This is a summary for business research 1 (statistics). It covers the topics frequency distribution, data-numerical measures, correlation, and regression. It refers to chapter 1,2,3,4 and 13 of the book "Business Research Applied Statistics" (custom edition for Hanze)
Statistics: science of collecting, organizing, presenting, and interpreting data to assist in
making more effective decisions
- Descriptive statistics: methods of organizing, summarizing, and presenting data
in an informative way
- Inferential statistics: methods used to estimate a property of a population on
the basis of a sample
- Population: entire set of individuals or objects of interest or the
measurements obtained from all individuals or objects of interest
- Sample: a portion, a part, of the population of interest
Types of variables:
- Qualitative: nonnumeric; usually percent or number of observations in each
category
- Quantitative: numerically
- Discrete: only certain values; gaps between values
- Continuous: any value within a specified range
Levels of measurement:
- Nominal: data may only be classified; no particular order to the categories
- Ordinal: data are ranked
- Interval: ordinal + distance between values is a constant size
- Ratio: ordinal + meaningful 0 point and ratio between values
CHAPTER 2: DESCRIBING DATA–FREQUENCY TABLES, FREQUENCY
DISTRIBUTIONS AND GRAPHIC PRESENTATIONS
Frequency table: grouping of qualitative data into mutually exclusive and collectively
exhaustive classes showing the number of observations in each class
Relative frequency table: shows fraction of the number of frequencies in each class
Frequency distribution: grouping of quantitative data into mutually exclusive and
collectively exhaustive classes showing the number of observations in each class
- Class interval or class width: is obtained by subtracting the lower boundary of a
class from the upper boundary of a class
- Class frequency (or f): number of observations in each class
- Class midpoint: point that divides a class into two equal parts; average of the
upper and lower boundaries of a class
1. Decide on number of classes: k = √N
max value−min vaue
2. Determine the class interval: 𝑖 ≥
k
Rounded up to a convenient number, i.e. multiple of 10 or 100
3. Set individual class limits
4. Tally individual observation into the classes and determine number of
observations in each class
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