introductory Statistics for Buuisness.pptx

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1. Introduction to Statistics This presentation will explore the fundamental concepts of statistics, its diverse applications in business and economics, and its essential role in extracting meaningful insights from data. We'll delve into different types of data, measurement scales, data sources, and the power of descriptive and inferential statistics. by Ahmed Othman

2. Applications of Statistics Accounting Statistical sampling is widely used by public accounting firms to conduct audits for their clients. This involves selecting a representative sample of transactions to examine, rather than reviewing every single one. Finance Financial analysts rely heavily on statistical information to guide their investment recommendations. They use various statistical tools to analyze market trends, assess risk, and evaluate the performance of different investment options.

3. More Applications of Statistics Marketing Electronic scanners at retail checkout counters collect vast amounts of data that are used for various marketing research applications. This data allows companies to understand consumer preferences, product demand, and market trends. Economics Economists use a wide range of statistical information to make forecasts about the future of the economy or specific economic sectors. They analyze historical data, economic indicators, and other relevant information to predict economic growth, inflation, and unemployment rates.

4. Even More Applications of Statistics Production Modern production processes rely heavily on statistical quality control to ensure consistent product quality. Statistical control charts are used to monitor the output of a production process, identify potential problems, and take corrective action. Other Applications Statistics finds wide application in various other fields, including healthcare, engineering, social sciences, and environmental research. Its ability to analyze and interpret data empowers decision-making and problem-solving in diverse disciplines.

5. Understanding Data 1 A data set is a collection of all the data gathered in a particular study. 2 Elements are the entities for which data are collected. For example, in a survey about customer satisfaction, each customer would be an element. 3 A variable is a characteristic of interest for the elements. It can be a numerical or categorical value. For example, age, gender, income level, and satisfaction rating are all variables. 4 An observation is the set of measurements obtained for a particular element. It represents the values of all variables for that element.

6. Scales of Measurement Nominal Scale Used for data that are labels or names used to identify an attribute. For example, in a survey, respondents might be asked to select their gender, with options like "Male," "Female," or "Other." Ordinal Scale Used for data where the order or rank of the data is meaningful. For example, in a customer satisfaction survey, respondents might be asked to rate their satisfaction level on a scale of "Very Satisfied," "Satisfied," "Neutral," "Dissatisfied," or "Very Dissatisfied." Continuous Scale Covers a range of values without gaps, interruptions, or jumps. For example, height, weight, and temperature are measured on a continuous scale. Discrete Scale Used for data that are countable. For example, the number of children in a family, the number of cars in a parking lot, or the number of defective units in a production run are all examples of discrete data.

7. Types of Data Qualitative Data Also known as categorical data, this type of data can be grouped by specific categories. It uses either the nominal or ordinal scale of measurement. Quantitative Data Also known as numerical data, this type of data uses numeric values to indicate how much or how many. It is obtained using either the discrete or continuous scale of measurement.

8. Data Sources 1 Existing Sources Data can often be obtained from existing sources, such as company databases, government records, and published reports. This saves time and resources compared to collecting new data. 2 Surveys Surveys are commonly used to collect data on opinions, attitudes, behaviors, and demographics. They can be conducted through various methods, such as personal interviews, telephone calls, or online questionnaires. 3 Experiments Experiments are used to study the effects of one or more variables on a variable of interest. They involve controlling the variables of interest and collecting data on the outcomes.

9. Descriptive and Inferential Statistics Descriptive Statistics Focuses on summarizing and presenting data in a way that is easy to understand. It involves collecting, organizing, summarizing, and presenting data to reveal patterns and insights. Inferential Statistics Draws conclusions about a population based on sample data. It uses statistical methods to estimate population parameters, test hypotheses, and make predictions.