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Statistics for International Business School, Hanze University of Applied Science, Groningen, The Netherlands

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• 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&apos;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&apos;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&apos;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!
• Lesson07

1. 1. IBS Statistics<br />Year 1<br />Dr. Ning DING <br />n.ding@pl.hanze.nl<br />I.007<br />
2. 2. Population: <br />Chapter 1: <br />What is Statistics?<br />Sample: <br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />Play<br />
3. 3. Chapter 1: <br />What is Statistics?<br />Quantitative: <br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Qualitative:<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
4. 4. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Discrete counting<br />Continuous measuring<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
5. 5. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Nominal: <br />Ordinal: <br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Interval: <br />Ordered, Equal differences <br />Ratio: <br />Zero<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
6. 6. Chapter 1: <br />What is Statistics?<br />Frequency Table: <br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Relative Class Frequencies: <br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Frequency Distribution<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
7. 7. Chapter 1: <br />What is Statistics?<br />Histogram<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Polygon<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Cumulative frequency distribution:<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
8. 8. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />ArithmaticMean<br />Mean<br />WeightedMean<br />Chapter 12: <br />Correlational Analysis<br />Median<br />Central Tendency<br />Chapter 16: <br />Time Series & Forecasting<br />Mode<br />
9. 9. Frequency counts<br />Chapter 1: <br />What is Statistics?<br />Example: <br />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. <br />Weighted Mean<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />ArithmaticMean<br />Mean<br />WeightedMean<br />Chapter 12: <br />Correlational Analysis<br />Median<br />Central Tendency<br />Chapter 16: <br />Time Series & Forecasting<br />Mode<br />
10. 10. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />ArithmaticMean<br />Mean<br />WeightedMean<br />Chapter 12: <br />Correlational Analysis<br />Median<br />Central Tendency<br />Qualitative Data<br />Chapter 16: <br />Time Series & Forecasting<br />Mode<br />Quantitative Data<br />
11. 11. ArithmaticMean<br />Chapter 1: <br />What is Statistics?<br />Mean<br />WeightedMean<br />Grouped Data<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Median<br />Central Tendency<br />Ungrouped Data<br />Qualitative Data<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Value: 100 Median 150<br />Mode<br />Quantitative Data<br />19.5<br />Position: 201 300.5 388<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Draw two lines (value & position)<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
12. 12. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Coefficient of Variation<br />This is the ratio of the standard deviation to the mean:<br />The coefficient of variation describes the magnitude sample values and the variation within them. <br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
13. 13. Chapter 1: <br />What is Statistics?<br />Range<br />Population<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Variance<br />Measures of Dispersion<br />Sample<br />σ<br />Population<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Standard Deviation<br />Schiphol 20 40 50 60 80<br />Sample<br />SD<br />Utrecht 20 49 50 51 80<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br /><ul><li>The number of coffee sales in Utrecht Starbucks is more closely clustered around the mean of 50 than for the sales number in Schiphol </li></ul>Starbucks. <br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
14. 14. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />minimum<br />Q1<br />Median<br />Q3<br />maximum<br />Range<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Box Plots<br />Chapter 12: <br />Correlational Analysis<br />Q1<br />Q3<br />Chapter 16: <br />Time Series & Forecasting<br />Interquartile Range<br />
15. 15. DependentVariable<br />Y<br />X<br />Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Independent Variable<br />Least Square Equation:<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Intercept=27.2857<br />Ŷ = a + bX<br />Chapter 12: <br />Correlational Analysis<br />Slope=5.75<br />Chapter 16: <br />Time Series & Forecasting<br />
16. 16. Chapter 1: <br />What is Statistics?<br />r 2 = coefficient of determination<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />r = coefficient of correlation<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
17. 17. r 2 = coefficient of determination<br />Chapter 1: <br />What is Statistics?<br />r = coefficient of correlation<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
18. 18. Chapter 1: <br />What is Statistics?<br />Seasonal Index:<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Remove trend, cyclical and irregular components from Y<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Deseasonalizing Data:<br />Remove the seasonal fluctuations in order to study the trend <br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Predicting Data:<br />Chapter 12: <br />Correlational Analysis<br /><ul><li>Using deseasonalized data to formulate Least Square Equation Ŷ =a + b t
19. 19. Times seasonal index</li></ul>Chapter 16: <br />Time Series & Forecasting<br />
20. 20. Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Seasonal Index:<br />Step 1: Re-organize the data<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />2005<br />2006<br />2007<br />2008<br />2009<br />2010<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
21. 21. 6.7+4.6+10.0+12.7=34<br />/4=8.50<br />4.6+10.0+12.7+6.5=33.8<br />/4=8.45<br />Seasonal Index:<br />Step 2: Moving Average<br />
22. 22. Seasonal Index:<br />Step 3: Centered Moving Average<br />
23. 23. Seasonal Index:<br />Step 4: Specific Seasonal Index<br />
24. 24. 10/8.475=1.180<br />12.7/8.45=1.503<br />6.5/8.425=0.772<br />Seasonal Index:<br />Step 4: Specific Seasonal Index<br />
25. 25. 2005<br />2006<br />2007<br />2008<br />2009<br />2010<br />+ + + =<br />*(0.9978)<br />*(0.9978)<br />*(0.9978)<br />*(0.9978)<br />Seasonal Index:<br />Step 5: TypicalQuarterly Index<br />
26. 26. Sales for the Winter are 23.5% below the typical quarter.<br />2005<br />2006<br />2007<br />2008<br />2009<br />2010<br />Salesfor the Fall are 51.9% above the typicalquarter. <br />Seasonal Index:<br />Step 6: Interpret<br />
27. 27. Chapter 16: Time Series & Forecasting<br />5. Deseasonalizing Data<br />76.5<br />57.5<br />114.1<br />151.9<br />/ 0.765<br />= 8.759<br />/ 0.575<br />= 8.004<br />/ 1.141<br />= 8.761<br />/ 1.519<br />= 8.361<br />/ 0.765<br />/ 0.575<br />/ 1.141<br />/ 1.519<br />/ 0.765<br />/ 0.575<br />/ 1.141<br />= 8.498<br />= 9.021<br />/ 1.519<br />= 8.004<br />= 8.700<br />= 8.586<br />= 9.112<br />= 8.953<br />= 9.283<br />Deseasonalizing Data:<br />
28. 28. Ŷ = a + bt<br />Chapter 16: Time Series & Forecasting<br />76.5<br />57.5<br />114.1<br />151.9<br />Ŷ = 8.1096 + 0.0899 t<br />Sale increased at a rate of 0.0899 (\$ millions) per quarter.<br />Ŷ = 8.1096 + 0.0899 * 25<br />= 10.3571 \$ millions<br />10.3571*0.765 = 7.9232 \$ millions<br />Predicting Data:<br />
29. 29. Coding the time series?<br />Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
30. 30. Deseasonalization: Study the trend<br />Chapter 1: <br />What is Statistics?<br />Chapter 2: <br />Describing data- Freaquency Table/Distribution<br />Chapter 3: <br />Describing data-<br />Numerical Measures<br />Chapter 4: <br />Describing data-<br />Displaying & Exploring data<br />Chapter 12: <br />Correlational Analysis<br />Chapter 16: <br />Time Series & Forecasting<br />
31. 31. Summary of the reasons<br />How to prepare for STA1?<br />Absent for the lessons;<br />Didn’t do the home assignments;<br />Ignore the EXCEL <br />lessons;<br />Cannotuse the theoriesflexibly;<br />Keep misconceptions and misunderstandingtill the exam;<br />Overestimateself and underestimate the subject.<br />
32. 32. How to prepare for STA1?<br />EXCEL LessonAnswer sheets<br />MockedExam<br />Books and Syllabus<br />PPT files<br />Blackboard CourseDocuments  …<br />