Counting Rule for Combinations
Sample Mean
[pic]
Population Mean
[pic]
Interquartile Range
[pic]
|Sample Variance |Sample Standard Deviation |
|[pic] |[pic] |
|Population Variance |Population Standard Deviation |
|[pic] |[pic] |
|Coefficient of Variation | |
|[pic] | |
Z-Score
[pic] or [pic]
|Sample Covariance |Population Covariance |
|[pic] |[pic] |
|Pearson Product Moment Correlation Coefficient (Pearson R): |
|Sample Data |Population Data |
|[pic] |[pic] |
Weighted Mean
[pic]
|Sample Mean for Grouped Data |Population Mean for Grouped Data |
|[pic] |[pic] |
|Sample Variance for Grouped Data |Population Variance for Grouped Data |
|[pic] |[pic] |
|Sample Standard Deviation for Grouped Data |Population Standard Deviation for Grouped Data |
|[pic] |[pic] |
Counting Rule for Combinations
[pic]
Counting Rule for Permutations
[pic]
Computing Probability Using the Complement
[pic]
Addition Law
[pic]
Conditional Probability
[pic] or [pic]
Multiplication Law
[pic] or [pic]
Multiplication Law for Independent Events
[pic]
Expected Value of a Discrete Random Variable
[pic]
Variance of a Discrete Random Variable
[pic]
Binomial Probability Function
[pic]
Expected Value and Variance for the Binomial Distribution
[pic] [pic]
Poisson Probability Function
[pic] where [pic] = the probability of x occurrences in an interval
[pic] = the expected value or mean number of occurrences in an interval
e = 2.718
Sampling & Sampling Distributions
| |
|Standard Deviation of [pic] (Standard Error) |
|For a Finite Population |For an Infinite Population |
|[pic] |[pic] |
Sampling Proportions
| |
|Standard Deviation of [pic] (Standard Error) |
|For a Finite Population |For an Infinite Population |
|[pic] |[pic] |
Interval Estimate of a Population Mean: [pic] Known
[pic] Where [pic]
Interval Estimate of a Population Mean: [pic] Unknown
[pic] Where [pic]
Sample Size for an Interval Estimate of a Population Mean
[pic]
Interval Estimate of a Population Proportion
[pic]
Sample Size for an Interval Estimate of a Population Proportion
[pic]
Sampling Proportions
Test Statistic for Hypothesis Tests About a Population Proportion
[pic]
| |
|Standard Deviation of [pic] (Standard Error) |
|For a Finite Population |For an Infinite Population |
|[pic] |[pic] |
Interval Estimate of a Population Mean: [pic] Known
[pic] Where [pic] [pic]
Interval Estimate of a Population Mean: [pic] Unknown
[pic] Where [pic] [pic]
Interval Estimate of a Population Proportion
[pic]
Interval Estimate of the Difference Between Two Population Proportions
[pic]
Sample Size for an Interval Estimate of Population Proportions and Normal Distributions
[pic] [pic]
Z-Value Formulas for Normal and “Approximately Normal” Distributions – Calculations of Test Statistics
[pic] [pic] [pic][pic]
Test Statistic for Hypothesis Tests About Two Independent Proportions
[pic]
Standard Error of [pic]
[pic]
Degrees of Freedom for the t Distribution Using Two Independent Random Samples
[pic]
Test Statistic for Hypothesis Tests About Two Independent Means; Population Standard Deviations Unknown
[pic]
Test Statistic for Hypothesis Tests Involving Matched Samples
[pic]
Mean Difference Involving Matched (or Dependent) Samples
[pic]
Standard Deviation Notation Used for Matched Samples
[pic]
Interval Estimate of Means of Matched Samples
[pic]
Test Statistic for Hypothesis Tests About a Population Variance
[pic] [pic]
Test Statistic for Hypothesis Tests About Two Population Variances when [pic]
[pic] [pic] and [pic]
Interval Estimate of the Difference Between Two Population Means: σ1 and σ2 Known and Unknown
[pic] (σ known) [pic] (σ unknown)
Pooled Sample Standard Deviation (Assumptions that σ1 and σ2 are equal and populations are approximately normal) & Test Statistic for Comparison of Independent Samples
[pic] [pic] [pic] [pic]
Chi-Square Goodness of Fit Test Statistic
[pic]
Chi-Square Test for Independence Test Statistic
[pic]
Testing for the Equality of k Population Means—ANOVA
Sample Mean for Treatment j
[pic]
Sample Variance for Treatment j
[pic]
Overall Sample Mean (Grand Mean)
[pic]
Mean Square Due to Treatments
[pic]
Sum of Squares Due to Treatments
[pic]
Mean Square Due to Error
Sum of Squares Due to Error
[pic]
Test Statistic for the Equality of k Population Means
[pic]
Total Sum of Squares
[pic]
Partitioning of Sum of Squares
[pic]
Multiple Comparison Procedures
Test Statistic for Fisher’s LSD Procedure
[pic]
Fisher’s LSD
[pic]
Completely Randomized Designs
Mean Square Due to Treatments
[pic]
Mean Square Due to Error
[pic]
F Test Statistic
[pic]
Randomized Block Designs
Total Sum of Squares
[pic]
Sum of Squares Due to Treatments
[pic]
Sum of Squares Due to Blocks
[pic]
Sum of Squares Due to Error
[pic]
Factorial Experiments
Total Sum of Squares
[pic]
Sum of Squares for Factor A
[pic]
Sum of Squares for Factor B
[pic]
Sum of Squares for Interaction
[pic]
Sum of Squares for Error
[pic]
Simple Linear Regression Formulas
Simple Linear Regression Model
[pic]
Simple Linear Regression Equation
[pic]
Estimated Simple Linear Regression Equation
[pic]
Least Squares Criterion
[pic]
Slope and y-Intercept for the Estimated Regression Equation
[pic]
[pic]
Total Sum of Squares
[pic]
Sum of Squares Due to Error
[pic]
Sum of Squares Due to Regression
[pic]
Total Sum of Squares for Regression
[pic]
Coefficient of Determination
[pic]
Sample Correlation Coefficient
[pic]
Mean Square Error (Estimate of σ2 )
[pic]
Standard Error of the Estimate
[pic]
Standard Deviation of b1
[pic]
Estimated Standard Deviation of b1
[pic]
t Test Statistic
[pic]
Mean Square Regression
[pic]
F Test Statistic
[pic]
Estimated Standard Deviation of [pic]
[pic]
Confidence Interval for [pic]
[pic]
Estimated Standard Deviation of an Individual Value
[pic]
Prediction Interval for yp
[pic]
Residual for Observation i
[pic]
Standard Deviation of the i th Residual
[pic]
Standardized Residual for Observation i
[pic]
Leverage of Observation i
[pic]
Nonparametric Method Formulas
Mann-Whitney-Wilcoxon Test (Large Sample)
[pic]
[pic][pic]
Kruskall-Wallis Test Statistic
[pic]
Spearman Rank –Correlation Coefficient
[pic]
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[pic]
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