 °C
To find sweat and urine production at 35°C, we can observe the trend from previous recorded values and use linear interpolation.
For sweat production:
- At 30°C, sweat production is 190 units.
- Let's assume the trend continues, and at higher temperatures, sweat production increases.
- We can estimate the sweat production at 35°C using a linear trend (or through more exact methods using nearby points).
For urine production:
- At 30°C, urine production is 21 units.
- Let's assume the trend continues, and at higher temperatures, urine production decreases.
- Similarly, we can estimate urine production at 35°C using a linear trend.
#### Step 2.2: Estimate for 40 °C
Similarly, we interpolate between 35°C and the next logical increment of 5°C to estimate values at 40°C.
### Step 3: Plot the Data
Using interpolation (the exact interpolated values can be confirmed using a tool or exact calculations):
| Temperature (°C) | Sweat Production (unit) | Urine Production (unit) |
|------------------|-------------------------|-------------------------|
| 10 | 28 | 70 |
| 15 | 45 | 60 |
| 20 | 70 | 47 |
| 25 | 115 | 35 |
| 30 | 190 | 21 |
| 35 | ~250 | ~10 |
| 40 | ~320 | ~5 |
Next, plot these values using a line graph.
### Step 4: Describe the Relationship
By observing and plotting the data on a graph, we can describe the relationship between sweat and urine production:
- Sweat Production: Increases with temperature. The relationship appears to be nonlinear; as temperature rises, sweat production dramatically increases.
- Urine Production: Decreases with temperature. The relationship also seems nonlinear; urine production significantly drops as the temperature increases.
This relationship suggests a physiological response to temperature where the body sweats more to cool down and conserves water, leading to reduced urine production.
### Step 5: Predictions
At 40°C:
- Sweat Production: Approximately 320 units.
- Urine Production: Approximately 5 units.
### Step 6: Variables to Control
To achieve accurate results in such an experiment, control the following variables:
- Humidity: Ensure constant humidity levels throughout the experiment.
- Hydration: Ensure all participants are equally hydrated.
- Physical Activity: Control for physical activity levels, as activity affects sweat and urine production.
- Measurement Methods: Use consistent and precise methods for measuring sweat and urine.
### Step 7: Improving the Experiment
To improve the experiment:
- Larger Sample Size: Use more participants to increase result reliability.
- Controlled Environment: Conduct the experiment in a controlled environment to account for external variables.
- Additional Variables: Control additional variables like food intake and clothing that can influence the results.
By following these steps, you can accurately analyze the relationship between temperature and sweat/urine production and predict values at unmeasured temperatures.
### Step 1: Tabulate the Data
The data provided in the table can be summarized as follows:
| Temperature (°C) | Sweat Production (unit) | Urine Production (unit) |
|------------------|-------------------------|-------------------------|
| 10 | 28 | 70 |
| 15 | 45 | 60 |
| 20 | 70 | 47 |
| 25 | 115 | 35 |
| 30 | 190 | 21 |
| 35 | | |
### Step 2: Interpolate Missing Values
The values for temperature 35°C and 40°C are missing in the given data set. We can use linear interpolation to estimate these values.
#### Step 2.1: Estimate for 35 °C
To find sweat and urine production at 35°C, we can observe the trend from previous recorded values and use linear interpolation.
For sweat production:
- At 30°C, sweat production is 190 units.
- Let's assume the trend continues, and at higher temperatures, sweat production increases.
- We can estimate the sweat production at 35°C using a linear trend (or through more exact methods using nearby points).
For urine production:
- At 30°C, urine production is 21 units.
- Let's assume the trend continues, and at higher temperatures, urine production decreases.
- Similarly, we can estimate urine production at 35°C using a linear trend.
#### Step 2.2: Estimate for 40 °C
Similarly, we interpolate between 35°C and the next logical increment of 5°C to estimate values at 40°C.
### Step 3: Plot the Data
Using interpolation (the exact interpolated values can be confirmed using a tool or exact calculations):
| Temperature (°C) | Sweat Production (unit) | Urine Production (unit) |
|------------------|-------------------------|-------------------------|
| 10 | 28 | 70 |
| 15 | 45 | 60 |
| 20 | 70 | 47 |
| 25 | 115 | 35 |
| 30 | 190 | 21 |
| 35 | ~250 | ~10 |
| 40 | ~320 | ~5 |
Next, plot these values using a line graph.
### Step 4: Describe the Relationship
By observing and plotting the data on a graph, we can describe the relationship between sweat and urine production:
- Sweat Production: Increases with temperature. The relationship appears to be nonlinear; as temperature rises, sweat production dramatically increases.
- Urine Production: Decreases with temperature. The relationship also seems nonlinear; urine production significantly drops as the temperature increases.
This relationship suggests a physiological response to temperature where the body sweats more to cool down and conserves water, leading to reduced urine production.
### Step 5: Predictions
At 40°C:
- Sweat Production: Approximately 320 units.
- Urine Production: Approximately 5 units.
### Step 6: Variables to Control
To achieve accurate results in such an experiment, control the following variables:
- Humidity: Ensure constant humidity levels throughout the experiment.
- Hydration: Ensure all participants are equally hydrated.
- Physical Activity: Control for physical activity levels, as activity affects sweat and urine production.
- Measurement Methods: Use consistent and precise methods for measuring sweat and urine.
### Step 7: Improving the Experiment
To improve the experiment:
- Larger Sample Size: Use more participants to increase result reliability.
- Controlled Environment: Conduct the experiment in a controlled environment to account for external variables.
- Additional Variables: Control additional variables like food intake and clothing that can influence the results.
By following these steps, you can accurately analyze the relationship between temperature and sweat/urine production and predict values at unmeasured temperatures.
