Applications of Electronic Programs in Economics
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Questions and Answers

How can you tell if something is data or information?

Look for stories you can tell

What is the importance of data analysis?

  • Informed decision-making
  • Reduce costs
  • Target customers better
  • All of the above (correct)

    • What is the first stage of the Data Analysis Process?

    Identify

    What does Diagnostic Analytics focus on?

    <p>Determining causes of events</p> Signup and view all the answers

    Correlation always implies causation in diagnostic analytics.

    <p>False</p> Signup and view all the answers

    ______ is the most advanced type of analytics that recommends the best course of action to achieve a desired outcome.

    <p>Prescriptive</p> Signup and view all the answers

    What is the purpose of inferential statistics?

    <p>To make inferences and draw conclusions about a population based on sample data</p> Signup and view all the answers

    What statistical technique is used to explore the relationship between variables like correlation coefficients?

    <p>Correlation analysis</p> Signup and view all the answers

    Descriptive statistics involves making inferences and drawing conclusions about a population based on sample data.

    <p>False</p> Signup and view all the answers

    To test whether basketball players are larger than the average male population, a __ is calculated.

    <p>t-Test</p> Signup and view all the answers

    Match the following: Descriptive Statistics vs. Inferential Statistics

    <p>Describe and summarize data = Make inferences and draw conclusions about a population based on sample data Provides measures of central tendency and dispersion = Estimates parameters, tests hypotheses, and determines the level of confidence or significance in the results Summarize, organize, and present data = Generalize findings to a larger population, make predictions, test hypotheses, evaluate relationships, and support decision-making</p> Signup and view all the answers

    What type of statistics would you use to describe study strategies and assess the number of students who experience stress?

    <p>Descriptive statistics</p> Signup and view all the answers

    What type of statistics would you use to assess whether one group experiences more stress than another and to determine the relationship between two variables?

    <p>Inferential statistics</p> Signup and view all the answers

    What is quantitative data analysis?

    <p>Quantitative data analysis involves turning spreadsheets of individual data points into meaningful insights to drive informed decisions.</p> Signup and view all the answers

    What is qualitative data analysis?

    <p>Qualitative data analysis is a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioural observations.</p> Signup and view all the answers

    What type of data is associated with measuring parameters like weight, cost, etc.?

    <p>Quantitative data</p> Signup and view all the answers

    Match the following with their descriptions:

    <p>Descriptive analysis = Methods to summarize attributes of a data set Inferential analysis = Methods to draw conclusions from statistics</p> Signup and view all the answers

    What is the purpose of descriptive analysis?

    <p>Descriptive analysis summarizes attributes of a data set to learn about a problem or opportunity.</p> Signup and view all the answers

    What does inferential statistics aim to do?

    <p>Inferential statistics aims to draw conclusions about the population from sample data.</p> Signup and view all the answers

    Study Notes

    Data Analysis Process

    • Identify: Establish the questions that need to be answered, and determine why data is needed.
    • Collect: Gather data from various sources (internal, external, surveys, interviews, etc.).
    • Clean: Ensure data is accurate and free from errors, duplicate records, and formatting issues.
    • Analyse: Use statistical techniques to extract insights and identify patterns, trends, and correlations.
    • Interpret: Draw conclusions based on the analysis, and make recommendations for action.

    Types of Data Analytics

    • Descriptive Analytics: Examines historical data to identify patterns, trends, and correlations.
      • Describes what happened.
      • Uses statistical methods to summarize data.
    • Diagnostic Analytics: Analyzes data to identify the causes of trends and correlations.
      • Examines why something happened.
      • Uses techniques like hypothesis testing, regression analysis, and correlation analysis.
    • Predictive Analytics: Uses statistical models and machine learning to forecast future events.
      • Predicts what might happen.
      • Identifies potential opportunities and risks.
    • Prescriptive Analytics: Recommends actions based on the results of predictive analytics.
      • Provides a plan of action.
      • Identifies the best course of action to achieve a desired outcome.

    Data Analysis Techniques

    • Quantitative Data Analysis: Analyzes numerical data using statistical methods.
      • Focuses on quantifiable data.
      • Uses mathematical calculations to make decisions.
    • Qualitative Data Analysis: Analyzes non-numerical data using contextual insights.
      • Focuses on understanding the why behind user behavior.
      • Uses techniques like open-ended survey responses, interview clips, and behavioral observations.

    Descriptive Statistics

    • Summarize and describe data.
    • Use statistical characteristics, charts, graphics, or tables.
    • Focus on central tendency and dispersion.
    • Includes measures like mean, median, mode, standard deviation, and variance.
    • Used to describe the properties of a sample, but does not involve generalizing beyond the data.

    Cross-Tabulation Analysis

    • Analyzes categorical data.

    • Compares results for one or more variables with the results of another.

    • Presents data in a table format.

    • Used to examine relationships within the data.

    • Helps identify patterns and trends that may not be readily apparent.### Cross-Tabulation Analysis

    • Useful when you have information that can be divided into categories or subgroups, such as age, gender, product type, or region.

    • Examples of cross-tabulation analysis:

      • Newsletter signups by age
      • Preference by gender
      • Sales by region
      • Job seniority by education level
      • Product categories by payment method

    Rule 1: Cross-Tabulation

    • Cross-tabulation doesn't work as a statistical tool for numerical data, like if you wanted to sort through a list of heights or weights.
    • You'd need to group this numerical data into categories for cross-tabulation to be effective.

    Rule 2: Sample Size

    • You must have enough data in your sample size for accurate cross-tab reports.
    • The rule of thumb is that each entry in your data table should have a value of at least 5.

    Mode, Median, and Average

    • Mode: the most common answer in a data set, which means you use it to discover the most popular response for questions with numeric answer options.
    • Median: reveals the middle of the road of your quantitative data by lining up all numeric values in ascending order and then looking at the data point in the middle.
    • Average (mean): finding the average of a dataset is an essential quantitative data analysis method.
      • First, add all your quantitative data points, like numeric survey responses or daily sales revenue.
      • Then, divide the sum of your data points by the number of responses to get a single number representing the entire dataset.

    Inferential Statistics

    • A branch of statistics that uses various analytical tools to draw conclusions about the population from sample data.
    • Inferential statistics is used to make inferences and draw conclusions; that is, to make valid generalizations from samples.
    • Examples of inferential statistics:
      • Testing hypotheses and exploring relationships between variables
      • Correlation coefficients to explore the relationship between variables (e.g., whether there is a relationship between stress levels and academic results)

    Key Differences: Descriptive vs. Inferential Statistics

    • Descriptive statistics:
      • Purpose: describe and summarize data
      • Focuses on a dataset or subset of a population
      • Examples: mean, median, mode, standard deviation, range, frequency tables
      • Goal: summarize, organize, and present data
    • Inferential statistics:
      • Purpose: make inferences and draw conclusions about a population
      • Focuses on a subset of the population (sample) to draw conclusions about the entire population
      • Examples: hypothesis testing, confidence intervals, regression analysis, ANOVA (analysis of variance), chi-square tests, t-tests, etc.
      • Goal: generalize findings to a larger population, make predictions, test hypotheses, evaluate relationships, and support decision-making

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