Statistics Tutorial
Selected Reading
- Statistics - Discussion
- Z table
- Weak Law of Large Numbers
- Venn Diagram
- Variance
- Type I & II Error
- Trimmed Mean
- Transformations
- Ti 83 Exponential Regression
- T-Distribution Table
- Sum of Square
- Student T Test
- Stratified sampling
- Stem and Leaf Plot
- Statistics Notation
- Statistics Formulas
- Statistical Significance
- Standard normal table
- Standard Error ( SE )
- Standard Deviation
- Skewness
- Simple random sampling
- Signal to Noise Ratio
- Shannon Wiener Diversity Index
- Scatterplots
- Sampling methods
- Sample planning
- Root Mean Square
- Residual sum of squares
- Residual analysis
- Required Sample Size
- Reliability Coefficient
- Relative Standard Deviation
- Regression Intercept Confidence Interval
- Rayleigh Distribution
- Range Rule of Thumb
- Quartile Deviation
- Qualitative Data Vs Quantitative Data
- Quadratic Regression Equation
- Process Sigma
- Process Capability (Cp) & Process Performance (Pp)
- Probability Density Function
- Probability Bayes Theorem
- Probability Multiplecative Theorem
- Probability Additive Theorem
- Probability
- Power Calculator
- Pooled Variance (r)
- Poisson Distribution
- Pie Chart
- Permutation with Replacement
- Permutation
- Outlier Function
- One Proportion Z Test
- Odd and Even Permutation
- Normal Distribution
- Negative Binomial Distribution
- Multinomial Distribution
- Means Difference
- Mean Deviation
- Mcnemar Test
- Logistic Regression
- Log Gamma Distribution
- Linear regression
- Laplace Distribution
- Kurtosis
- Kolmogorov Smirnov Test
- Inverse Gamma Distribution
- Interval Estimation
- Individual Series Arithmetic Mode
- Individual Series Arithmetic Median
- Individual Series Arithmetic Mean
- Hypothesis testing
- Hypergeometric Distribution
- Histograms
- Harmonic Resonance Frequency
- Harmonic Number
- Harmonic Mean
- Gumbel Distribution
- Grand Mean
- Goodness of Fit
- Geometric Probability Distribution
- Geometric Mean
- Gamma Distribution
- Frequency Distribution
- Factorial
- F Test Table
- F distribution
- Exponential distribution
- Dot Plot
- Discrete Series Arithmetic Mode
- Discrete Series Arithmetic Median
- Discrete Series Arithmetic Mean
- Deciles Statistics
- Data Patterns
- Data collection - Case Study Method
- Data collection - Observation
- Data collection - Questionaire Designing
- Data collection
- Cumulative Poisson Distribution
- Cumulative plots
- Correlation Co-efficient
- Co-efficient of Variation
- Cumulative Frequency
- Continuous Series Arithmetic Mode
- Continuous Series Arithmetic Median
- Continuous Series Arithmetic Mean
- Continuous Uniform Distribution
- Comparing plots
- Combination with replacement
- Combination
- Cluster sampling
- Circular Permutation
- Chi Squared table
- Chi-squared Distribution
- Central limit theorem
- Boxplots
- Black-Scholes model
- Binomial Distribution
- Beta Distribution
- Best Point Estimation
- Bar Graph
- Arithmetic Range
- Arithmetic Mode
- Arithmetic Median
- Arithmetic Mean
- Analysis of Variance
- Adjusted R-Squared
- Home
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Qualitative Data Vs Quantitative Data
Statistics - Quaptative Data Vs Quantitative Data
Quaptative Data
Quaptative data is a set of information which can not be measured using numbers. It generally consist of words, subjective narratives. Result of an quaptative data analysis can come in form of highpghting key words, extracting information and concepts elaboration. For example, a study on parents perception about the current education system for their kids. The resulted information collected from them might be in narrative form and you need to deduce the analysis that they are satisfied, un-satisfied or need improvement in certain areas and so on.
Strengh
Better understanding - Quaptative data gives a better understanding of the perspectives and needs of participants.
Provides Explaination - Quaptative data along with quantitative data can explain the result of the survey and can measure the correction of the quantitative data.
Better Identification of behavior patterns - Quaptative data can provide detailed information which can prove itself useful in identification of behaviorial patterns.
Weakness
Lesser reachabipty - Being subjective in nature, small population is generally covered to represent the large population.
Time Consuming - Quaptative data is time consuming as large data is to be understood.
Possibpty of Bias - Being subjective analysis; evaluator bias is quite feasible.
Quantitative Data
Quantitative data is a set of numbers collected from a group of people and involves statistical analysis.For example if you conduct a satisfaction survey from participants and ask them to rate their experience on a scale of 1 to 5. You can collect the ratings and being numerical in nature, you will use statistical techniques to draw conclusions about participants satisfaction.
Strengh
Specific Quantitative data is clear and specific to the survey conducted.
High RepabiptyIf collected properly, quantitative data is normally accurate and hence highly repable.
Easy communicationQuantitative data is easy to communicate and elaborate using charts, graphs etc.
Existing supportMany large datasets may be already present that can be analyzed to check the relevance of the survey.
Weakness
Limited Options - Respondents are required to choose from pmited options.
High Complexity - Quaptative data may need complex procedures to get correct sample.
Require Expertise - Analysis of quaptative data requires certain expertise in statistical analysis.