Lecture_1
Chapter 1: Data and Statistics
1.1 What is Statistics?
Definition: Statistics is the art and science of collecting, analyzing, presenting, and interpreting data to help make sense of business and economic situations.
Characteristics of Data: Includes measures such as mean, mode, median, averages, minimums, and maximums.
Examples of Usage:
Advertising claims: "4 in 5 dentists recommend Colgate!"
Public opinion polling: Sources like NYT, 538.
Sports statistics: FG%, YAC, OBP, etc.
1.2 Data
Definition: Data are the facts and figures collected, analyzed, and summarized for interpretation.
Data Set: All data collected in a particular study.
1.3 Elements, Variables, and Observations
Elements: Entities on which data are collected.
Variables: Characteristics of interest for the elements.
Observations: Set of measurements for a particular element.
Data Set Structure: A data set with N elements contains N observations; total data values = elements x variables.
Example Variables:
Company: Dataram
Stock Exchange: NQ
Annual Sales: $73.10M
Earnings per share: $0.86
1.4 Scales of Measurement
Importance of measurement scales in summarizing data:
Nominal: Data are labels/names identifying attributes (e.g., nonnumeric labels or codes).
Ordinal: Data have properties of nominal and rank meaningfully (e.g., preference rankings).
Interval: Data have properties of ordinal; intervals are fixed units (e.g., SAT scores).
Ratio: Full properties of interval; includes a natural zero point (e.g., height, weight).
1.5 Categorical and Quantitative Data
Categorical Data: Grouped by categories, can be nominal or ordinal; appropriate statistical analysis is limited.
Quantitative Data: Uses numeric values to represent measurable quantities; can be interval or ratio; allows for standard arithmetic operations.
1.6 Types of Data
Cross-sectional Data: Collected at the same time.
Time Series Data: Collected over different time periods.
1.7 Data Sources
Types of Sources:
Existing Sources: Government agencies, business records, industry associations.
Observational Study: Data is collected by observing the phenomenon without influencing.
Experimental Study: Variables are controlled to observe effects (e.g., Salk polio vaccine study).
Example Sources: Census Bureau for population data, Bureau of Labor Statistics for unemployment rate.
1.8 Statistical Inference
Key Concepts:
Population: All elements of interest in a study.
Sample: Subset of the population used for analysis.
Statistical inference: Using sample data to estimate population characteristics.
Census: Collecting data from the entire population.
Sample Survey: Collecting data from a portion of the population.
1.9 Analytics
Definition: The scientific process of transforming data into insights for decision-making.
Types of Analytics:
Descriptive Analytics: Describes historical data.
Predictive Analytics: Uses models from past data to forecast future outcomes.
Prescriptive Analytics: Provides recommended actions based on data analysis.
1.10 Descriptive Statistics
Most statistical information in media (newspapers, magazines) is summarized data.
Presentations can be tabular, graphical, or numerical, aiding comprehension.
This outline presents the foundational concepts regarding data and statistics as learned in Economics 270 with instructor Samuel Wylde. It emphasizes the importance of understanding different types of data, measurement scales, inference processes, and the role of analytics in decision-making.
Lecture_1
Chapter 1: Data and Statistics
1.1 What is Statistics?
Definition: Statistics is the art and science of collecting, analyzing, presenting, and interpreting data to help make sense of business and economic situations.
Characteristics of Data: Includes measures such as mean, mode, median, averages, minimums, and maximums.
Examples of Usage:
Advertising claims: "4 in 5 dentists recommend Colgate!"
Public opinion polling: Sources like NYT, 538.
Sports statistics: FG%, YAC, OBP, etc.
1.2 Data
Definition: Data are the facts and figures collected, analyzed, and summarized for interpretation.
Data Set: All data collected in a particular study.
1.3 Elements, Variables, and Observations
Elements: Entities on which data are collected.
Variables: Characteristics of interest for the elements.
Observations: Set of measurements for a particular element.
Data Set Structure: A data set with N elements contains N observations; total data values = elements x variables.
Example Variables:
Company: Dataram
Stock Exchange: NQ
Annual Sales: $73.10M
Earnings per share: $0.86
1.4 Scales of Measurement
Importance of measurement scales in summarizing data:
Nominal: Data are labels/names identifying attributes (e.g., nonnumeric labels or codes).
Ordinal: Data have properties of nominal and rank meaningfully (e.g., preference rankings).
Interval: Data have properties of ordinal; intervals are fixed units (e.g., SAT scores).
Ratio: Full properties of interval; includes a natural zero point (e.g., height, weight).
1.5 Categorical and Quantitative Data
Categorical Data: Grouped by categories, can be nominal or ordinal; appropriate statistical analysis is limited.
Quantitative Data: Uses numeric values to represent measurable quantities; can be interval or ratio; allows for standard arithmetic operations.
1.6 Types of Data
Cross-sectional Data: Collected at the same time.
Time Series Data: Collected over different time periods.
1.7 Data Sources
Types of Sources:
Existing Sources: Government agencies, business records, industry associations.
Observational Study: Data is collected by observing the phenomenon without influencing.
Experimental Study: Variables are controlled to observe effects (e.g., Salk polio vaccine study).
Example Sources: Census Bureau for population data, Bureau of Labor Statistics for unemployment rate.
1.8 Statistical Inference
Key Concepts:
Population: All elements of interest in a study.
Sample: Subset of the population used for analysis.
Statistical inference: Using sample data to estimate population characteristics.
Census: Collecting data from the entire population.
Sample Survey: Collecting data from a portion of the population.
1.9 Analytics
Definition: The scientific process of transforming data into insights for decision-making.
Types of Analytics:
Descriptive Analytics: Describes historical data.
Predictive Analytics: Uses models from past data to forecast future outcomes.
Prescriptive Analytics: Provides recommended actions based on data analysis.
1.10 Descriptive Statistics
Most statistical information in media (newspapers, magazines) is summarized data.
Presentations can be tabular, graphical, or numerical, aiding comprehension.
This outline presents the foundational concepts regarding data and statistics as learned in Economics 270 with instructor Samuel Wylde. It emphasizes the importance of understanding different types of data, measurement scales, inference processes, and the role of analytics in decision-making.
