Update pages/statistics.py

pages/statistics.py

@@ -18,7 +18,7 @@ def show_content(topic):
 
     elif topic == "Types of Data":
         st.subheader("Types of Data")
-        st.image("C:/Data_Analysis/Images/page_1_img_1.png")
+        st.image("images/page_1_img_1.png")
         st.write("""
         Quantitative data: The data which is measured in numerical figures.
         Ex: Ages, weights, heights
@@ -29,13 +29,13 @@ def show_content(topic):
         This is again divided into:
         1. Discrete frequency data: Which can take some distinct values and maintain some gap between the values is called discrete frequency data.
         """)
-        st.image("C:/Data_Analysis/Images/page_2_img_1.png")
+        st.image("images/page_2_img_1.png")
         st.write("""
         2. Continuous frequency data: Which can take any functional values within the specified range is called continuous frequency data.
         This again divided into:
         - Exclusive continuous frequency data: In this format, the upper bound of any class is the same as the lower bound of the next class is known as exclusive continuous frequency data. Here the lower bounds are included, the upper bounds are excluded.
         """)
-        st.image("C:/Data_Analysis/Images/page_2_img_2.png")
+        st.image("images/page_2_img_2.png")
         st.write("""
         - Inclusive continuous frequency data: In this format, the upper bound of any class is not the same as the lower bound of the next class is known as inclusive continuous. Here lower bound and upper bound are included.
         So we convert into exclusive continuous because of partial continuation. Inclusive to Exclusive:
@@ -43,7 +43,7 @@ def show_content(topic):
         Correlation factor c.f = d/2 = 1/2 = 0.5
         Then lower bound - c.f and upper bound +c.f
         """)
-        st.image("C:/Data_Analysis/Images/page_3_img_1.png")
+        st.image("images/page_3_img_1.png")
         st.write("""
         Qualitative data: Also known as categorical data, describes the data that fits into the categories. Qualitative data are not numerical. The categorical information involves categorical variables that describe the features such as a person’s gender, home town, etc. Categorical measures are defined in terms of natural language specifications, but not in terms of numbers. Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense. Examples of the categorical data are birthdate, favorite sport, school postcode. Here, the birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.
         """)
@@ -66,7 +66,7 @@ def show_content(topic):
         2. Sample Data: This refers to data collected from a subset of the population. A sample is a group of subjects selected from the population. For example, if it’s not feasible to measure the height of every adult man in India, you might measure the heights of a sample of 1000 men selected randomly from across the country. The idea is that the sample represents the population and can give you a good estimate of the population parameter.
         In both cases, the goal is usually to learn something about the population. When it’s not practical or possible to study the entire population, then a sample is used, and statistical inference is used to draw conclusions about the population based on the sample.
         """)
-        st.image(r"C:\Users\THINKPAD\Downloads\page_7_img_1.png")
+        st.image(r"images\page_7_img_1.png")
         st.write("Examples:")
         st.write("1. Educational levels (e.g., elementary school, high school, bachelor’s degree, master’s degree).")
         st.write("2. Satisfaction levels (e.g., very satisfied, somewhat satisfied, neither satisfied nor dissatisfied, somewhat dissatisfied, very dissatisfied).")