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GENERAL STATISTICS
 
TABLE OF CONTENTS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
[object Object]
1.1 Introduction and Basic Concepts ,[object Object],[object Object],[object Object],[object Object]
[object Object]
Statistics ,[object Object],[object Object]
Data ,[object Object]
Statistician ,[object Object]
Two Branches of Statistics ,[object Object]
Descriptive Statistics ,[object Object]
Inferential Statistics ,[object Object]
[object Object]
Population ,[object Object]
Sample ,[object Object]
1.2 Variables and Data ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
[object Object]
[object Object]
[object Object],[object Object]
Scales of Measurement of Data
Nominal Data ,[object Object]
Ordinal Data  ,[object Object]
Interval Data  ,[object Object]
Ratio Data ,[object Object]
1.3 Summation ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Chapter 2
2.1 Data Collection ,[object Object],[object Object],[object Object],[object Object]
Types of Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Secondary Data ,[object Object]
Two types of Sampling Technique: ,[object Object],[object Object]
Sampling Technique ,[object Object]
Different Types of Sampling Techniques
Simple Random Sampling ,[object Object]
Systematic Random Sampling ,[object Object]
Stratified Random Sampling  ,[object Object]
Cluster Sampling ,[object Object]
2.2 Data Presentation ,[object Object],[object Object],[object Object],[object Object]
Methods in Presenting Data ,[object Object],[object Object],[object Object],[object Object]
Stem and Leaf Diagram ,[object Object],[object Object]
[object Object]
Example: ,[object Object],[object Object],[object Object],[object Object],[object Object]
I. Setting up an array from the largest to the smallest 80 79 77 71 71 66 66 66 63 63 62 52 52 52 61 61 60 60 59 58 58 55 54 54 53 53 52 52 50 50 50 45 44 43 41 38 36 34 26 18
II.  An  array  from the smallest to the largest 18 26 34 36 38 41 43 44 45 50 50 51 52 52 53 53 54 54 55 58 58 59 60 60 61 61 62 62 62 62 53 53 66 66 66 71 71 77 79 80
III.  Setting up into stem-and-leaf diagram ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tally Method CLASS LIMIT TALLY f CLASS BOUNDARY 80-89 I 1 79.5-89.5 70-79 IIII 4 69.5-79.5 60-69 IIIII-IIIII-III 13 59.5-69.5 50-59 IIIII-IIIII-III 13 49.5-59.5 40-49 IIII 4 39.5-49.5 30-39 III 3 29.5-39.5 20-29 I 1 19.5-29.5 10-19 I 1 9.5-19.5 n=40
2.3  Graphical Representation of Frequency Distribution ,[object Object],[object Object],[object Object]
[object Object]
Graphical Representation of Frequency Distribution
Frequency Histogram ,[object Object]
Frequency Histogram
Frequency Polygon ,[object Object]
Frequency Polygon
Ogive ,[object Object]
Ogive
Pie Chart ,[object Object]
Pie Chart
Chapter 3
3.1 The Mean ,[object Object],[object Object],[object Object]
[object Object],[object Object]
MEASURES OF CENTRAL TENDENCY ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
UNGROUPED DATA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GROUPED DATA ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
3.2 Median and Mode ,[object Object],[object Object],[object Object]
[object Object]
[object Object],[object Object],[object Object]
[object Object]
ASSUMED MEAN ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
3.3 Percentiles, Deciles, and Quartiles ,[object Object],[object Object],[object Object]
Measure of Location ,[object Object]
QUARTILE (Q) ,[object Object],[object Object],[object Object]
DECILE (D) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PERCENTILE (P) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
UNGROUPED DATA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
GROUPED DATA C.I. f <cf X <cf f/n sector 80-89 1 40 84.5 1 0.0025 9percent 70-79 4 39 74.5 5 0.1000 36 60-69 13 35 64.5 18 0.3250 117 50-59 13 22 54.5 31 0.3250 117 40-49 4 9 44.5 35 0.1000 36 30-39 3 5 34.5 38 0.0750 27 20-29 1 2 24.5 39 0.0250 9 10-19 1 1 14.5 40 0.0250 9 n=40 ∑ rf=1
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MEASURE OF VARIABILITY OR DISPERSION ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
FORMULA FOR FINDING MEAN AVERAGE DEVIATION: ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
FORMULA: ,[object Object],[object Object],[object Object]
Chapter 4
MEASURE OF VARIABILITY ,[object Object],[object Object],[object Object]
FORMULA: CV SD x  100% x
  1  2  3  4  5  6  7  8  9  10 Coke  8  10  2  8  9  5  8  6  8  10 Pepsi  9  8  1  10  9  3  7  8  8  10 Example: 10- Highest 1-  Lowest
x= 7.4 (coke) x x x-x |x-x|  (x-x) 2 (x) 2 8 7.4 .6 .6 .36 64 10 7.4 2.6 2.6 6.76 100 2 7.4 5.4 5.4 29.16 4 8 7.4 .6 .6 .36 64 9 7.4 1.6 1.6 2.56 81 5 7.4 -2.4 2.4 5.76 25 8 7.4 .6 .6 .36 64 6 7.4 -1.4 1.4 1.96 36 8 7.4 .6 .6 .36 64 10 7.4 2.6 2.6 6.76 100 ∑ |x-x|=18.4 ∑ (x-x)2=54.4 ∑ (x) 2 =602
x= 7.3 (pepsi) x x x-x |x-x|  (x-x) 2 (x) 2 9 7.3 1.7 1.7 2.87 81 8 7.3 0.7 0.7 0.49 64 1 7.3 -6.3 6.3 39.69 1 10 7.3 2.7 2.7 7.29 100 9 7.3 1.7 1.7 2.87 81 3 7.3 -4.3 4.3 18.47 9 7 7.3 -0.3 0.3 0.09 49 8 7.3 0.7 0.7 0.49 64 8 7.3 0.7 0.7 0.49 64 10 7.3 2.7 2.7 7.29 100 ∑ |x-x|= ∑ (x-x) 2 =80.1
Coke CV=  204585  x  100% 7.4 CV = SD  x 100% x SD=  ∑(x-x) 2 n-1 =  54.4 9 = √6.04 SD= 2.4585 CV= 33.2229%
SD=  ∑(x-x) 2 n-1 Pepsi = 80.1 10-1 =  80.1 9 = √ 8.9 SD  =  2.9833 SD2=  8.9
DECISION: Pepsi needs more  improvement than coke in terms of  taste
A distribution of 2 different units is given to compare in dispersion of heights versus in dispersion of weights.  The mean height is 5.70 feet with SD = 0.9 ft.  The mean weight is 72.5 kg with SD = 801 kg.  Compare the dispersion in heights and in weights. CV = SD  x 100% x = 0.9  x 100% 5.7 = 0.15789 =  15.7985% CV = SD  x 100% x =  8.1  x 10% 72.5 =  0.111724 =  11.1724% HEIGHTS  WEIGHTS
MEASURE OF SKEWNESS ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],4. SK4 = P90 – 2P50 + P10 P90 – P10 5. SK5 = ∑f(x-x)3 n(SD)3 UNGROUPED DATA GROUPED DATA SK5 = ∑f(x-x)3 n(SD)3
Negatively Skewed Distribution  (all negative)  Positively Skewed Distribution  (all positive)
Normal Distribution  =  0
Measure of Kurtosis ,[object Object],[object Object],[object Object],[object Object],[object Object]
Leptokurtic Distribution Mesokurtic Distribution
[object Object]
Chapter 5
5.1 PRINCIPLE OF COUNTING ,[object Object],[object Object],[object Object],[object Object]
Principle of Counting ,[object Object]
1. In  a class of  20 the # of ways selecting president,  Vice-President, Secretary, treasurer is  20  .  19  .  18  .  17  =  116280 ,[object Object],[object Object],[object Object],[object Object],EXAMPLES:
GENERALIZATION OF PRINCIPLE OF COUNTING ,[object Object]
EXAMPLES: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
5.2 Permutations ,[object Object],[object Object],[object Object],[object Object]
PERMUTATIONS ,[object Object]
EXAMPLES: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FORMULAS: ,[object Object],[object Object]
CIRCULAR PERMUTATION ,[object Object],[object Object]
COMBINATION ,[object Object],[object Object],[object Object]
The End
Thank You!
Presenters: Mary Ann Frogosa Mary Ann Mosquerra BOA IV-1

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General Statistics boa

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  • 37. Different Types of Sampling Techniques
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  • 47. I. Setting up an array from the largest to the smallest 80 79 77 71 71 66 66 66 63 63 62 52 52 52 61 61 60 60 59 58 58 55 54 54 53 53 52 52 50 50 50 45 44 43 41 38 36 34 26 18
  • 48. II. An array from the smallest to the largest 18 26 34 36 38 41 43 44 45 50 50 51 52 52 53 53 54 54 55 58 58 59 60 60 61 61 62 62 62 62 53 53 66 66 66 71 71 77 79 80
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  • 50. Tally Method CLASS LIMIT TALLY f CLASS BOUNDARY 80-89 I 1 79.5-89.5 70-79 IIII 4 69.5-79.5 60-69 IIIII-IIIII-III 13 59.5-69.5 50-59 IIIII-IIIII-III 13 49.5-59.5 40-49 IIII 4 39.5-49.5 30-39 III 3 29.5-39.5 20-29 I 1 19.5-29.5 10-19 I 1 9.5-19.5 n=40
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  • 53. Graphical Representation of Frequency Distribution
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  • 85. GROUPED DATA C.I. f <cf X <cf f/n sector 80-89 1 40 84.5 1 0.0025 9percent 70-79 4 39 74.5 5 0.1000 36 60-69 13 35 64.5 18 0.3250 117 50-59 13 22 54.5 31 0.3250 117 40-49 4 9 44.5 35 0.1000 36 30-39 3 5 34.5 38 0.0750 27 20-29 1 2 24.5 39 0.0250 9 10-19 1 1 14.5 40 0.0250 9 n=40 ∑ rf=1
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  • 100. FORMULA: CV SD x 100% x
  • 101. 1 2 3 4 5 6 7 8 9 10 Coke 8 10 2 8 9 5 8 6 8 10 Pepsi 9 8 1 10 9 3 7 8 8 10 Example: 10- Highest 1- Lowest
  • 102. x= 7.4 (coke) x x x-x |x-x| (x-x) 2 (x) 2 8 7.4 .6 .6 .36 64 10 7.4 2.6 2.6 6.76 100 2 7.4 5.4 5.4 29.16 4 8 7.4 .6 .6 .36 64 9 7.4 1.6 1.6 2.56 81 5 7.4 -2.4 2.4 5.76 25 8 7.4 .6 .6 .36 64 6 7.4 -1.4 1.4 1.96 36 8 7.4 .6 .6 .36 64 10 7.4 2.6 2.6 6.76 100 ∑ |x-x|=18.4 ∑ (x-x)2=54.4 ∑ (x) 2 =602
  • 103. x= 7.3 (pepsi) x x x-x |x-x| (x-x) 2 (x) 2 9 7.3 1.7 1.7 2.87 81 8 7.3 0.7 0.7 0.49 64 1 7.3 -6.3 6.3 39.69 1 10 7.3 2.7 2.7 7.29 100 9 7.3 1.7 1.7 2.87 81 3 7.3 -4.3 4.3 18.47 9 7 7.3 -0.3 0.3 0.09 49 8 7.3 0.7 0.7 0.49 64 8 7.3 0.7 0.7 0.49 64 10 7.3 2.7 2.7 7.29 100 ∑ |x-x|= ∑ (x-x) 2 =80.1
  • 104. Coke CV= 204585 x 100% 7.4 CV = SD x 100% x SD= ∑(x-x) 2 n-1 = 54.4 9 = √6.04 SD= 2.4585 CV= 33.2229%
  • 105. SD= ∑(x-x) 2 n-1 Pepsi = 80.1 10-1 = 80.1 9 = √ 8.9 SD = 2.9833 SD2= 8.9
  • 106. DECISION: Pepsi needs more improvement than coke in terms of taste
  • 107. A distribution of 2 different units is given to compare in dispersion of heights versus in dispersion of weights. The mean height is 5.70 feet with SD = 0.9 ft. The mean weight is 72.5 kg with SD = 801 kg. Compare the dispersion in heights and in weights. CV = SD x 100% x = 0.9 x 100% 5.7 = 0.15789 = 15.7985% CV = SD x 100% x = 8.1 x 10% 72.5 = 0.111724 = 11.1724% HEIGHTS WEIGHTS
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  • 110. Negatively Skewed Distribution (all negative) Positively Skewed Distribution (all positive)
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  • 129. Presenters: Mary Ann Frogosa Mary Ann Mosquerra BOA IV-1