گراف پد

   به دلخواه زمان و مدت مشاوره آنلاین خود را انتخاب کنید.

بارگذاری...

شماره تماس و پیام

09128186605

    مدرس کلاس

    ابوالفضل قودجانی
    رتبه ۱ آزمون دکترا آمار کشور

    نویسنده کتاب روش‌های پیشرفته آماری و کاربردهای آن (نامزد کتاب سال)
    دارای ایمپکت RI Score =834 در ResearchGate
    در رده پنج درصد محققان و پژوهشگران برتر ResearchGate

    درباره مدرس بیشتر بدانیم

    گستردگی انواع تحلیل‌های آماری موجود در گراف پد را ببینید

     

    Statistical Comparisons

        html nested lists in direction rtl
    • Paired or unpaired t tests. Reports P values and confidence intervals
    •  
    • Automatically generate volcano plot (difference vs. P value) from multiple t test analysis
    •  
    • Nonparametric Mann-Whitney test, including confidence interval of difference of medians
    •  
    • Kolmogorov-Smirnov test to compare two groups
    •  
    • Wilcoxon test with confidence interval of median
    •  
    • Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further
    •  
    • Ordinary or repeated measures ANOVA followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni or Holm-Sidak multiple comparison tests, the post-test for trend, or Fisher’s Least Significant tests
    •  
    • One-way ANOVA without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3)
    •  
    • Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values
    •  
    • Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do not have to assume sphericity. When this is chosen, multiple comparison tests also do not assume sphericity
    •  
    • Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn’s post test
    •  
    • Fisher’s exact test or the chi-square test. Calculate the relative risk and odds ratio with confidence intervals
    •  
    • Two-way ANOVA, even with missing values with some post tests
    •  
    • Two-way ANOVA, with repeated measures in one or both factors. Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher’s LSD multiple comparisons testing main and simple effects
    •  
    • Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third)
    •  
    • Analysis of repeated measures data (one-, two-, and three-way) using a mixed effects model (similar to repeated measures ANOVA, but capable of handling missing data)
    •  
    • Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend)
    •  
    • Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model)
    •  
     

    Nonlinear Regression

        html nested lists in direction rtl
    • Fit one of our 105 built-in equations, or enter your own. Now including family of growth equations: exponential growth, exponential plateau, Gompertz, logistic, and beta (growth and then decay)
    •  
    • Enter differential or implicit equations
    •  
    • Enter different equations for different data sets
    •  
    • Global nonlinear regression – share parameters between data sets
    •  
    • Robust nonlinear regression
    •  
    • Automatic outlier identification or elimination
    •  
    • Compare models using extra sum-of-squares F test or AICc
    •  
    • Compare parameters between data sets
    •  
    • Apply constraints
    •  
    • Differentially weight points by several methods and assess how well your weighting method worked
    •  
    • Accept automatic initial estimated values or enter your own
    •  
    • Automatically graph curve over specified range of X values
    •  
    • Quantify precision of fits with SE or CI of parameters. Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate)
    •  
    • Quantify symmetry of imprecision with Hougaard’s skewness
    •  
    • Plot confidence or prediction bands
    •  
    • Test normality of residuals
    •  
    • Runs or replicates test of adequacy of model
    •  
    • Report the covariance matrix or set of dependencies
    •  
    • Easily interpolate points from the best fit curve
    •  
    • Fit straight lines to two data sets and determine the intersection point and both slopes
     

    Simulations

        html nested lists in direction rtl
    • Simulate XY, Column or Contingency tables
    •  
    • Repeat analyses of simulated data as a Monte-Carlo analysis
    •  
    • Plot functions from equations you select or enter and parameter values you choose

    Principal Component Analysis (PCA)

        html nested lists in direction rtl
    • Component selection via Parallel Analysis (Monte Carlo simulation), Kaiser criterion (Eigenvalue threshold), Proportion of Variance threshold, and more
    •  
    • Automatically generated Scree Plots, Loading Plots, Biplots, and more
    •  
    • Use results in downstream applications like Principal Component Regression
     

    Multiple Variable Graphing

        html nested lists in direction rtl
    • Specify variables defining axis coordinates, color, and size
    •  
    • Create Bubble Plots
     

    Column Statistics

        html nested lists in direction rtl
    • Calculate descriptive statistics: min, max, quartiles, mean, SD, SEM, CI, CV, skewness, kurtosis
    •  
    • Mean or geometric mean with confidence intervals
    •  
    • Frequency distributions (bin to histogram), including cumulative histograms
    •  
    • Normality testing by four methods (new: Anderson-Darling)
    •  
    • Lognormality test and likelihood of sampling from normal (Gaussian) vs. lognormal distribution
    •  
    • Create QQ Plot as part of normality testing
    •  
    • One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value
    •  
    • Identify outliers using Grubbs or ROUT method
    •  
    • Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify “significant” findings or discoveries
     

    Simple Linear Regression and Correlation

        html nested lists in direction rtl
    • Calculate slope and intercept with confidence intervals
    •  
    • Force the regression line through a specified point
    •  
    • Fit to replicate Y values or mean Y
    •  
    • Test for departure from linearity with a runs test
    •  
    • Calculate and graph residuals in four different ways (including QQ plot)
    •  
    • Compare slopes and intercepts of two or more regression lines
    •  
    • Interpolate new points along the standard curve
    •  
    • Pearson or Spearman (nonparametric) correlation
     

    Generalized Linear Models (GLMs)

        html nested lists in direction rtl
    • Generate models relating multiple independent variables to a single dependent variable using the new multiple variables data table
    •  
    • Multiple linear regression (when Y is continuous)
    •  
    • Poisson regression (when Y is counts; 0, 1, 2, …)
    •  
    • Logistic regression (when Y is binary; yes/no, pass/fail, etc.)
     

    Clinical (Diagnostic) Lab Statistics

        html nested lists in direction rtl
    • Bland-Altman plots
    •  
    • Receiver operator characteristic (ROC) curves
    •  
    • Deming regression (type ll linear regression)
     

    Other Calculations

        html nested lists in direction rtl
    • Area under the curve, with confidence interval
    •  
    • Transform data
    •  
    • Normalize
    •  
    • Identify outliers
    •  
    • Normality tests
    •  
    • Transpose tables
    •  
    • Subtract baseline (and combine columns)
    •  
    • Compute each value as a fraction of its row, column or grand total