Answer: Hello All 1. Here 1s my data set: temperature 0 73 1 g1 2 T2 3 24 4 34 5 81 74 Z 74 g 73 10 82 11 o 12 i 13 66 2. Here are my descriptive statistics: Mean= 775 Median= 7.0 Variance= 2781 Standard Deviation= 5.27 Based on these statistics the distribution of the daily macximum temperature in my zip code 15 around 78 degrees and the middle is 79 degrees. The variance of 27.81 is the average square difference from the mean, it means that the maximum temperature set is spread out over a range of 27.81. The standard deviation of 5.27 is the square root of the variance. 3. The line plot graph shows the general trend of the daily maximum temperature of my zip code. Line plot of temperature data 80.0 77.5 temperature 725 70 0 67.5 10 12 4. The purpose of the central tendency is to give a summary statistics measures of mean, median, and mode. And the measure of variability describes how far the data points fall from the mean. 5. The Boxplot graph shows the difference temperature data of my zip code and the Zion City.Since my zip code has a wider range of temperatures than Zion, my zip code has a higher value of mean than Zion. Zion upper and lower median data is lower than my median data.Emperature my_data aan_data In your follow-up posts to other students, review your peers' data sets and statistics and discuss the significance of these results. Here are some questions that you should address 1n your follow-up posts: 1. How do your peers' measures of central tendency compare to yvours? Are they are lower or higher? What does this signify? 2. How do the measures of variability compare? What does this signify? 3. In what ways are their data similar to or different from your own? Why are those similarities or distinetions meaningful? Hello, Compared to your mean and median, my temperature is lower. It is obvious that your city or zip code has nicer summer weather than mine because it has been raining recently in my area. My median temperature is only 79.0 degrees while yours is 89.21 degrees and my mean temperature is just 77.5 degrees while yours is 90.0 degrees. Your variability measurements are higher than mine because your temperature data collection is greater. This demonstrates how your info is more distributed. Your dataset and temperature ranges tend to be higher than mine, indicating that the area you live in is warmer than mine. Mean= 77.5 Median= 79.0 Variance= 27.81 Standard Deviation= 5.27 Hello, Although it appears that we both live in the same state, my location appears to have had a greater mean and median over the last 14 days than yours.Hello, Although 1t appears that we both live in the same state, my location appears to have had a greater mean and median over the last 14 days than vours. Since we are 1n the same state, there are not many differences in the temperature data we collected. Only 8.2 vanance differences are present in our measurement of variability. Since our dataset and temperature ranges are from the same state but a different zip code, there 1s not much difference between them. Thank you. For this discussion, you will collect data from a public source and calculate descriptive statistics, including measures of central tendency and variability. You will then interpret the results and provide feedback to your peers. In your initial post, use the World Temperatures website (or a similar website of your choice) to find the daily maximum temperature data rounded to the nearest integer (whole number) in your city or zip code for the past fourteen days. You will use this data set to calculate measures of central tendency and variability. You will also provide a detailed analysis based on your results. In your initial post, address the following items: 1. Share your data set. See Step 1 in the Python script. 2. What were your descriptive statistics for this data set? Report the mean, median, variance, and standard deviation. Based on these statistics, what can you say about the distribution of daily maximum temperature in your city or zip code? Use all of the statistics that you calculated to explain the distribution in detail. See Step 2 in the Python script. 3. Which graph showed the general trend of daily maximum temperature in your city or zip code? See Step 3 in the Python script. 4. In general, how are the measures of central tendency and variability used to analyze a data distribution? 5. The Python script also provides you with temperature data for a city called Zion. Which graph showed the difference in the distribution of your data and Zion's data? What can you say about the differences in data distributions? See Step 4 in the Python script. 6. Your graphs will not show up in your html document when attached to your discussion board post. Please embed them in your posts by right clicking on the graph while in your Python script, select "save image as", save it and then attach to your discussion board post using the camera icon in the menu bar