Statistics Tutorial
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- 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
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Pie Chart
Statistics - Pie Chart
A pie chart (or a pie graph) is a circular statistical graphical chart, which is spanided into spces in order to explain or illustrate numerical proportions. In a pie chart, centeral angle, area and an arc length of each spce is proportional to the quantity or percentages it represents. Total percentages should be 100 and total of the arc measures should be 360° Following illustration of pie graph depicts the cost of construction of a house.
From this graph, one can compare the sum spent on cement, steel and so on. One can also compute the actual sum spent on each inspanidual expense. Consider an example, where we want to know how much more is the labour cost when compared to cost of steel.
$ { Amount spent on labor = frac{90}{60} imes 600000 = $ 150000 \[7pt] Sum spent on steel = frac{54}{360} imes 600000 = $ 90000 \[7pt] Excess = 150000 - 90000 = $ 60000 \[7pt] Let 60000=x\% of 600000. \[7pt] imppes frac{x}{100} imes 600000 = $ 60000. \[7pt] imppes x = 10\% of total expense. } $ Advertisements