![]() Xpl<-xp_hat + qnorm(0.025)*sqrt(v_xp_hat) #lower bound Xp_hat<-fd$estimate+qprobs*fd$estimate #estimated perc. To add the confidence bounds as in Minitab, you can do the following fd<-fitdistr(x, "normal") #Maximum-likelihood Fitting of Univariate Dist from MASS Ggplot(data = df, aes(x = x, y = y)) + geom_point() + geom_abline(intercept = int,slope = slope)+scale_y_continuous(limits=range(qprobs), breaks=qprobs, labels = 100*probs)+labs(y ="Percent", x="Data") The following shows the line in the right spot: df check str(check) Sample data to recreate the plot above: x xl yl slope int slope It seems that geom_smooth() would be the likely candidate to add the bands, but I haven't figure that out.įinally, the Getting Genetics Done guys describe something similar here. ![]() Similarly, ggplot's stat_qq() seems to present similar information with a transformed x axis. Unfortunately, I cannot figure out how to add the confidence interval bands around this plot. ![]() The probplot gets you most of the way there. Minitab describes this as a normal probability plot. Residual plots - Stat > Regression > Regression > Graphs Simulation Generating Random Numbers - Calc > Random Data Probability Calculations Any Probability Distribution - Graph > Probability Distribution Plot Binomial Distributions - Calc > Probability Distributions Binomial Probability - P(B=b)Ĭumulative Probability - P(B Probability Distributions Uniform Probability Density - height of probability density curveĬumulative Probability - P(U Probability Distributions Normal Probability Density - height of probability density curveĬumulative Probability - P(Z Random Data Confidence Intervals and Hypothesis Tests Confidence Intervals and Hypothesis Tests for a Population Mean Stat > Basic Statistics > 1-Sample t Confidence Intervals and Hypothesis Tests for a Population Median Stat > Nonparametrics > 1-Sample Sign Confidence Intervals and Hypothesis Tests for Paired Data Stat > Basic Stats > Paired tįorm differences (post-pre, before-after, second-first, etc.) and then use the one sample procedures on the differences.I am trying to recreate the following plot with R. Scatterplot with regression line - Stat > Regression > Fitted Line Plot Prediction and assessing the fit Fitted values and residuals - Stat > Regression > Regression > Storage > Fits and Residuals ![]() Time plots - Graph > Scatterplot OR Graph > Time series plot Measuring the strength of association Pearson's Correlation - Stat > Basic Statistics > Correlation Fitting a line to data Least squares regression line - Stat > Regression > Regression Scatterplot Smoothing - Graph > Scatterplot > Data View > Smoother > LOWESS Stat > Basic Statistics > Display Descriptive StatisticsĬreating New Variables - Calc > Calculator Normal Distributions Calculations - Calc > Probability Distributions Normal Probability Density - height of probability density curveĬumulative Probability - P(Z Standardize Evaluating Normality - Graph > Probability Plot Association and relationship between 2 quantitative variables Scatterplots - Graph > Scatterplot Minitab Commands Summary of Minitab Commands Graphical Displays Bar Graphs - Graph > Chartīoxplots - Graph > Boxplot Describing and Summarizing Data Mean, trimmed mean, median, standard deviation, quartiles,
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