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• Data management involves organizing, storing, and handling data to ensure accuracy, availability, and security. It enables actionable insights, operational efficiency, competitiveness, and compliance with regulations.
• Statistics aids in data management by providing tools to summarize, analyze, and interpret data. It ensures data quality, supports data-driven decisions, and helps extract insights from complex datasets for better management and decision-making.
• Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting quantitative or numerical data. It transforms numbers into useful information and has two main categories: descriptive and inferential.
• Descriptive Statistics: Involves collecting, organizing, and presenting data to provide a clear overview, focusing on central tendency and variability. It is used to summarize and describe the features of a dataset.
• Descriptive Statistics is concerned with describing the characteristics and properties of a group of persons, places, or things of interest.
• Inferential Statistics: Goes beyond describing data by making predictions or inferences about a population based on a sample. It includes techniques like hypothesis testing, confidence intervals, and regression analysis to draw conclusions and make decisions with a known level of uncertainty.
• Inferential Statistics consists of methods that use sample results to help make decisions or predictions about a population.
• Advancements in technology are transforming statistics, enabling effective use of big data. This chapter explores Excel, a popular tool in statistics. Excel is a powerful tool for managing, analyzing, and visualizing data efficiently for statistical applications.
• Population – consists of all elements whose characteristics are being studied. 
• Sample – a portion of the population selected for study
• Constant - characteristics of objects that do not vary
• Variable

  • characteristic/conditions that can change 
• Data

  • values associated with a variable
• Parameter

  • descriptive of population 
• A measure of central tendency or position is a single figure which is representative of a general level of magnitudes or values of items in a set of data.
• 3 most common central tendency:

  • Mean — refers to the sum of the value of the items divided by the number of items.
  • Median — the middle value in an ordered set of data. 
  • Mode — the most frequently occurring value in a set of data.
• Percentiles are values that divide a set of data into 100 equal parts.
• Some Properties of Mean, Median, and Mode

  • Mean is unique
  • Mean is affected by extremely high and extremely low values, called the outliers.
  • Median is used when one must determine whether the values fall into the upper half or lower half of the distribution
  • Mode can be used when the data is nominal
• Median is also a measure of location.
• Quartiles – values that divide a set of observations into 4 equal parts.

  • 25% of data falls below Q1
  • 50% of data falls below Q2 - median
  • 75% of data falls below Q3
• Deciles – values that divide a set of observations into 10 equal parts.

  • 30% of the data falls below  D3
  • 80% of the data falls below  D8
  • ETC.
• Percentiles

  • values that divide a set of observations into 100 equal parts

  40% of the data falls below P40
• A measure of variability is a value that describes the spread or dispersion of a set of data points.
• The range is the difference between the highest and lowest values in a dataset.
• The variance is the average of the squared deviation from the mean.
• The standard deviation is the square root of the variance.
• Correlation analysis is a group of statistical techniques to measure the association between two variables.
• Correlation coefficient is a measure of the relative strength of a linear relationship between two numerical variables.
• simple linear regression is a fundamental tool in statistics for understanding the relationships between two variables and make predictions based on those relationships
• the dependent variable (x) is also known as the response variable is the variable being predicted and estimated
• the independent variable (y) is also known as the predictor or explanatory variable is the variable believed to have an impact on the dependent variable
• the intercept, denoted by a, is the expected mean value of the dependent variable when the independent variable is set to zero
• the slope, b, is average change in the dependent variable for every unit change in the independent variable
• the coefficient of determination (r2) is the proportion of the total variation in the dependent variable that is explained or accounted for by the variation in the independent variable
• Logic is the study or science of correct reasoning 
• A proposition (p) or a logic statement which is either true or false but not both simultaneously
• a proposition variable represented by lowercase or capital letter in the english alphabet is used to denote an arbitrary proposition with unspecified true value
• a proposition that conveys a simple idea with no connecting words and can be represented by only one propositional variable is called simple proposition
• a compound proposition is a proposition formed by combining two or more simple propositions using some connecting words
• a logical operator is a connecting word used to construct compound propositions by combining simple propositions