One Categorical Variable
Variable name:
Groups:
Single
Multiple
Input data as:
Counts in categories
Raw data
Category Name
Frequency
1
2
Input observations (category names) separated by spaces or commas. (Category names cannot have spaces in them.)
Graph Distributions
Plot type:
Bar chart
Pie chart
Label bar chart with:
Frequency
Relative frequency
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Summary Statistics
Perform Inference
Inference procedure:
1-sample z interval
1-sample z test
Chi-square goodness-of-fit test
Simulate sample proportion
Simulate sample count
Category to indicate as success:
Confidence level:
90%
95%
99%
Alternative hypothesis:
p <
p ≠
p >
Hypothesized proportion:
Enter expected counts for each category.
Category
Observed Frequency
Expected Frequency
Simulate the distribution of the sample
for samples of the original size assuming that the true proportion is equal to
a hypothesized value
the observed proportion
.
Hypothesized proportion:
Add this many samples:
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Count the
number
percent
of dots
less than
greater than
or equal to
Input data as:
Counts in categories
Raw data
For each group, input observations (category names) separated by spaces or commas. (Category names cannot have spaces in them.)
Group Name
Data
1
2
Graph Distributions
Bar chart type:
Segmented
Side-by-side
Label bar graph with:
Relative frequency
Frequency
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Summary Statistics
Perform Inference
Inference procedure:
Chi-square test for association
2-sample z interval (p₁ - p₂)
2-sample z test
Simulate difference in proportions
Category to indicate as success:
Confidence level:
90%
95%
99%
Alternative hypothesis:
p₁ - p₂ < 0
p₁ - p₂ ≠ 0
p₁ - p₂ > 0
Simulates the distribution of the difference in sample proportion of successes when the observed successes and failures are combined, shuffled, and redistributed into two groups that match the sizes of the original groups.
Add this many samples:
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Count the
number
percent
of dots
less than
greater than
or equal to
Adjust rounding and color preferences