Select term:
Statistics Dictionary Absolute Value Accuracy Addition Rule Alpha Alternative Hypothesis ANOVA Back-to-Back Stemplots Balanced Design Bar Chart Bartlett's Test Bayes Rule Bayes Theorem Bias Biased Estimate Bimodal Distribution Binomial Distribution Binomial Experiment Binomial Probability Binomial Random Variable Bivariate Data Blinding Blocking Blocking Variable Boxplot Cartesian Plane Categorical Variable Census Central Limit Theorem Chi-Square Distribution Chi-Square Goodness of Fit Test Chi-Square Statistic Chi-Square Test for Homogeneity Chi-Square Test for Independence Cluster Cluster Sampling Coefficient of Determination Coefficient of Multiple Determination Column Vector Combination Complement Completely Randomized Design Conditional Distribution Conditional Frequency Conditional Probability Confidence Interval Confidence Level Confounding Contingency Table Continuous Probability Distribution Continuous Variable Control Group Convenience Sample Correlation Covariance Critical Parameter Value Critical Value Cumulative Frequency Cumulative Frequency Plot Cumulative Probability Decision Rule Degrees of Freedom Dependent Variable Determinant Deviation Score Diagonal Matrix Discrete Probability Distribution Discrete Variable Discriminant Analysis Disjoint Disproportionate Stratification Dotplot Double Bar Chart Double Blinding Dummy Variable E Notation Echelon Matrix Effect Size Element Elementary Matrix Operations Elementary Operators Empty Set Epsilon Estimation Estimator Event Event Multiple Expected Value Experiment Experimental Design Extraneous Variable F Distribution F Statistic Factor Factorial Factorial Experiment Finite Population Correction Fixed Effects Model Fixed Factor Frequency Count Frequency Table Full Rank Gaps in Graphs Geometric Distribution Geometric Probability Hartley's Fmax Test Heterogeneous Histogram Homogeneous Hypergeometric Distribution Hypergeometric Experiment Hypergeometric Probability Hypergeometric Random Variable Hypothesis Test Identity Matrix Independent Independent Groups Design Independent Variable Influential Point Inner Product Interaction Plot Interactions Interquartile Range Intersection Interval Estimate Interval Scale Inverse IQR Joint Frequency Joint Probability Distribution Law of Large Numbers Level Line Linear Combination of Vectors Linear Dependence of Vectors Linear Transformation Logarithm Lurking Variable Margin of Error Marginal Distribution Marginal Frequency Marginal Mean Matched Pairs Design Matched-Pairs t-Test Matrix Matrix Dimension Matrix Inverse Matrix Order Matrix Rank Matrix Transpose Mauchly's Sphericity Test Mean Mean Square Measurement Scales Median Mixed Model Mode Multicollinearity Multinomial Distribution Multinomial Experiment Multiple Regression Multiplication Rule Multistage Sampling Mutually Exclusive Natural Logarithm Negative Binomial Distribution Negative Binomial Experiment Negative Binomial Probability Negative Binomial Random Variable Neyman Allocation Nominal Scale Nonlinear Transformation Non-Probability Sampling Nonresponse Bias Normal Distribution Normal Random Variable Null Hypothesis Null Set Observational Study One-Sample t-Test One-Sample z-Test One-stage Sampling One-Tailed Test One-Way ANOVA One-Way Table Optimum Allocation Ordinal Scale Outer Product Outlier Paired Data Parallel Boxplots Parameter Pearson Product-Moment Correlation Percentage Percentile Permutation Placebo Point Estimate Poisson Distribution Poisson Experiment Poisson Probability Poisson Random Variable Population Power Precision Probability Probability Density Function Probability Distribution Probability Sampling Proportion Proportionate Stratification P-Value Qualitative Variable Quantitative Variable Quartile Random Effects Model Random Factor Random Number Table Random Numbers Random Sampling Random Variable Randomization Randomized Block Design Randomized Block Experiment Range Ratio Scale Reduced Row Echelon Form Region of Acceptance Region of Rejection Regression Relative Frequency Relative Frequency Table Repeated Measures Design Replication Representative Residual Residual Plot Response Bias Row Echelon Form Row Vector Sample Sample Design Sample Point Sample Space Sample Survey Sampling Sampling Distribution Sampling Error Sampling Fraction Sampling Method Sampling With Replacement Sampling Without Replacement Scalar Matrix Scalar Multiple Scatterplot Selection Bias Set Significance Level Simple Random Sampling Simple Regression Singular Matrix Skewness Slope Sphericity Standard Deviation Standard Error Standard Normal Distribution Standard Score Statistic Statistical Experiment Statistical Hypothesis Statistics Stemplot Strata Stratified Sampling Subset Subtraction Rule Sum Vector Sums of Squares Symmetric Matrix Symmetry Systematic Sampling T Distribution T Score T Statistic Test Statistic Transpose Treatment t-Test Two-Sample t-Test Two-stage Sampling Two-Tailed Test Two-Way Table Type I Error Type II Error Unbiased Estimate Undercoverage Uniform Distribution Unimodal Distribution Union Univariate Data Variable Variance Variance Inflation Factor Vector Inner Product Vector Outer Product Vectors Venn Diagram Voluntary Response Bias Voluntary Sample Y Intercept z-score

Stemplot

A stemplot is used to display quantitative data, generally from small data sets (50 or fewer observations). The stemplot below shows IQ scores for 30 sixth graders.

In a stemplot, the entries on the left are called stems; and the entries on the right are called leaves. In the example above, the stems are tens (80 and 90) and hundreds (100 through 140). However, they could be other units - millions, thousands, ones, tenths, etc. In the example above, the stems and leaves are explicitly labeled for educational purposes. In the real world, however, stemplots usually do not include explicit labels for the stems and leaves.

Some stemplots include a key to help the user interpret the display correctly. The key in the stemplot above indicates that a stem of 110 with a leaf of 7 represents an IQ score of 117.

See also:

AP Statistics Tutorial: Stemplots (aka, Stem and Leaf Plot)