Chapter no. 09.pptx sgtatistic analytsis for mangers

Chapter no. 09.pptx sgtatistic analytsis for mangers

1. Statistical Analysis for Managers

2. Chapter no 09 Time Series Analysis Time Series Analysis is a statistical technique used to analyze and interpret data points collected or recorded at regular intervals over time. The primary goal is to identify underlying patterns, trends, seasonal effects, and other characteristics in the data to make forecasts or predictions about future values. Example Stock Market Analysis: Analyzing historical prices to forecast future trends. Sales Forecasting: Predicting future sales based on past performance, identifying seasonal trends. Weather Prediction: Using historical weather data to predict future conditions.

3. Moving Average A Moving Average is a technique used to smooth out short-term fluctuations and highlight longer-term trends or cycles. It’s commonly used to identify trends in time series data by averaging a fixed number of past data points.

4. Seasonality Seasonality refers to regular patterns or fluctuations in a time series that repeat over a specific period (e.g., daily, monthly, or yearly). These patterns are often driven by external factors such as weather, holidays, or social behaviors. Example: Retail sales tend to peak during the holiday season every year, or electricity consumption may be higher in summer due to air conditioning use. In time series forecasting, it's essential to identify and separate the seasonal component to ensure accurate forecasting.

5. Measure of Trend A trend refers to the long-term movement or direction in the data, either upwards or downwards. The trend component reflects the underlying growth or decline in the data over time, excluding seasonal and random fluctuations. Measuring the tren d Graphical method: By plotting the data, you can visually inspect the direction (increasing, decreasing, or constant). Moving averages: Often used to reveal the underlying trend by smoothing out short-term fluctuations.

6. Seasonal Variations Seasonal variations are the regular changes in the data that occur within specific time periods, such as months, quarters, or seasons. This is a type of repetitive fluctuation that can be observed within a time series. Example These variations are typically between observed values and the expected seasonal values.

7. Time Series Analysis in Forecasting Time series analysis is widely used in forecasting to predict future values based on past observations. The goal is to understand the underlying patterns in the data and extrapolate them into the future. The techniques often involve calculations, like determining averages, residuals, or differences between observed and predicted values.

8. Key Components of Time Series Analysis for Forecasting Trend Component (T): The long-term progression of the series (e.g., a steady increase in sales over the years). Seasonal Component (S): Fluctuations that repeat at regular intervals (e.g., higher sales during certain months). Cyclic Component (C): Long-term cycles that are not of a fixed length but still affect the data over time (e.g., economic cycles). Irregular or Residual Component (E): Random noise or residual effects not explained by trend or seasonality.

9. Assignment Write 5 examples of each definition other than mentioned in slides.