Dr. Ning's Statistics Notes
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The document outlines chapters from a statistics textbook, covering topics such as describing and exploring data through frequency tables, histograms, measures of central tendency, dispersion, correlation, and time series analysis. It also discusses deseasonalizing time series data to study trends and uses an example of predicting quarterly sales figures after removing seasonal fluctuations. The later chapters focus on time series forecasting through techniques like determining a seasonal index and forming a least squares regression line to predict future values.
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Dr. Ning's Statistics Notes
- 1. IBS Statistics Year 1 Dr. Ning DING n.ding@pl.hanze.nl I.007
- 2. Population: Chapter 1: What is Statistics? Sample: Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting Play
- 3. Chapter 1: What is Statistics? Quantitative: Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Qualitative: Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 4. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Discrete counting Continuous measuring Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 5. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Nominal: Ordinal: Chapter 3: Describing data- Numerical Measures Interval: Ordered, Equal differences Ratio: Zero Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 6. Chapter 1: What is Statistics? Frequency Table: Chapter 2: Describing data- Freaquency Table/Distribution Relative Class Frequencies: Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Frequency Distribution Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 7. Chapter 1: What is Statistics? Histogram Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Polygon Chapter 4: Describing data- Displaying & Exploring data Cumulative frequency distribution: Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 8. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data ArithmaticMean Mean WeightedMean Chapter 12: Correlational Analysis Median Central Tendency Chapter 16: Time Series & Forecasting Mode
- 9. Frequency counts Chapter 1: What is Statistics? Example: During a one hour period on a hot Saturday afternoon, Julie served fifty lemon drinks. She sold five drinks for $0.50, fifteen for $0.75, fifteen for $0.90, and fifteen for $1.10. Compute the weighted mean of the price of the drinks. Weighted Mean Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data ArithmaticMean Mean WeightedMean Chapter 12: Correlational Analysis Median Central Tendency Chapter 16: Time Series & Forecasting Mode
- 10. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data ArithmaticMean Mean WeightedMean Chapter 12: Correlational Analysis Median Central Tendency Qualitative Data Chapter 16: Time Series & Forecasting Mode Quantitative Data
- 11. ArithmaticMean Chapter 1: What is Statistics? Mean WeightedMean Grouped Data Chapter 2: Describing data- Freaquency Table/Distribution Median Central Tendency Ungrouped Data Qualitative Data Chapter 3: Describing data- Numerical Measures Value: 100 Median 150 Mode Quantitative Data 19.5 Position: 201 300.5 388 Chapter 4: Describing data- Displaying & Exploring data Draw two lines (value & position) Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 12. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Coefficient of Variation This is the ratio of the standard deviation to the mean: The coefficient of variation describes the magnitude sample values and the variation within them. Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 14. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures minimum Q1 Median Q3 maximum Range Chapter 4: Describing data- Displaying & Exploring data Box Plots Chapter 12: Correlational Analysis Q1 Q3 Chapter 16: Time Series & Forecasting Interquartile Range
- 15. DependentVariable Y X Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Independent Variable Least Square Equation: Chapter 4: Describing data- Displaying & Exploring data Intercept=27.2857 Ŷ = a + bX Chapter 12: Correlational Analysis Slope=5.75 Chapter 16: Time Series & Forecasting
- 16. Chapter 1: What is Statistics? r 2 = coefficient of determination Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures r = coefficient of correlation Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 17. r 2 = coefficient of determination Chapter 1: What is Statistics? r = coefficient of correlation Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 19. Times seasonal indexChapter 16: Time Series & Forecasting
- 20. Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Seasonal Index: Step 1: Re-organize the data Chapter 3: Describing data- Numerical Measures 2005 2006 2007 2008 2009 2010 Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 21. 6.7+4.6+10.0+12.7=34 /4=8.50 4.6+10.0+12.7+6.5=33.8 /4=8.45 Seasonal Index: Step 2: Moving Average
- 22. Seasonal Index: Step 3: Centered Moving Average
- 23. Seasonal Index: Step 4: Specific Seasonal Index
- 24. 10/8.475=1.180 12.7/8.45=1.503 6.5/8.425=0.772 Seasonal Index: Step 4: Specific Seasonal Index
- 25. 2005 2006 2007 2008 2009 2010 + + + = *(0.9978) *(0.9978) *(0.9978) *(0.9978) Seasonal Index: Step 5: TypicalQuarterly Index
- 26. Sales for the Winter are 23.5% below the typical quarter. 2005 2006 2007 2008 2009 2010 Salesfor the Fall are 51.9% above the typicalquarter. Seasonal Index: Step 6: Interpret
- 27. Chapter 16: Time Series & Forecasting 5. Deseasonalizing Data 76.5 57.5 114.1 151.9 / 0.765 = 8.759 / 0.575 = 8.004 / 1.141 = 8.761 / 1.519 = 8.361 / 0.765 / 0.575 / 1.141 / 1.519 / 0.765 / 0.575 / 1.141 = 8.498 = 9.021 / 1.519 = 8.004 = 8.700 = 8.586 = 9.112 = 8.953 = 9.283 Deseasonalizing Data:
- 28. Ŷ = a + bt Chapter 16: Time Series & Forecasting 76.5 57.5 114.1 151.9 Ŷ = 8.1096 + 0.0899 t Sale increased at a rate of 0.0899 ($ millions) per quarter. Ŷ = 8.1096 + 0.0899 * 25 = 10.3571 $ millions 10.3571*0.765 = 7.9232 $ millions Predicting Data:
- 29. Coding the time series? Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 30. Deseasonalization: Study the trend Chapter 1: What is Statistics? Chapter 2: Describing data- Freaquency Table/Distribution Chapter 3: Describing data- Numerical Measures Chapter 4: Describing data- Displaying & Exploring data Chapter 12: Correlational Analysis Chapter 16: Time Series & Forecasting
- 31. Summary of the reasons How to prepare for STA1? Absent for the lessons; Didn’t do the home assignments; Ignore the EXCEL lessons; Cannotuse the theoriesflexibly; Keep misconceptions and misunderstandingtill the exam; Overestimateself and underestimate the subject.
- 32. How to prepare for STA1? EXCEL LessonAnswer sheets MockedExam Books and Syllabus PPT files Blackboard CourseDocuments …
Notes de l'éditeur
- Correlation and CauseJust because two variables are correlated, does not mean that one of the variables is the cause of the other. It could be the case, but it does not necessarily follow: There is a strong positive correlation between the number of cigarettes that one smokes a day and one's chances of contracting lung cancer (measured as the number of cases of lung cancer per hundred people who smoke a given number of cigarettes). The percentage of heavy smokers who contract lung cancer is higher than the percentage of light smokers who develop the disease, and both figures are higher than the percentage of non-smokers who get lung cancer. In this case, the cigarettes are definitely causing the cancer. There is a strong negative correlation between the total number of skiing holidays that people book for any month of the year and the total amount of ice cream that supermarkets sell for that month. This means that the more skiing holidays that are booked, the less ice cream is sold. Is there a cause here? Are people spending so much money on ice cream that they can't afford skiing holidays? Is the fact that the ice cream is so cold putting people off skiing? Clearly not! The simple fact is that most people tend to book their skiing holidays in the winter, and they tend to buy ice cream in the summer. Although a correlation between two variables doesn't mean that one of them causes the other, it can suggest a way of finding out what the true cause might be. There may be some underlying variable that is causing both of them. For instance, if a survey found that there is a correlation between the time that people spend watching television and the amount of crime that people commit, it could be because unemployed people tend to sit around watching the television, and that unemployed people are more likely to commit crime. If that were the case, then unemployment would be the true cause!