Whille comparing two distributions, the reason why it is best to use relative frequency histograms rather than frequency histogram.

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pattererX · Accepted Answer

Explanation:  When there are different number of observations in two histograms, the frequency of histogram-1 class interval might be high when compared to frequency of histogram-2 class interval but it might be because of more number of observations in histogram-1.  For example  There are 500 observations is first histogram and 50 is frequency of one class interval.  There are 100 observations is second histogram and 30 is frequency of same class interval.  Here if we compare frequency we say frequency of histogram-1 is high that of frequency of histogram-2 in that class interval. But in reality only 10% of data fall in that class interval in histogram-1 and 30% of data fall in that class interval in histogram-2  Hence when comparing two histograms, it is best to use relative frequency histograms rather than frequency histograms when the data distributions have different number of observations.  Option d is correct.

user_27qwe · Accepted Answer

When comparing two distributions, it is best to use relative frequency histograms (RFH) rather than frequency histograms. The RFH takes into account the proportions or percentages of each category or bin in relation to the total number of observations. This normalization allows for a fairer and more accurate comparison between the distributions, as it eliminates the influence of differing sample sizes. By representing the data in terms of relative frequencies, we can focus on the shape and pattern of the distributions rather than being biased by absolute frequencies.

karton · Accepted Answer

Result:Relative frequency histograms provide a more accurate representation when comparing two distributions, especially when sample sizes differ.Solution:When comparing two distributions, it is best to use relative frequency histograms rather than frequency histograms.A frequency histogram represents the number of occurrences of each value or interval in a dataset. It displays the frequencies of different values or intervals on the vertical axis. However, when comparing distributions with different sample sizes, frequency histograms can be misleading.On the other hand, a relative frequency histogram represents the proportion or percentage of occurrences of each value or interval in a dataset. It displays the relative frequencies on the vertical axis, which can be calculated by dividing the frequency of each value or interval by the total number of data points.Using relative frequency histograms allows for a fairer comparison between distributions with different sample sizes. It standardizes the data by showing the proportions or percentages of occurrences rather than the raw frequencies. This enables us to focus on the distribution&#039;s shape and relative proportions, rather than being influenced by the sample size.

star233 · Accepted Answer

To understand why relative frequency histograms are preferred, let&#039;s first define the terms. A frequency histogram represents the counts or frequencies of observations within specified intervals or bins. On the other hand, a relative frequency histogram displays the proportion or percentage of observations in each interval relative to the total number of observations.To illustrate this concept, let&#039;s consider two distributions: Distribution A and Distribution B. We can represent the frequency histogram of Distribution A as fA(x) and the frequency histogram of Distribution B as fB(x).The frequency histogram fA(x) would show the absolute counts of observations in each interval for Distribution A, and similarly, fB(x) would show the absolute counts of observations in each interval for Distribution B.However, if we want to compare the distributions more accurately, it is better to use relative frequency histograms. Let&#039;s denote the relative frequency histogram of Distribution A as rfA(x) and the relative frequency histogram of Distribution B as rfB(x).The relative frequency histograms rfA(x) and rfB(x) represent the proportions or percentages of observations in each interval relative to the total number of observations in Distribution A and Distribution B, respectively.Using relative frequency histograms allows us to directly compare the shapes and patterns of the distributions, regardless of the differences in their total counts or sample sizes. It provides a standardized representation that is not affected by variations in sample size.To summarize, when comparing two distributions, it is best to use relative frequency histograms (rfA(x) and rfB(x)) rather than frequency histograms (fA(x) and fB(x)) because relative frequency histograms offer a more accurate and meaningful comparison by considering the proportions or percentages of observations in each interval.

Whille comparing two distributions, the reason why it is best

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