Essential Statistical Methods for Data Analysis
Understanding Statistical Dispersion
Dispersion is the extent to which data values in a dataset are spread out or scattered around a central value, such as the mean or median. It quantifies the variability or consistency within the data, complementing measures of central tendency (which describe the center of the data). A high dispersion indicates widely scattered data, while low dispersion suggests data points clustered closely together.
Measures of dispersion are essential for understanding data
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Descriptive Statistics
Descriptive statistics is a branch of statistics used to summarize, organize, and describe the main features of a dataset. It helps in understanding data using numerical measures like mean, median, mode, variance, and standard deviation.
1. Mean
- Mean is the average value of a dataset.
- It is calculated by dividing the sum of all observations by the total number of observations.
- It gives a general idea about the overall data value.
- Formula: Mean = (Sum of all values) / (Number of
Mastering Statistical Methods and Data Analysis
Sampling Methods
- Simple Random Sampling: Every subject has an equal probability of being selected. This provides a good representation but may be subject to non-response bias.
- Systematic Sampling: This involves applying a selection interval k from a random starting point. While every subject has an equal probability of being selected, it is simple but may not provide a good representation if there is a pattern in the way subjects are lined up.
- Stratified Sampling: The sampling frame is divided into
Sampling, Correlation, and Multivariate Methods for Research
Sampling: Population, Sample, Census
Population, sample, census: The population of interest is the entire group researchers want to generalize to. A sample is the smaller group that is actually observed or measured. A census collects data from every single member of the population. Population = who you care about. Sample = who you study. Census = everyone in the population.
Representative vs. Biased Samples
Representative vs. biased samples: A representative sample (unbiased) gives every member of
Read MoreData Types and Statistical Analysis Concepts Explained
Q1. Data Types: Categorical vs. Numerical
[8–9 Marks]
Answer:
Data comprises raw facts and figures collected for analysis and decision-making. Based on nature, data is mainly classified into Categorical data and Numerical data.
1) Categorical Data (Qualitative)
Categorical data represents qualities or categories and cannot be measured numerically.
Types:
Nominal: No natural order
Example: Gender (Male/Female), Blood GroupOrdinal: Ordered categories
Example: Grades (A, B, C), Satisfaction level
Example:
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Measures of Central Tendency
Explain measures of central tendency.
- Mean: The average value, calculated by summing all values and dividing by the number of observations.
- Median: The middle value when data is arranged in order; useful for skewed distributions.
- Mode: The most frequently occurring value in the dataset.
Regression and Regression Equations
Describe regression and types of regression equations
Regression models the relationship between a dependent variable (y) and one or more independent variables
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