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دوره Excel Data Analysis Forecasting

دسته بندی ها: آموزش های لیندا (Lynda) ، تحلیل داده (Data Analysis) ، آموزش اکسل ، آموزش آفیس

دوره-excel-data-analysis-forecasting

در این آموزش تصویری با پیش بینی و تجزیه و تحلیل داده ها در Excel آشنا می شوید.

این دوره آموزشی محصول موسسه Lynda می باشد.

سرفصل های دوره:

  • این دوره برای چه کسی مناسب است؟
  • نمایش سری های زمانی
  • داده های توالی زمانی چیست؟
  • آشنایی با سری های زمانی
  • آشنایی با نویز در یک سری زمانی
  • ایجاد نمودار متحرک برای میانگین
  • مدیریت خطاها
  • محاسبه میانگین مطلق (MAD)
  • محاسبه میانگین درصد خطا مطلق (MAPE)
  • محاسبه مجموع خطاهای مجذور (SSE)
  • محاسبه MAD، MAPE، و SSE برای بازی NFL
  • استفاده از یک روند برای پیش بینی خطا
  • مدل سازی رشد نمایی و ترکیب نرخ رشد سالانه (CAGR)
  • هنگامی که یک روند خطی با شکست مواجه می شود؟
  • محاسبه  نرخ رشد سالانه ترکیبی (CAGR)
  • فصلی بودن و نسبت به روش میانگین حرکت
  • شاخص فصلی چیست؟
  • محاسبه شاخص های فصلی
  • برآورد روند سری
  • پیش بینی ها با رگرسیون چندمتغیره
  • رگرسیون چندگانه چیست؟
  • آماده سازی داده ها برای رگرسیون چندگانه
  • اجرای رگرسیون خطی چندگانه
  • پیش بینی معادله چند رگرسیون
  • اعتبار یک معادله چند رگرسیون با استفاده از تابع TREND
  • و.......

عنوان دوره: Lynda Excel Data Analysis Forecasting سطح: متوسط مدت زمان: 3 ساعت و 7 دقیقه نویسنده: Wayne Winstonتوضیحات:

Professor Wayne Winston has taught advanced forecasting techniques to Fortune 500 companies for more than twenty years. In this course, he shows how to use Excel's data-analysis tools—including charts, formulas, and functions—to create accurate and insightful forecasts. Learn how to display time-series data visually; make sure your forecasts are accurate, by computing for errors and bias; use trendlines to identify trends and outlier data; model growth; account for seasonality; and identify unknown variables, with multiple regression analysis. A series of practice challenges along the way helps you test your skills and compare your work to Wayne's solutions.
Topics include:

Plotting and displaying time-series data Creating a moving average chart Accounting for errors and bias Using and interpreting trendlines Modeling exponential growth Calculating compound annual growth rate (CAGR) Analyzing the impact of seasonality Introducing the ratio-to-moving-average method Forecasting with multiple regression

Welcome 42s

Who is this course for? 34s

What you should know before watching this course 35s

Using the exercise files 42s

Using the challenges 30s

1. Visually Displaying Your Time-Series Data 16m 56s

What is time-series data? 1m 10s

Plotting a time series 1m 45s

Understanding level in a time series 2m 26s

Understanding trend in a time series 1m 25s

Understanding seasonality in a time series 2m 34s

Understanding noise in a time series 1m 36s

Creating a moving average chart 3m 8s

Challenge: Analyze time-series data for airline miles 27s

Solution: Analyze time-series data for airline miles 2m 25s

2. How Good Are Your Forecasts? Errors, Accuracy, and Bias 29m 0s

Exploring why some forecasts are better than others 4m 31s

Computing the mean absolute deviation (MAD) 3m 54s

Computing the mean absolute percentage error (MAPE) 5m 29s

Calculating the sum of squared errors (SSE) 2m 39s

Computing forecast bias 3m 21s

Advanced forecast bias: Determining significance 3m 49s

Challenge: Compute MAD, MAPE, and SSE for an NFL game 36s

Solution: Compute MAD, MAPE, and SSE for an NFL game 4m 41s

3. Using a Trendline for Forecasting 28m 59s

Fitting a linear trend curve 2m 55s

Interpreting the trendline 1m 51s

Interpreting the R-squared value 4m 41s

Computing standard error of the regression and outliers 6m 10s

Exploring autocorrelation 6m 56s

Challenge: Create a trendline to analyze R squared and outliers 37s

Solution: Create a trendline to analyze R squared and outliers 5m 49s

4. Modeling Exponential Growth and Compound Annual Growth Rate (CAGR) 17m 25s

When does a linear trend fail? 5m 19s

Creating an exponential trend curve 5m 31s

Computing compound annual growth rate (CAGR) 2m 45s

Challenge: Fit an exponential growth curve, estimate CAGR, and forecast revenue 41s

Solution: Fit an exponential growth curve, estimate CAGR, and forecast revenue 3m 9s

5. Seasonality and the Ratio-to-Moving-Average Method 28m 22s

What is a seasonal index? 4m 23s

Introducing the ratio-to-moving-average method 1m 47s

Computing the centered moving average 4m 7s

Calculating seasonal indices 4m 22s

Estimating a series trend 2m 4s

Forecasting sales 5m 6s

Forecasting if the series trend is changing 3m 8s

Challenge: Predicting future quarterly sales 37s

Solution: Predicting future quarterly sales 2m 48s

6. Forecasting with Multiple Regressions 1h 1m

What is multiple regression? 6m 2s

Preparing data for multiple regression 9m 7s

Running a multiple linear regression 2m 32s

Finding the multiple-regression equation and testing for significance 9m 15s

How good is the fit of the trendline? 5m 51s

Making forecasts from a multiple-regression equation 4m 17s

Validating a multiple-regression equation using the TREND function 9m 24s

Interpreting regression coefficients 4m 27s

Challenge: Regression analysis of Amazon.com revenue 1m 24s

Solution: Regression analysis of Amazon.com revenue 9m 13s

Conclusion 1m 48s

Next steps 1m 48s

حجم فایل: 650MB

Lynda Excel Data Analysis Forecasting

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