Your SlideShare is downloading. ×
Staisticsii
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Staisticsii

616

Published on

Fundamentals of statistics

Fundamentals of statistics

Published in: Business, News & Politics
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
616
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
20
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. What is Business Statistics?
  • 2. What Is Statistics?  Collection of Data • Survey • Interviews  Summarization and Presentation of Data • Frequency Distribution • Measures of Central Tendency and Dispersion • Charts, Tables,Graphs Decision-  Analysis of Data • Estimation Making • Hypothesis Testing  Interpretation of Data for use in more Effective Decision-Making
  • 3. Descriptive Statistics  Involves • Collecting Data • Summarizing Data • Presenting Data  Purpose: Describe Data
  • 4. Inferential Statistics  Involves Samples • Estimation • Hypothesis Testing  Purpose • Make Decisions About Population Characteristics Based on a Sample
  • 5. Key Terms  Population (Universe) • P in Population • All Items of Interest  Sample & Parameter • Portion of Population • S in Sample & Statistic  Parameter • Summary Measure about Population  Statistic • Summary Measure about Sample
  • 6. Collection of Data
  • 7. Data Types  Quantitative (categorical)  Qualitative (numerical) • Discrete • Continuous
  • 8. How Are Data Measured? 1. Nominal Scale 3. Interval Scale • Categories/Labels • Equal Intervals  e.g., Male- • No True 0 Female • Data is always numeric • Data is nonnumeric or v e • e.g., Degrees Celsius v e numeric ti ti • Arithmetic Operations ta i ta • No Arithmetic l ti • Multiples not a Operations n meaningful a u • Count u 4. Ratio Scale Q Q • Properties of Interval 2. Ordinal Scale Scale • All of the above, • True 0 plus • Meaningful Ratios • Ordering Implied • e.g., Height in Inches
  • 9. Summarization and Presentation of Data
  • 10. Data Presentation  Ordered Array  Stem and Leaf Display  Frequency Distribution • Histogram • Polygon • Ogive
  • 11. Stem-and-Leaf Display  Divide Each Observation into 2 144677 Stem Value and Leaf Value 3 028 26 • Stem Value Defines Class • Leaf Value 4 1 Defines Frequency (Count) Data: 21, 24, 24, 26, 27, 27, 30, 32, 38, 41
  • 12. Time (in seconds) that 30 Randomly Selected Customers Before Being Spent in Line of Bank Served 183 121 140 198 199 90 62 135 60 175 320 110 185 85 172 235 250 242 193 75 263 295 146 160 210 165 179 359 220 170
  • 13. 183 121 140 198 199 90 62 135 60 175 320 110 185 85 172 235 250 242 193 75 263 295 146 160 210 165 179 359 220 170 SECONDS Stem-and-Leaf Plot Frequency Stem & Leaf 5.00 0 . 66789 5.00 1 . 12344 11.00 1 . 66777788999 4.00 2 . 1234 3.00 2 . 569 1.00 3 . 2 1.00 Extremes (>=359) Stem width: 100 Each leaf: 1 case(s)
  • 14. Frequency Distribution Table Example Raw Data: 24, 26, 24, 21, 27, 27, 30, 41, 32, 38 Class Midpoint Frequency 15 but < 25 20 3 Width 25 but < 35 30 5 35 but < 45 40 2 (Upper + Lower Boundaries) / 2 Boundaries
  • 15. Rules for Constructing Frequency Distributions  Every score must fit into exactly one class (mutually exclusive)  Use 5 to 20 classes  Classes should be of the same width  Consider customary preferences in numbers  The set of classes is exhaustive
  • 16. Frequency Distribution Table Steps 1. Determine Range Highest Data Point - Lowest Data Point 2. Decide the Width (Number) of Each Class 3. Compute the Number (width) of Classes Number of classes = Range / (Width of Class) Width of classes = Range/(Number of classes) 3. Determine the lower boundary (limit) of the first class 4. Determine Class Boundaries (Limits) 5. Tally Observations & Assign to Classes
  • 17. Time (in seconds) that 30 Randomly Selected Customers Spent in Line of Bank Before Being Served 183 121 140 198 199 90 62 135 60 175 320 110 185 85 172 235 250 242 193 75 263 295 146 160 210 165 179 359 220 170
  • 18. Mean for GroupedofData Number Customers Time (in seconds) f 60 and under 120 6 120 and under 180 10 180 and under 240 8 240 and under 300 4 300 and under 360 2 30
  • 19. SECOND Valid Cumulative Frequency Percent Percent Percent Valid 60 but less than 120 6 20.0 20.0 20.0 120 but less than 180 10 33.3 33.3 53.3 180 but less than 240 8 26.7 26.7 80.0 240 but less than 300 4 13.3 13.3 93.3 300 but less than 360 2 6.7 6.7 100.0 Total 30 100.0 100.0
  • 20. 12 10 8 Frequency 6 4 2 Std. Dev = 1.17 Mean = 3 0 N = 30.00 1 2 3 4 5 90 150 210 270 330 SECOND
  • 21. ‘Chart Junk’ Bad Presentation Good Presentation Minimum Wage Minimum Wage 1960: Rs1.00 Rs 4 1970: Rs1.60 2 1980: Rs3.10 0 1990: Rs.3.80 1960 1970 1980 1990
  • 22. No Relative Basis Bad Presentation Good Presentation A’s by Class A’s by Class Freq. % 300 30% 200 20% 100 10% 0 0% FR SO JR SR FR SO JR SR
  • 23. Compressing Vertical Axis Bad Presentation Good Presentation Quarterly Sales Quarterly Sales Rs Rs 200 50 100 25 0 0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
  • 24. No Zero Point on Vertical Axis Good Presentation Bad Presentation Monthly Sales Monthly Sales Rs Rs 60 45 40 42 20 39 0 36 J M M J S N J M M J S N
  • 25. Standard Notation Measure Sample Population Mean X µ Stand. Dev. S σ 2 2 Variance S σ Size n N
  • 26. Numerical Data Properties Central Tendency (Location) Variation (Dispersion) Shape
  • 27. Measures of Central Tendency for Ungrouped Data Raw Data
  • 28. Mean  Measure of Central Tendency  Most Common Measure  Acts as ‘Balance Point’  Affected by Extreme Values (‘Outliers’)  Formula (Sample Mean) n ∑ Xi X1 + X 2 +  + X n i =1 X= = n n
  • 29. Mean Example  Raw Data: 10.3 4.9 8.911.76.3 7.7 n ∑ Xi X1 + X 2 + X 3 + X 4 + X 5 + X 6 i =1 X= = n 6 10.3 + 4.9 + 8.9 + 117 + 6.3 + 7.7 . = 6 = 8.30
  • 30. Advantages of the Mean  Most widely used  Every item taken into account  Determined algebraically and amenable to algebraic operations  Can be calculated on any set of numerical data (interval and ratio scale) -Always exists  Unique  Relatively reliable
  • 31. Disadvantages of the Mean  Affected by outliers  Cannot use in open- ended classes of a frequency distribution
  • 32. Median  Measure of Central Tendency  Middle Value In Ordered Sequence • If Odd n, Middle Value of Sequence • If Even n, Average of 2 Middle Values  Not Affected by Extreme Values  Position of Median in Sequence n +1 Positioning Point = g 2
  • 33. Median Example Odd-Sized Sample  Raw Data: 24.1, 22.6, 21.5, 23.7, 22.6  Ordered: 21.5 22.6 22.6 23.7 24.1  Position: 1 2 3 4 5 n +1 5 +1 Positioning Point = = = 3.0 2 2 Median = 22.6
  • 34. Median Example Even-Sized Sample  Raw Data: 10.3 4.9 8.9 11.7 6. 3 7.7  Ordered:4.9 6.3 7.7 8.9 10.3 11.7  Position: 1 2 3 4 5 6 n +1 6 +1 Positioning Point = = = 3.5 2 2 Median = 7.7 + 8.9 = 8.3 2
  • 35. Advantages of the Median  Unique  Unaffected by outliers and skewness  Easily understood  Can be computed for open- ended classes of a frequency distribution  Always exists on ungrouped data  Can be computed on ratio, interval and ordinal scales
  • 36. Disadvantages of Median  Requires an ordered array  No arithmetic properties
  • 37. Mode  Measure of Central Tendency  Value That Occurs Most Often  Not Affected by Extreme Values  May Be No Mode or Several Modes  May Be Used for Numerical & Categorical Data
  • 38. Advantages of Mode  Easily understood  Not affected by outliers  Useful with qualitative problems  May indicate a bimodal distribution
  • 39. Disadvantages of Mode  May not exist  Not unique  No arithmetic properties  Least accurate
  • 40. Shape Left-Skewed Symmetric Right-Skewed Mean Median Mode Mean = Median = Mode Mode Median Mean  Describes How Data Are Distributed  Measures of Shape • Skew = Symmetry
  • 41. Return on Stock Stock X Stock Y 1998 10% 17% 1997 8 -2 1996 12 16 1995 2 1 1994 8 8 40% 40% Average Return = 40 / 5 = 8% on Stock
  • 42. Measures of Dispersion for Ungrouped Data Raw Data
  • 43. Range  Measure of Dispersion  Difference Between Largest & Smallest Observations Range = X l arg est − X smallest  Ignores How Data Are Distributed 7 8 9 10 7 8 9 10
  • 44. Return on Stock Stock X Stock Y 1998 10% 17% 1997 8 -2 1996 12 16 1995 2 1 1994 8 8 Range on Stock X = 12 - 2 = 10% Range on Stock Y = 17 - (-2) = 19%
  • 45. Variance & Standard Deviation  Measures of Dispersion  Most Common Measures  Consider How Data Are Distributed  Show Variation About Mean ( X or µ )
  • 46. Sample Standard Deviation Formula n 2 ∑ (Xi − X) 2 i =1 S = S = n − 1
  • 47. Sample Standard Deviation Formula (Computational Version) s= ∑( X ) − n( X ) 2 2 n −1
  • 48. Return on Stock Stock X Stock Y 1998 10% 17% 1997 8 -2 1996 12 16 1995 2 1 1994 8 8 Range on Stock X = 12 - 2 = 10% Range on Stock Y = 17 - (-2) = 19%
  • 49. Standard Deviation of Stock X X X (X-X) ( X - X )2 1998 10 8 2 4 1997 8 8 0 0 1996 12 8 4 16 1995 2 8 -6 36 1994 8 8 0 0 56 s= ∑ (X − X ) 2 = 56 = 14 = 3.74% n− 1 4
  • 50. Return on Stock Stock X Stock Y 1998 10% 17% 1997 8 -2 1996 12 16 1995 2 1 1994 8 8 40% 40% Standard Deviation on Stock X = 3.74% Standard Deviation on Stock Y = 8.57%
  • 51. Population Mean µ= ∑ x N
  • 52. Population Standard Deviation σ= ∑ (x − µ) 2 N
  • 53. Coefficient of Variation  1. Measure of Relative Dispersion  2. Always a %  3. Shows Variation Relative to Mean  4. Used to Compare 2 or More Groups S  5. Formula (Sample) CV = ⋅100% X
  • 54. Population Coefficient of Variation σ  CV pop =  100% µ  
  • 55. Example You’re a financial analyst for Prudential- Bache Securities. You have also collected the closing stock prices of 20 old stock issues and determined the mean price is Rs.10.89 and the standard deviation was Rs.3.95. Which stock prices - old or new- were relatively more variable?
  • 56. Comparison of CV’s  Coefficient of Variation of new stocks S 3.34 34 CV = ⋅ 100% = ⋅ 100% = 215% . X 15.5  Coefficient of Variation of old stocks S 3.95 CV = ⋅ 100% = ⋅ 100% = 36.3% X 10.89

×