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MAE202N ASSIGNMENT 2 SEMESTER 2 2023
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1.1 What type(s) of data are best for using the following? 1.1.1 A stem-and-leaf plot: Quantitative data 1.1.2 A bar chart: Categorical or qualitative data
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MAE202NASSIGNMENT 2
SEMESTER 2
2023
, Question 1:
1.1 What type(s) of data are best for using the following?
1.1.1 A stem-and-leaf plot: Quantitative data
1.1.2 A bar chart: Categorical or qualitative data
1.1.3 Pie chart: Categorical data with relative frequencies
1.1.4 Scatter plot: Two sets of quantitative data (usually x and y variables)
1.1.5 Line plot: Time-series data or data with a natural sequence
1.2 How is the bar graph better than the pictograph?
Bar graphs are often better than pictographs because they provide a clearer representation
of data. Bar graphs use consistent bar widths and heights to represent data quantities,
making it easier to compare values accurately. Pictographs, on the other hand, use pictures
or symbols, and the visual representation might not be precise, leading to misinterpretation.
1.3 For what reason are dot plots preferred to histograms?
Dot plots are often preferred to histograms when the data set is small or moderate in size
because they display each data point individually, making it easier to see the distribution and
identify specific data points. Histograms are better for larger data sets as they group data into
intervals, providing a smoother overview of the distribution.
1.4 What is or are the advantage(s) of histograms over stem-and-leaf plots?
Histograms can handle larger data sets more efficiently, showing the distribution of data
across intervals. They also provide a smoother visual representation of data distribution,
allowing for easier identification of trends and patterns. Stem-and-leaf plots are more
suitable for smaller data sets and might become cluttered with large data sets.
1.5 When should we make use of scatter plot graphs?
Scatter plots are used to display the relationship between two variables (often referred to as
x and y variables) in a data set. They are particularly useful when you want to explore how
changes in one variable affect another, such as investigating correlations or trends between
variables.
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