As managers, we make decisions daily and these decisions are not based on assumptions but based on facts

Statistics is the branch of mathematics that deals with collecting, organizing/ presenting, analyzing, and interpreting numerical facts(data) to make informed decisions

As managers, the quality of decisions is a function of the information available

**ROLE OF STATISTICS**

Today, statistics play an ever-increasing important role for business managers. These decision-makers use statistics to:

- Present/describe business data and information properly
- Draw conclusions using information collected from samples
- Make reliable forecasts about a business activity
- Monitoring and maintaining the quality of products and services

**STATISTICAL CONCEPTS**

- Variable: A characteristic that varies from one person or Business to another Examples of variables for humans are height, weight, number of siblings/genders, marital status. For businesses Sales, Profit, Assets
- Variables can be classified as either

Qualitative (non-numerical/categorical) or Quantitative (numerical)

- Quantitative variables can be classified as either

Discrete (0,1,2,3…) or Continuous (1.3m, 5.34kg)

- Outlier: Observation that falls far from the rest of the data
- The categories into which a qualitative variable falls may or may not have a natural ordering.
- Nominal scale: refers to the fact that the categories are merely names.
- Ordinal scale: If the categories can be put in order, the scale is called an ordinal scale.
- Interval scale: If one can compare the differences between measurements of the variable meaningfully, then the quantitative variable is defined on an interval scale.
- Ratio scale: (zero means nothing)

**DESCRIPTIVE STATISTICS**

Data in their raw form do not make any sense until we try to describe/summarize the data.

Descriptive statistics is the branch of statistics that deals with summarizing and organizing/presenting data without drawing a conclusion

Descriptive Statistics (mean, median, mode, standard deviation, variance, and so on…)

- Arithmetic Mean (Average) is the most common
- When the variable is quantitative with symmetric distribution, then we use the mean
- In the case of quantitative variables with skewed distribution, the median is a good choice for the measure of center. This is related to the fact that the mean can be highly inﬂuenced by an observation that falls far from the rest of the data, called an outlier.
- The median and the mean apply only to quantitative data, whereas the mode can be used with either quantitative or qualitative data
- The mode should be used when calculating a measure of center for the qualitative variable.
- Variance and Standard Deviation shows the average distance from the Mean

**INFERENTIAL STATISTICS**

Inferential statistics is the branch of statistics that deals with drawing conclusions about a population based on information obtained from the samples

Inferential statistics is made up of two parts:

Estimation & Confidence Interval Construction

**ESTIMATION**

- Estimation refers to the process by which one makes inferences about a population, based on information obtained from a sample.
- An estimate of a population parameter may be expressed in two ways:
- Point estimate: A point estimate of a population parameter is a single value of a statistic. x = μ.
- Interval estimate: An interval estimate is defined by two numbers, between which a population parameter is said to lie.
- a < x < b is an interval estimate of the population mean μ
- Confidence Interval Construction

As managers, the quality of decisions is a function of the information available

**KEEP CALM AND LOVE STATISTICS**