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Catégorie :Category: nCreator TI-Nspire
Auteur Author: NSRBIKE
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 6.15 Ko KB
Mis en ligne Uploaded: 24/10/2024 - 08:53:02
Uploadeur Uploader: NSRBIKE (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4272667
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 6.15 Ko KB
Mis en ligne Uploaded: 24/10/2024 - 08:53:02
Uploadeur Uploader: NSRBIKE (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4272667
Description
Fichier Nspire généré sur TI-Planet.org.
Compatible OS 3.0 et ultérieurs.
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assignment comments : For the `variable amount`, I chose to represent it using a **histogram**. This is an ideal choice since amount is a continuous variable that spans a wide range of values. The histogram allows us to visualize the entire distribution in a way that doesn't force the data into arbitrary categories. By adjusting the number of bins, we can control the level of **granularity** in the chart. In this case, I set the number of bins to 68, which gives us enough detail to clearly see how the amounts are spread out. From the chart, we can easily observe a **bell-shaped curve**, which suggests that the distribution of amounts might follow a **normal distribution**. Most of the values are concentrated around a central range (around **150 to 200**), with fewer amounts on the lower and higher ends. This provides a clear picture of the overall distribution of amounts in the dataset, helping us identify patterns and any potential outliers. For the variable `age customer`, I chose a **bar chart** to represent the data. This type of chart is perfect for showing how many customers fall into each specific age group, making it easy to compare the frequencies across different ages. The bar chart clearly highlights the distribution of ages and helps us identify patterns. In this case, we can observe a clear **bell curve**, meaning that most customers are concentrated around the ages of **35 to 45**, with fewer customers at the younger and older ends of the spectrum. This kind of distribution suggests that the customer base is centered on middle-aged individuals. The chart also shows that there is a gradual decrease in frequency as we move towards both younger and older age groups, which provides a good overview of the age demographics in the dataset. The **bar chart** is a good choice here because even though **age** is a continuous variable, we can treat it as **discrete** by grouping ages into distinct categories, making it easier to visualize the distribution. For the variable `age customer`, I chose a **bar chart** to represent the data. This type of chart is perfect for showing how many customers fall into each specific age group, making it easy to compare the frequencies across different ages. The bar chart clearly highlights the distribution of ages and helps us identify patterns. In this case, we can observe a clear **bell curve**, meaning that most customers are concentrated around the ages of **35 to 45**, with fewer customers at the younger and older ends of the spectrum. This kind of distribution suggests that the customer base is centered on middle-aged individuals. The chart also shows that there is a gradual decrease in frequency as we move towards both younger and older age groups, which provides a good overview of the age demographics in the dataset. The **bar chart** is a good choice here because even though **age** is a continuous variable, we can treat it as **discrete** by grouping ages into distinct categories, making it easier to visualize the distribution. **1.** Correlation between amount and age customer: 0.6095 - This correlation of **0.61** suggests a **moderate positive relationship** between the **age of the customer** and the **amount spent**. As the age of the customer increases, there is a tendency for the amount they spend to also increase. However, its not a perfect relationship, indicating that age is a factor, but other elements are likely influencing spending as well. **2.** Correlation between amount and items bought: 0.2298 - The correlation of **0.23** shows a **weak positive relationship** between the **number of items bought** and the **amount spent**. This means that the number of items a customer buys has only a small impact on the total amount they spend. Factors such as the price per item likely play a bigger role in determining the total amount spent than the sheer number of items. # Select the 'amount' column where 'credit card' was used (1) credit_used = df[df['credit card'] == 1]['amount'] # Select the 'amount' column where 'credit card' was not used (0) credit_not_used = df[df['credit card'] == 0]['amount'] # Set the title for the plot title = 'Distribution of amounts paid by clients with and without credit card usage' # Plot a double histogram comparing 'debit used' and 'debit not used' draw_double_histogram(credit_used, credit_not_used, 44, title, 'Amount paid', 'credit used', 'credit not used') **Coefficient for age customer: 3.46** - For each additional year of the customer's age, the amount spent increases by approximately **3.46** units, **all else being equal**. This suggests that older customers tend to spend more than younger customers, provided other factors (like items bought and credit card usage) remain constant. **Coefficient for items bought: 0.77** - For each additional item bought, the amount spent increases by approximately **0.77** units, **all else being equal**. This indicates that the number of items has a small positive effect on the t
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Compatible OS 3.0 et ultérieurs.
<<
assignment comments : For the `variable amount`, I chose to represent it using a **histogram**. This is an ideal choice since amount is a continuous variable that spans a wide range of values. The histogram allows us to visualize the entire distribution in a way that doesn't force the data into arbitrary categories. By adjusting the number of bins, we can control the level of **granularity** in the chart. In this case, I set the number of bins to 68, which gives us enough detail to clearly see how the amounts are spread out. From the chart, we can easily observe a **bell-shaped curve**, which suggests that the distribution of amounts might follow a **normal distribution**. Most of the values are concentrated around a central range (around **150 to 200**), with fewer amounts on the lower and higher ends. This provides a clear picture of the overall distribution of amounts in the dataset, helping us identify patterns and any potential outliers. For the variable `age customer`, I chose a **bar chart** to represent the data. This type of chart is perfect for showing how many customers fall into each specific age group, making it easy to compare the frequencies across different ages. The bar chart clearly highlights the distribution of ages and helps us identify patterns. In this case, we can observe a clear **bell curve**, meaning that most customers are concentrated around the ages of **35 to 45**, with fewer customers at the younger and older ends of the spectrum. This kind of distribution suggests that the customer base is centered on middle-aged individuals. The chart also shows that there is a gradual decrease in frequency as we move towards both younger and older age groups, which provides a good overview of the age demographics in the dataset. The **bar chart** is a good choice here because even though **age** is a continuous variable, we can treat it as **discrete** by grouping ages into distinct categories, making it easier to visualize the distribution. For the variable `age customer`, I chose a **bar chart** to represent the data. This type of chart is perfect for showing how many customers fall into each specific age group, making it easy to compare the frequencies across different ages. The bar chart clearly highlights the distribution of ages and helps us identify patterns. In this case, we can observe a clear **bell curve**, meaning that most customers are concentrated around the ages of **35 to 45**, with fewer customers at the younger and older ends of the spectrum. This kind of distribution suggests that the customer base is centered on middle-aged individuals. The chart also shows that there is a gradual decrease in frequency as we move towards both younger and older age groups, which provides a good overview of the age demographics in the dataset. The **bar chart** is a good choice here because even though **age** is a continuous variable, we can treat it as **discrete** by grouping ages into distinct categories, making it easier to visualize the distribution. **1.** Correlation between amount and age customer: 0.6095 - This correlation of **0.61** suggests a **moderate positive relationship** between the **age of the customer** and the **amount spent**. As the age of the customer increases, there is a tendency for the amount they spend to also increase. However, its not a perfect relationship, indicating that age is a factor, but other elements are likely influencing spending as well. **2.** Correlation between amount and items bought: 0.2298 - The correlation of **0.23** shows a **weak positive relationship** between the **number of items bought** and the **amount spent**. This means that the number of items a customer buys has only a small impact on the total amount they spend. Factors such as the price per item likely play a bigger role in determining the total amount spent than the sheer number of items. # Select the 'amount' column where 'credit card' was used (1) credit_used = df[df['credit card'] == 1]['amount'] # Select the 'amount' column where 'credit card' was not used (0) credit_not_used = df[df['credit card'] == 0]['amount'] # Set the title for the plot title = 'Distribution of amounts paid by clients with and without credit card usage' # Plot a double histogram comparing 'debit used' and 'debit not used' draw_double_histogram(credit_used, credit_not_used, 44, title, 'Amount paid', 'credit used', 'credit not used') **Coefficient for age customer: 3.46** - For each additional year of the customer's age, the amount spent increases by approximately **3.46** units, **all else being equal**. This suggests that older customers tend to spend more than younger customers, provided other factors (like items bought and credit card usage) remain constant. **Coefficient for items bought: 0.77** - For each additional item bought, the amount spent increases by approximately **0.77** units, **all else being equal**. This indicates that the number of items has a small positive effect on the t
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