The DeathRate variable in the AllCountries dataset gives the death rate per 1000 people for all the countries in the world. Use StatKe) r other technology to create a histogram for this variable, and describe the shape of the distribution: is it relatively symmetric, mildly kewed to the left or right, or strongly skewed to the left or right? Click here for the dataset associated with this question.

I understand you're looking to analyze the distribution of the **DeathRate** variable from the **AllCountries** dataset. While I can't access external datasets or directly create visualizations, I can guide you through the process of creating a histogram and help you interpret the potential shapes based on typical data distributions. ### **Creating a Histogram** You can use various statistical tools to create a histogram. Here's a step-by-step guide using **StatKey**, **Excel**, and **Python (with matplotlib)**: #### **Using StatKey:** 1. **Access StatKey:** Go to the [StatKey website](https://www.lock5stat.com/StatKey). 2. **Upload Data:** If StatKey allows, upload your **AllCountries** dataset. If not, you might need to input the **DeathRate** data manually. 3. **Select Histogram Option:** Choose the histogram tool from the available options. 4. **Generate Histogram:** Select the **DeathRate** variable to create the histogram. #### **Using Excel:** 1. **Input Data:** Enter your **DeathRate** data into a single column. 2. **Insert Histogram:** - Highlight the data column. - Go to the **Insert** tab. - Select **Histogram** from the **Charts** group. 3. **Customize:** Adjust bin sizes and formatting as needed. #### **Using Python (matplotlib):** ```python import matplotlib.pyplot as plt import pandas as pd # Load your dataset data = pd.read_csv('AllCountries.csv') # Adjust the path as needed # Plot histogram plt.hist(data['DeathRate'], bins=20, edgecolor='black') plt.title('Histogram of Death Rate per 1000 People') plt.xlabel('Death Rate per 1000 People') plt.ylabel('Frequency') plt.show() ``` ### **Describing the Distribution Shape** Once you've created the histogram, analyze its shape based on the following characteristics: 1. **Symmetric Distribution:** - **Appearance:** The left and right sides of the histogram are mirror images. - **Implication:** Mean ≈ Median ≈ Mode. 2. **Skewed Right (Positively Skewed):** - **Appearance:** The tail extends to the right. - **Implication:** Mean > Median > Mode. 3. **Skewed Left (Negatively Skewed):** - **Appearance:** The tail extends to the left. - **Implication:** Mean < Median < Mode. 4. **Bimodal or Multimodal:** - **Appearance:** Two or more peaks in the histogram. - **Implication:** The data may come from different underlying groups. 5. **Uniform Distribution:** - **Appearance:** All bins have roughly the same frequency. - **Implication:** No distinct peak; each value is equally likely. ### **Interpreting Your Histogram** After creating the histogram for the **DeathRate** variable: - **Relatively Symmetric:** If both sides of the histogram look balanced with similar tails. - **Mildly Skewed Right/Left:** If one tail is slightly longer but not overwhelmingly so. - **Strongly Skewed Right/Left:** If one tail is significantly longer, indicating a large number of lower or higher values respectively. **For Example:** - **If your histogram shows a long tail on the right side**, you would describe the distribution as **strongly skewed to the right**. - **If the histogram has a longer tail on the left side**, it would be **strongly skewed to the left**. - **Balanced tails** on both sides indicate a **relatively symmetric distribution**. ### **Final Description** Based on the histogram you create, use the above guidelines to describe the shape of the **DeathRate** distribution. For instance: *"The histogram of the DeathRate variable is **moderately skewed to the right**, indicating that while most countries have a lower death rate per 1000 people, there are some countries with significantly higher death rates pulling the tail to the right."* Or *"The distribution of DeathRate is **relatively symmetric**, suggesting that the death rates are evenly distributed around the central value without significant skewness."* ### **Additional Tips** - **Check for Outliers:** Extreme values can affect the shape of the distribution. - **Consider Log Transformation:** If the data is highly skewed, a log transformation might normalize it, which can be useful for certain analyses. - **Compare with Summary Statistics:** Look at measures like mean, median, and mode to complement your visual interpretation. Feel free to share more specifics about your histogram if you need a more detailed interpretation!