Statistics

• Descriptive Statistics

  • Focuses on collection, organization, presentation, and summarization of data.
  • Key measures include central tendency (mean, median, mode), variation, skewness, and kurtosis.
  • Examples: Finding the average, percentage values, or organizing data into tables/graphs.
• Inferential Statistics

  • Involves drawing conclusions or making predictions based on data.
  • This is used to generalize about a population using data from a sample.
• Descriptive statistics summarize data, while inferential statistics make conclusions about larger groups based on samples.
• Mathematics: Techniques like averages and dispersions.
• Economics: Analyzing national income, trends, and economic policies.
• Business: Assists in decision-making, predicting customer needs, and checking product quality.
• Banking: Uses probability to manage risks and estimate customer behavior.
• Accounting: Aids in creating precise financial reports and tracking trends.
• Management: Helps in policy-making and planning through data-based decisions.
• Statistics is vital for making informed decisions in any field.
• Descriptive statistics summarize data, while inferential statistics draw conclusions about a population.
• Descriptive statistics are used to calculate measures like averages, which describe the data at hand.
• Statistical Investigation involves testing statements or theories, which are referred to as hypotheses.
• A hypothesis must be clearly stated as a problem and can be tested to determine whether it is true or false.
• The nature of the problem and resources such as time, cost, and the number of respondents guide the data collection method.
• Data Collection Method:

  • Interview Method
  • Questionnaire Method
  • Registration Method
  • Observation Method
  • Experimental Method
• Interview Method

  • A good interviewer guides respondents to provide accurate answers.
  • The interviewer has direct influence over responses.
• Questionnaire Method

  • Easy to administer but takes time to prepare.
  • It’s less expensive and does not allow the interviewer to influence answers.
• Registration Method

  • Data comes from official registration records (e.g., birth certificates, business licenses).
• Observation Method

  • Behavior or phenomena are observed and recorded.
• Experimental Method

  • Requires controlled conditions and keen observation over an extended time to gather accurate data.
• Descriptive Statistics

  • Focuses on describing a group without making conclusions about a larger population.
  • Examples include counting and summarizing data (e.g., "How many students are in a school?").
• Inferential Statistics

  • Makes predictions and inferences about a larger population based on a sample.
• A hypothesis is a statement or theory that can be tested for validity.
• The experimental method requires controlled conditions and a longer time for accurate data collection.
• Interviews involve direct interaction, while questionnaires do not.
• Descriptive statistics summarize data, such as counting the number of males and females in a school.
• The observation method focuses on recording and studying observed behavior.
• Population

  • Refers to the entire group of individuals, objects, or items under study.
  • Denoted by N.
  • Examples:
  • All students in a school.
  • People living in a country.
• Sample

  • A subset or portion of the population.
  • Denoted by n.
  • Example: Selecting 50 students from a school for a survey.
• Data

  • Refers to any quantitative (numbers) or qualitative (descriptions) information collected.
• Types of Data:

  • Quantitative Data: Numerical and measurable (e.g., height, weight).
  • Qualitative Data: Descriptive and categorical (e.g., gender, religion).
• Variables

  • Refers to specific characteristics or attributes of a population or sample.
• Types of Variables:

  • Discrete Variable: Found by counting (e.g., number of books).
  • Continuous Variable: Found by measuring (e.g., height, weight).
• Under continuous variable there are four levels of measurement.
• Four Levels of Measurement:

  • Nominal
  • Ordinal
  • Interval
  • Ratio
• Nominal

  • Data serves as labels or categories.
  • No order or ranking.
  • Examples: Gender, political party, species of flowers.
• Ordinal

  • Data with a meaningful order or ranking, but the differences between ranks are not measurable.
  • Examples: Birth order, contest rankings, social class.
• Interval

  • Data with equal intervals between values but no true zero.
  • Examples: IQ scores, temperature in Celsius.
• Ratio

  • Like interval data but includes a true zero, and values can be compared as multiples.
  • Examples: Weight, height, income, speed.