Visualizations
All plot functions return an easystat_result object.
Access the ggplot2 object via result$plot_object and the
plain-language narrative via result$explanation.
Bar Chart (mean +/- SE)
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EasyStat implements a four-step pipeline that transforms raw data into publication-ready statistical output with a single function call:
stats functions (lm, t.test,
aov, etc.)broom::tidy()
/ broom::glance() to extract key values (p-value, effect
size, CIs, df)easystat_result S3 object that prints as HTML in RStudio
Viewer or as ASCII in the console, and can be exported to Wordresult <- easy_describe(mtcars$mpg)
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: DESCRIBE
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Variable N Missing Mean Median Mode SD SE Variance Min Q1
#> mtcars$mpg 32 0 20.0906 19.2 21 6.0269 1.0654 36.3241 10.4 15.425
#> Q3 Max Range IQR CV_pct Skewness Kurtosis CI_lower CI_upper Shapiro_p
#> 22.8 33.9 23.5 7.375 29.9988 0.6724 -0.022 17.9177 22.2636 12.2881%
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Variable Shape
#> mtcars$mpg moderately right-skewed
#> Kurtosis
#> approximately mesokurtic (similar tail weight to a normal distribution)
#> Normality Shapiro_p
#> approximately normal (Shapiro-Wilk p = 12.2881%) 12.2881%
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> DESCRIPTIVE STATISTICS: mtcars$mpg
#>
#> The variable 'mtcars$mpg' has 32 valid observations (missing: 0). The central
#> tendency is characterised by a mean of 20.0906 and a median of 19.2, with a
#> standard deviation of 6.0269. Values range from 10.4 to 33.9 (range = 23.5;
#> IQR = 7.375). The distribution is moderately right-skewed and approximately
#> mesokurtic (similar tail weight to a normal distribution). Based on the
#> Shapiro-Wilk test, the data are approximately normal (Shapiro-Wilk p =
#> 12.2881%). The coefficient of variation is 30%, indicating moderate
#> relative variability. The 95% confidence interval for the population mean
#> is [17.9177, 22.2636].
#>
#> ================================================================================result <- easy_describe(mtcars, vars = c("mpg", "hp", "wt"))
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: DESCRIBE
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Variable N Missing Mean Median Mode SD SE Variance Min
#> mpg 32 0 20.0906 19.200 21.00 6.0269 1.0654 36.3241 10.400
#> hp 32 0 146.6875 123.000 110.00 68.5629 12.1203 4700.8669 52.000
#> wt 32 0 3.2172 3.325 3.44 0.9785 0.1730 0.9574 1.513
#> Q1 Q3 Max Range IQR CV_pct Skewness Kurtosis CI_lower
#> 15.4250 22.80 33.900 23.500 7.3750 29.9988 0.6724 -0.0220 17.9177
#> 96.5000 180.00 335.000 283.000 83.5000 46.7408 0.7994 0.2752 121.9679
#> 2.5812 3.61 5.424 3.911 1.0288 30.4129 0.4659 0.4166 2.8645
#> CI_upper Shapiro_p
#> 22.2636 12.2881%
#> 171.4071 4.8808%
#> 3.5700 9.2655%
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Variable Shape
#> mpg moderately right-skewed
#> hp moderately right-skewed
#> wt approximately symmetric
#> Kurtosis
#> approximately mesokurtic (similar tail weight to a normal distribution)
#> approximately mesokurtic (similar tail weight to a normal distribution)
#> approximately mesokurtic (similar tail weight to a normal distribution)
#> Normality Shapiro_p
#> approximately normal (Shapiro-Wilk p = 12.2881%) 12.2881%
#> non-normal (Shapiro-Wilk p = 4.8808%) 4.8808%
#> approximately normal (Shapiro-Wilk p = 9.2655%) 9.2655%
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> DESCRIPTIVE STATISTICS: mpg
#>
#> The variable 'mpg' has 32 valid observations (missing: 0). The central
#> tendency is characterised by a mean of 20.0906 and a median of 19.2, with a
#> standard deviation of 6.0269. Values range from 10.4 to 33.9 (range = 23.5;
#> IQR = 7.375). The distribution is moderately right-skewed and approximately
#> mesokurtic (similar tail weight to a normal distribution). Based on the
#> Shapiro-Wilk test, the data are approximately normal (Shapiro-Wilk p =
#> 12.2881%). The coefficient of variation is 30%, indicating moderate
#> relative variability. The 95% confidence interval for the population mean
#> is [17.9177, 22.2636].
#>
#> ---
#>
#> DESCRIPTIVE STATISTICS: hp
#>
#> The variable 'hp' has 32 valid observations (missing: 0). The central
#> tendency is characterised by a mean of 146.6875 and a median of 123, with a
#> standard deviation of 68.5629. Values range from 52 to 335 (range = 283;
#> IQR = 83.5). The distribution is moderately right-skewed and approximately
#> mesokurtic (similar tail weight to a normal distribution). Based on the
#> Shapiro-Wilk test, the data are non-normal (Shapiro-Wilk p = 4.8808%). The
#> coefficient of variation is 46.7%, indicating high relative variability.
#> The 95% confidence interval for the population mean is [121.9679,
#> 171.4071].
#>
#> ---
#>
#> DESCRIPTIVE STATISTICS: wt
#>
#> The variable 'wt' has 32 valid observations (missing: 0). The central
#> tendency is characterised by a mean of 3.2172 and a median of 3.325, with a
#> standard deviation of 0.9785. Values range from 1.513 to 5.424 (range =
#> 3.911; IQR = 1.0288). The distribution is approximately symmetric and
#> approximately mesokurtic (similar tail weight to a normal distribution).
#> Based on the Shapiro-Wilk test, the data are approximately normal
#> (Shapiro-Wilk p = 9.2655%). The coefficient of variation is 30.4%,
#> indicating high relative variability. The 95% confidence interval for the
#> population mean is [2.8645, 3.57].
#>
#> ================================================================================result <- easy_group_summary(mpg ~ cyl, data = mtcars)
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: GROUP_SUMMARY
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Group N Mean Median SD SE Min Max IQR CV_pct Skewness CI_lower
#> 6 7 19.7429 19.7 1.4536 0.5494 17.8 21.4 2.35 7.3625 -0.2586 18.3985
#> 4 11 26.6636 26.0 4.5098 1.3598 21.4 33.9 7.60 16.9138 0.3485 23.6339
#> 8 14 15.1000 15.2 2.5600 0.6842 10.4 19.2 1.85 16.9540 -0.4558 13.6219
#> CI_upper
#> 21.0872
#> 29.6934
#> 16.5781
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> Outcome variable mpg
#> Grouping variable cyl
#> Number of groups 3
#> Overall Mean 20.0906
#> Overall SD 6.0269
#> Overall Median 19.2
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> GROUP SUMMARY: mpg by cyl
#>
#> Descriptive statistics were computed for 'mpg' across 3 groups of 'cyl'. The
#> group with the highest mean is '4' (M = 26.6636), while the group with the
#> lowest mean is '8' (M = 15.1). The group with the greatest variability
#> (highest SD) is '4' (SD = 4.5098). Overall, the grand mean across all
#> groups is 20.0906 (SD = 6.0269, Median = 19.2). These group-level
#> statistics provide the foundation for inferential comparisons using ANOVA
#> or t-tests.
#>
#> ================================================================================result <- easy_regression(mpg ~ wt + hp, data = mtcars)
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: REGRESSION
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Term Estimate Std. Error t Statistic p-value
#> (Intercept) 37.2273 1.5988 23.2847 <0.0001%
#> wt -3.8778 0.6327 -6.1287 0.0001%
#> hp -0.0318 0.0090 -3.5187 0.1451%
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> R-squared 0.826785
#> Adjusted R-squared 0.81484
#> F-statistic 69.2112
#> Model df 2
#> Residual df 29
#> Overall p-value <0.0001%
#>
#> TABLE 3 — REGRESSION ANOVA TABLE
#> --------------------------------------------------------------------------------
#> Term Df Sum_Sq Mean_Sq F_value p_value
#> wt 1 847.7252 847.7252 126.0411 <0.0001%
#> hp 1 83.2742 83.2742 12.3813 0.1451%
#> Residuals 29 195.0478 6.7258 NA NA
#>
#> TABLE 4 — REGRESSION DIAGNOSTICS
#> --------------------------------------------------------------------------------
#> Metric Value
#> N used 32
#> RMSE 2.4689
#> MAE 1.9015
#> Residual SD 2.5084
#> Mean residual 0
#> Shapiro-Wilk residual p 3.4275%
#> Durbin-Watson statistic 1.3624
#>
#> TABLE 5 — INFLUENTIAL OBSERVATIONS
#> --------------------------------------------------------------------------------
#> Observation Cook_Distance Leverage Std_Residual Influential
#> 17 0.423611 0.186487 2.3545 Yes
#> 31 0.272040 0.394208 1.1199 Yes
#> 20 0.208393 0.099503 2.3786 Yes
#> 18 0.157426 0.079910 2.3319 Yes
#> 28 0.073540 0.153641 1.1024 No
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> LINEAR REGRESSION ANALYSIS Formula: mpg ~ wt + hp
#>
#> The overall regression model is highly statistically significant (p <
#> 0.0001%), indicating that the set of 2 predictor(s) collectively explains a
#> meaningful portion of the variance in the outcome variable (F(2, 29) =
#> 69.211). The model accounts for 82.7% (large effect) of the total variance
#> in the response variable (Adjusted R² = 81.5%). The intercept is estimated
#> at 37.2273, representing the predicted value of the outcome when all
#> predictors equal zero (highly statistically significant (p < 0.0001%)). The
#> predictor 'wt' is associated with a decrease of 3.8778 in the outcome for
#> each one-unit increase, and this effect is highly statistically significant
#> (p = 0.0001%). The predictor 'hp' is associated with a decrease of 0.0318
#> in the outcome for each one-unit increase, and this effect is statistically
#> significant (p = 0.1451%). Overall, the model provides statistically
#> meaningful insight and may be suitable for predictive or inferential
#> purposes.
#>
#> ================================================================================result <- easy_ttest(mpg ~ am, data = mtcars)
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: TTEST
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Metric Label Value
#> Mean (Group 1) 0 17.1474
#> Mean (Group 2) 1 24.3923
#> 95% CI (lower) - -11.2802
#> 95% CI (upper) - -3.2097
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> t-statistic -3.7671
#> Degrees of Freedom 18.33
#> p-value 0.1374%
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> INDEPENDENT-SAMPLES t-TEST Comparison: mpg ~ am
#>
#> An independent-samples t-test revealed a statistically significant (p =
#> 0.1374%) difference between the two groups (t(18.33) = -3.767). The mean
#> for '0' was 17.1474 and the mean for '1' was 24.3923. The 95% confidence
#> interval for the difference in means ranged from -11.2802 to -3.2097. These
#> results provide statistically significant evidence that '0' and '1' differ
#> meaningfully on the measured variable.
#>
#> ================================================================================result <- easy_anova(Sepal.Length ~ Species, data = iris)
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: ANOVA
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Source df Sum of Squares Mean Square F Statistic p-value
#> Species 2 63.2121 31.6061 119.2645 <0.0001%
#> Residuals 147 38.9562 0.2650 NA NA
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> F-statistic 119.2645
#> Group df 2
#> Residual df 147
#> Overall p-value <0.0001%
#> Eta-squared (η²) 0.6187
#>
#> TABLE 3 — GROUP DESCRIPTIVES
#> --------------------------------------------------------------------------------
#> Group N Mean SD SE CI_Lower CI_Upper
#> setosa 50 5.006 0.3525 0.0498 4.9058 5.1062
#> versicolor 50 5.936 0.5162 0.0730 5.7893 6.0827
#> virginica 50 6.588 0.6359 0.0899 6.4073 6.7687
#>
#> TABLE 4 — ASSUMPTION CHECKS
#> --------------------------------------------------------------------------------
#> Check Result
#> Residual normality (Shapiro-Wilk) 21.8864%
#> Equal variances (Bartlett) 0.0335%
#> Recommended next step Consider Welch ANOVA or Kruskal-Wallis
#>
#> TABLE 5 — TUKEY POST-HOC COMPARISONS
#> --------------------------------------------------------------------------------
#> Comparison Difference CI_Lower CI_Upper Adj_p_value Significant
#> versicolor-setosa 0.930 0.6862 1.1738 <0.0001% Yes
#> virginica-setosa 1.582 1.3382 1.8258 <0.0001% Yes
#> virginica-versicolor 0.652 0.4082 0.8958 <0.0001% Yes
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> ONE-WAY ANOVA Formula: Sepal.Length ~ Species
#>
#> A one-way ANOVA revealed a highly statistically significant (p < 0.0001%)
#> difference across the 3 groups (F(2, 147) = 119.265). The effect size
#> (eta-squared = 0.6187) indicates a large practical significance of the
#> group factor, meaning the grouping variable accounts for approximately
#> 61.9% of the total variance in the outcome. Post-hoc tests (e.g., Tukey
#> HSD) are recommended to determine which specific group pairs differ
#> significantly.
#>
#> ================================================================================result <- easy_chisq(~ cyl + am, data = mtcars)
#> Warning in stats::chisq.test(tbl, correct = correct): Chi-squared approximation
#> may be incorrect
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: CHISQ
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Category Observed Expected Residual Std_Residual
#> 4 | 0 3 6.53 -3.5312 -1.3818
#> 4 | 1 4 4.16 -0.1562 -0.0766
#> 6 | 0 12 8.31 3.6875 1.2790
#> 6 | 1 8 4.47 3.5312 1.6705
#> 8 | 0 3 2.84 0.1562 0.0927
#> 8 | 1 2 5.69 -3.6875 -1.5462
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> Chi-square statistic (χ²) 8.7407
#> Degrees of Freedom 2
#> p-value 1.2647%
#> N (total) 32
#> Cramér's V 0.5226
#> Effect Strength very strong
#>
#> TABLE 3 — OBSERVED CONTINGENCY TABLE
#> --------------------------------------------------------------------------------
#> Category 0 1
#> 4 3 8
#> 6 4 3
#> 8 12 2
#>
#> TABLE 4 — EXPECTED COUNTS
#> --------------------------------------------------------------------------------
#> Category 0 1
#> 4 6.5312 4.4688
#> 6 4.1562 2.8438
#> 8 8.3125 5.6875
#>
#> TABLE 5 — ROW PERCENTAGES
#> --------------------------------------------------------------------------------
#> Category 0 1
#> 4 27.2727 72.7273
#> 6 57.1429 42.8571
#> 8 85.7143 14.2857
#>
#> TABLE 6 — COLUMN PERCENTAGES
#> --------------------------------------------------------------------------------
#> Category 0 1
#> 4 15.7895 61.5385
#> 6 21.0526 23.0769
#> 8 63.1579 15.3846
#>
#> TABLE 7 — TOTAL PERCENTAGES
#> --------------------------------------------------------------------------------
#> Category 0 1
#> 4 9.375 25.000
#> 6 12.500 9.375
#> 8 37.500 6.250
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> CHI-SQUARE TEST OF INDEPENDENCE
#>
#> A Pearson chi-square test of independence revealed a statistically
#> significant (p = 1.2647%) association between 'cyl' and 'am' (χ²(2) =
#> 8.741). The effect size, measured by Cramér's V = 0.5226, indicates a very
#> strong practical association between the two categorical variables. The
#> observed cell frequencies deviate meaningfully from what would be expected
#> under statistical independence, suggesting a genuine relationship between
#> 'cyl' and 'am'.
#>
#> ================================================================================result <- easy_ftest(mpg ~ am, data = mtcars)
#> Multiple parameters; naming those columns num.df and den.df.
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: FTEST
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Metric Value
#> Variance — 0 14.699298
#> Variance — 1 38.025769
#> SD — 0 3.833966
#> SD — 1 6.166504
#> n — 0 19.000000
#> n — 1 13.000000
#> Variance Ratio (F) 0.386561
#> 95% CI lower (ratio) 0.124372
#> 95% CI upper (ratio) 1.070343
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> F-statistic 0.3866
#> Numerator df 18
#> Denominator df 12
#> p-value 6.6906%
#> Alternative two.sided
#> Conclusion Variances are EQUAL
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> F-TEST FOR EQUALITY OF VARIANCES Comparison: mpg ~ am
#>
#> An F-test for equality of variances found not statistically significant (p =
#> 6.6906%) evidence of a difference in variance between the two groups (F(18,
#> 12) = 0.3866). The ratio of variances is 0.3866. The 95% CI for the
#> variance ratio is [0.1244, 1.0703]. IMPLICATION: The assumption of equal
#> variances (homoscedasticity) is SUPPORTED. Both the classical t-test and
#> Welch's t-test are appropriate.
#>
#> ================================================================================result <- easy_correlation(~ mpg + wt, data = mtcars)
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: CORRELATION
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Metric Value
#> r (Pearson) -0.867659
#> r² (shared variance %) 75.28%
#> 95% CI lower -0.933826
#> 95% CI upper -0.744087
#> t-statistic -9.559
#> n (valid pairs) 32
#> Regression slope -0.140862
#> Regression intercept 6.047255
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> p-value <0.0001%
#> Correlation strength strong
#> Direction Negative
#> Effect size class large (d ≥ 0.80)
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> CORRELATION ANALYSIS (Pearson)
#>
#> A Pearson correlation analysis revealed a strong negative correlation between
#> the two variables (r = -0.8677), which is highly statistically significant
#> (p < 0.0001%). The coefficient of determination (r² = 0.7528) indicates
#> that approximately 75.3% of the variance in one variable is shared with the
#> other. The 95% confidence interval for the correlation coefficient is
#> [-0.9338, -0.7441]. This strong relationship may have meaningful practical
#> implications and warrants further investigation.
#>
#> ================================================================================result <- easy_correlation(mtcars, vars = c("mpg", "hp", "wt", "disp"))
print(result, viewer = FALSE)
#>
#> ================================================================================
#> EasyStat Result :: CORRELATION_MATRIX
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Var1 Var2 r r_squared p_value Strength Direction Sig
#> mpg hp -0.7762 0.6024 <0.0001% strong Negative Yes
#> mpg wt -0.8677 0.7528 <0.0001% strong Negative Yes
#> mpg disp -0.8476 0.7183 <0.0001% strong Negative Yes
#> hp wt 0.6587 0.4339 0.0041% moderate Positive Yes
#> hp disp 0.7909 0.6256 <0.0001% strong Positive Yes
#> wt disp 0.8880 0.7885 <0.0001% strong Positive Yes
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> Method Pearson
#> Variables 4
#> Pairs examined 6
#> Strongest correlation 0.888
#> Weakest correlation 0.6587
#> Pairs significant 6
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> CORRELATION HEATMAP INTERPRETATION
#>
#> The heatmap displays pairwise pearson correlations among 4 variables. Cell
#> colour intensity reflects the strength of association: dark blue = strong
#> positive, dark red = strong negative, white = no correlation. Among the 6
#> pairs examined, 5 show strong correlations (|r| ≥ 0.70) and 1 show moderate
#> correlations (0.30 ≤ |r| < 0.70). Diagonal values are 1.0 by definition
#> (each variable correlates perfectly with itself).
#>
#> ================================================================================All plot functions return an easystat_result object.
Access the ggplot2 object via result$plot_object and the
plain-language narrative via result$explanation.
export_to_word() creates a formatted .docx
report with the result narrative and tables.
reg_result <- easy_regression(mpg ~ wt + hp, data = mtcars)
export_to_word(
reg_result,
file = "MyReport.docx",
title = "Fuel Economy Analysis",
author = "Mahesh Divakaran, Gunjan Singh, Jayadevan Shreedharan"
)easystat_result ObjectEvery EasyStat function returns a list with class
"easystat_result":
| Field | Contents |
|---|---|
test_type |
Character identifier (e.g. "regression",
"ttest") |
formula_str |
Formula or label used |
raw_model |
The underlying R model object (lm, htest,
etc.) |
coefficients_table |
data.frame of parameter estimates |
model_fit_table |
data.frame of fit / summary statistics |
explanation |
Plain-language narrative string |
plot_object |
ggplot2 object (visualization functions only) |
You can access any field directly:
result <- easy_ttest(mpg ~ am, data = mtcars)
cat(result$explanation)
#> INDEPENDENT-SAMPLES t-TEST
#> Comparison: mpg ~ am
#>
#> An independent-samples t-test revealed a statistically significant (p = 0.1374%) difference between the two groups (t(18.33) = -3.767). The mean for '0' was 17.1474 and the mean for '1' was 24.3923. The 95% confidence interval for the difference in means ranged from -11.2802 to -3.2097. These results provide statistically significant evidence that '0' and '1' differ meaningfully on the measured variable.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.